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	# coding=utf-8
	# Copyright 2023 The IndicTrans2 Authors and AI4Bharat team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" PyTorch IndicTrans model."""


	import math
	from typing import List, Optional, Tuple, Union

	import torch
	import torch.nn as nn
	from torch.nn import functional as F

	from transformers.activations import ACT2FN

	from transformers.modeling_attn_mask_utils import (
	_prepare_4d_attention_mask,
	_prepare_4d_attention_mask_for_sdpa,
	_prepare_4d_causal_attention_mask,
	_prepare_4d_causal_attention_mask_for_sdpa,
	)

	from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
	from transformers.modeling_outputs import (
	BaseModelOutput,
	BaseModelOutputWithPastAndCrossAttentions,
	Seq2SeqLMOutput,
	Seq2SeqModelOutput
	)

	from transformers.utils import (
	logging,
	is_flash_attn_2_available,
	is_flash_attn_greater_or_equal_2_10,
	)

	from transformers.modeling_utils import PreTrainedModel

	from .configuration_indictrans import IndicTransConfig


	logger = logging.get_logger(__name__)

	INDICTRANS_PRETRAINED_MODEL_ARCHIVE_LIST = [""]

	try:
	if is_flash_attn_2_available():
	from flash_attn import flash_attn_func, flash_attn_varlen_func
	from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
	except:
	pass


	# Copied from transformers.models.llama.modeling_llama._get_unpad_data
	def _get_unpad_data(attention_mask):
	seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
	indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
	max_seqlen_in_batch = seqlens_in_batch.max().item()
	cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
	return (
	indices,
	cu_seqlens,
	max_seqlen_in_batch,
	)


	# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
	def shift_tokens_right(
	input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int
	):
	"""
	Shift input ids one token to the right.
	"""
	shifted_input_ids = input_ids.new_zeros(input_ids.shape)
	shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
	shifted_input_ids[:, 0] = decoder_start_token_id

	if pad_token_id is None:
	raise ValueError("self.model.config.pad_token_id has to be defined.")
	# replace possible -100 values in labels by `pad_token_id`
	shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

	return shifted_input_ids


	def create_position_ids_from_input_ids(
	input_ids, padding_idx, past_key_values_length=0
	):
	"""
	Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
	are ignored. This is modified from fairseq's `utils.make_positions`.
	"""
	# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
	mask = input_ids.ne(padding_idx).int()
	incremental_indices = (
	torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length
	) * mask
	return incremental_indices.long() + padding_idx


	# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding->IndicTrans
	class IndicTransSinusoidalPositionalEmbedding(nn.Module):
	"""This module produces sinusoidal positional embeddings of any length."""

	def __init__(
	self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None
	):
	super().__init__()
	self.offset = 2
	self.embedding_dim = embedding_dim
	self.padding_idx = padding_idx
	self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)

	def make_weights(
	self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None
	):
	emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
	if hasattr(self, "weights"):
	# in forward put the weights on the correct dtype and device of the param
	emb_weights = emb_weights.to(
	dtype=self.weights.dtype, device=self.weights.device
	)

	self.register_buffer("weights", emb_weights, persistent=False)

	@staticmethod
	def get_embedding(
	num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None
	):
	"""
	Build sinusoidal embeddings.

	This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
	"Attention Is All You Need".
	"""
	half_dim = embedding_dim // 2
	emb = math.log(10000) / (half_dim - 1)
	emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
	emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(
	1
	) * emb.unsqueeze(0)
	emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(
	num_embeddings, -1
	)
	if embedding_dim % 2 == 1:
	# zero pad
	emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
	if padding_idx is not None:
	emb[padding_idx, :] = 0

	return emb.to(torch.get_default_dtype())

	@torch.no_grad()
	def forward(
	self,
	input_ids: torch.Tensor = None,
	inputs_embeds: torch.Tensor = None,
	past_key_values_length: int = 0,
	):
	if input_ids is not None:
	bsz, seq_len = input_ids.size()
	# Create the position ids from the input token ids. Any padded tokens remain padded.
	position_ids = create_position_ids_from_input_ids(
	input_ids, self.padding_idx, past_key_values_length
	).to(input_ids.device)
	else:
	bsz, seq_len = inputs_embeds.size()[:-1]
	position_ids = self.create_position_ids_from_inputs_embeds(
	inputs_embeds, past_key_values_length
	)

	# expand embeddings if needed
	max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
	if max_pos > self.weights.size(0):
	self.make_weights(
	max_pos + self.offset, self.embedding_dim, self.padding_idx
	)

	return (
	self.weights.index_select(0, position_ids.view(-1))
	.view(bsz, seq_len, self.weights.shape[-1])
	.detach()
	)

	def create_position_ids_from_inputs_embeds(
	self, inputs_embeds, past_key_values_length
	):
	"""
	We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.

	Args:
	inputs_embeds: torch.Tensor

	Returns: torch.Tensor
	"""
	input_shape = inputs_embeds.size()[:-1]
	sequence_length = input_shape[1]

	position_ids = torch.arange(
	self.padding_idx + 1,
	sequence_length + self.padding_idx + 1,
	dtype=torch.long,
	device=inputs_embeds.device,
	)
	return (
	position_ids.unsqueeze(0).expand(input_shape).contiguous()
	+ past_key_values_length
	)


	# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->IndicTrans
	class IndicTransAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	def __init__(
	self,
	embed_dim: int,
	num_heads: int,
	dropout: float = 0.0,
	is_decoder: bool = False,
	bias: bool = True,
	is_causal: bool = False,
	config: Optional[IndicTransConfig] = None,
	):
	super().__init__()
	self.embed_dim = embed_dim
	self.num_heads = num_heads
	self.dropout = dropout
	self.head_dim = embed_dim // num_heads
	self.config = config

	if (self.head_dim * num_heads) != self.embed_dim:
	raise ValueError(
	f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
	f" and `num_heads`: {num_heads})."
	)
	self.scaling = self.head_dim**-0.5
	self.is_decoder = is_decoder
	self.is_causal = is_causal

	self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
	self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
	self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
	self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

	def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
	return (
	tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
	.transpose(1, 2)
	.contiguous()
	)

	def forward(
	self,
	hidden_states: torch.Tensor,
	key_value_states: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	layer_head_mask: Optional[torch.Tensor] = None,
	output_attentions: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""Input shape: Batch x Time x Channel"""

	# if key_value_states are provided this layer is used as a cross-attention layer
	# for the decoder
	is_cross_attention = key_value_states is not None

	bsz, tgt_len, _ = hidden_states.size()

	# get query proj
	query_states = self.q_proj(hidden_states) * self.scaling
	# get key, value proj
	# `past_key_value[0].shape[2] == key_value_states.shape[1]`
	# is checking that the `sequence_length` of the `past_key_value` is the same as
	# the provided `key_value_states` to support prefix tuning
	if (
	is_cross_attention
	and past_key_value is not None
	and past_key_value[0].shape[2] == key_value_states.shape[1]
	):
	# reuse k,v, cross_attentions
	key_states = past_key_value[0]
	value_states = past_key_value[1]
	elif is_cross_attention:
	# cross_attentions
	key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
	value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
	elif past_key_value is not None:
	# reuse k, v, self_attention
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
	key_states = torch.cat([past_key_value[0], key_states], dim=2)
	value_states = torch.cat([past_key_value[1], value_states], dim=2)
	else:
	# self_attention
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

	if self.is_decoder:
	# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
	# Further calls to cross_attention layer can then reuse all cross-attention
	# key/value_states (first "if" case)
	# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
	# all previous decoder key/value_states. Further calls to uni-directional self-attention
	# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
	# if encoder bi-directional self-attention `past_key_value` is always `None`
	past_key_value = (key_states, value_states)

	proj_shape = (bsz * self.num_heads, -1, self.head_dim)
	query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
	key_states = key_states.reshape(*proj_shape)
	value_states = value_states.reshape(*proj_shape)

	src_len = key_states.size(1)
	attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

	if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
	raise ValueError(
	f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
	f" {attn_weights.size()}"
	)

	if attention_mask is not None:
	if attention_mask.size() != (bsz, 1, tgt_len, src_len):
	raise ValueError(
	f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
	)
	attn_weights = (
	attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
	+ attention_mask
	)
	attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

	attn_weights = F.softmax(attn_weights, dim=-1)

	if layer_head_mask is not None:
	if layer_head_mask.size() != (self.num_heads,):
	raise ValueError(
	f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
	f" {layer_head_mask.size()}"
	)
	attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(
	bsz, self.num_heads, tgt_len, src_len
	)
	attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

	if output_attentions:
	# this operation is a bit awkward, but it's required to
	# make sure that attn_weights keeps its gradient.
	# In order to do so, attn_weights have to be reshaped
	# twice and have to be reused in the following
	attn_weights_reshaped = attn_weights.view(
	bsz, self.num_heads, tgt_len, src_len
	)
	attn_weights = attn_weights_reshaped.view(
	bsz * self.num_heads, tgt_len, src_len
	)
	else:
	attn_weights_reshaped = None

	attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training)

	attn_output = torch.bmm(attn_probs, value_states)

	if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
	raise ValueError(
	f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
	f" {attn_output.size()}"
	)

	attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
	attn_output = attn_output.transpose(1, 2)

	# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
	# partitioned across GPUs when using tensor-parallelism.
	attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

	attn_output = self.out_proj(attn_output)

	return attn_output, attn_weights_reshaped, past_key_value


	class IndicTransFlashAttention2(IndicTransAttention):
	"""
	IndicTrans flash attention module. This module inherits from `IndicTransAttention` as the weights of the module stays
	untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
	flash attention and deal with padding tokens in case the input contains any of them.
	"""

	# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
	def __init__(self, args, *kwargs):
	super().__init__(args, *kwargs)

	# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
	# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
	# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
	self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

	def _reshape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
	return tensor.view(bsz, seq_len, self.num_heads, self.head_dim)

	def forward(
	self,
	hidden_states: torch.Tensor,
	key_value_states: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	layer_head_mask: Optional[torch.Tensor] = None,
	output_attentions: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	# IndicTransFlashAttention2 attention does not support output_attentions
	if output_attentions:
	raise ValueError("IndicTransFlashAttention2 attention does not support output_attentions")

	# if key_value_states are provided this layer is used as a cross-attention layer
	# for the decoder
	is_cross_attention = key_value_states is not None

	bsz, q_len, _ = hidden_states.size()

	# get query proj
	query_states = self._reshape(self.q_proj(hidden_states), -1, bsz)
	# get key, value proj
	# `past_key_value[0].shape[2] == key_value_states.shape[1]`
	# is checking that the `sequence_length` of the `past_key_value` is the same as
	# the provided `key_value_states` to support prefix tuning
	if (
	is_cross_attention
	and past_key_value is not None
	and past_key_value[0].shape[2] == key_value_states.shape[1]
	):
	# reuse k,v, cross_attentions
	key_states = past_key_value[0].transpose(1, 2)
	value_states = past_key_value[1].transpose(1, 2)
	elif is_cross_attention:
	# cross_attentions
	key_states = self._reshape(self.k_proj(key_value_states), -1, bsz)
	value_states = self._reshape(self.v_proj(key_value_states), -1, bsz)
	elif past_key_value is not None:
	# reuse k, v, self_attention
	key_states = self._reshape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._reshape(self.v_proj(hidden_states), -1, bsz)
	key_states = torch.cat([past_key_value[0].transpose(1, 2), key_states], dim=1)
	value_states = torch.cat([past_key_value[1].transpose(1, 2), value_states], dim=1)
	else:
	# self_attention
	key_states = self._reshape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._reshape(self.v_proj(hidden_states), -1, bsz)

	if self.is_decoder:
	# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
	# Further calls to cross_attention layer can then reuse all cross-attention
	# key/value_states (first "if" case)
	# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
	# all previous decoder key/value_states. Further calls to uni-directional self-attention
	# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
	# if encoder bi-directional self-attention `past_key_value` is always `None`
	past_key_value = (key_states.transpose(1, 2), value_states.transpose(1, 2))

	kv_seq_len = key_states.shape[-2]
	if past_key_value is not None:
	kv_seq_len += past_key_value[0].shape[-2]

	# In PEFT, usually we cast the layer norms in float32 for training stability reasons
	# therefore the input hidden states gets silently casted in float32. Hence, we need
	# cast them back in the correct dtype just to be sure everything works as expected.
	# This might slowdown training & inference so it is recommended to not cast the LayerNorms
	# in fp32. (LlamaRMSNorm handles it correctly)

	input_dtype = query_states.dtype
	if input_dtype == torch.float32:
	if torch.is_autocast_enabled():
	target_dtype = torch.get_autocast_gpu_dtype()
	# Handle the case where the model is quantized
	elif hasattr(self.config, "_pre_quantization_dtype"):
	target_dtype = self.config._pre_quantization_dtype
	else:
	target_dtype = self.q_proj.weight.dtype

	logger.warning_once(
	f"The input hidden states seems to be silently casted in float32, this might be related to"
	f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
	f" {target_dtype}."
	)

	query_states = query_states.to(target_dtype)
	key_states = key_states.to(target_dtype)
	value_states = value_states.to(target_dtype)

	attn_output = self._flash_attention_forward(
	query_states, key_states, value_states, attention_mask, q_len, dropout=self.dropout
	)

	attn_output = attn_output.reshape(bsz, q_len, -1)
	attn_output = self.out_proj(attn_output)

	if not output_attentions:
	attn_weights = None

	return attn_output, attn_weights, past_key_value

	# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
	def _flash_attention_forward(
	self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
	):
	"""
	Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
	first unpad the input, then computes the attention scores and pad the final attention scores.

	Args:
	query_states (`torch.Tensor`):
	Input query states to be passed to Flash Attention API
	key_states (`torch.Tensor`):
	Input key states to be passed to Flash Attention API
	value_states (`torch.Tensor`):
	Input value states to be passed to Flash Attention API
	attention_mask (`torch.Tensor`):
	The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
	position of padding tokens and 1 for the position of non-padding tokens.
	dropout (`float`):
	Attention dropout
	softmax_scale (`float`, optional):
	The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
	"""
	if not self._flash_attn_uses_top_left_mask:
	causal = self.is_causal
	else:
	# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
	causal = self.is_causal and query_length != 1

	# Contains at least one padding token in the sequence
	if attention_mask is not None:
	batch_size = query_states.shape[0]
	query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
	query_states, key_states, value_states, attention_mask, query_length
	)

	cu_seqlens_q, cu_seqlens_k = cu_seq_lens
	max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

	attn_output_unpad = flash_attn_varlen_func(
	query_states,
	key_states,
	value_states,
	cu_seqlens_q=cu_seqlens_q,
	cu_seqlens_k=cu_seqlens_k,
	max_seqlen_q=max_seqlen_in_batch_q,
	max_seqlen_k=max_seqlen_in_batch_k,
	dropout_p=dropout,
	softmax_scale=softmax_scale,
	causal=causal,
	)

	attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
	else:
	attn_output = flash_attn_func(
	query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
	)

	return attn_output

	# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
	def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
	indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
	batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

	key_layer = index_first_axis(
	key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
	)
	value_layer = index_first_axis(
	value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
	)
	if query_length == kv_seq_len:
	query_layer = index_first_axis(
	query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
	)
	cu_seqlens_q = cu_seqlens_k
	max_seqlen_in_batch_q = max_seqlen_in_batch_k
	indices_q = indices_k
	elif query_length == 1:
	max_seqlen_in_batch_q = 1
	cu_seqlens_q = torch.arange(
	batch_size + 1, dtype=torch.int32, device=query_layer.device
	) # There is a memcpy here, that is very bad.
	indices_q = cu_seqlens_q[:-1]
	query_layer = query_layer.squeeze(1)
	else:
	# The -q_len: slice assumes left padding.
	attention_mask = attention_mask[:, -query_length:]
	query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

	return (
	query_layer,
	key_layer,
	value_layer,
	indices_q,
	(cu_seqlens_q, cu_seqlens_k),
	(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
	)


	class IndicTransSdpaAttention(IndicTransAttention):
	def forward(
	self,
	hidden_states: torch.Tensor,
	key_value_states: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	attention_mask: Optional[torch.Tensor] = None,
	layer_head_mask: Optional[torch.Tensor] = None,
	output_attentions: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""Input shape: Batch x Time x Channel"""
	if output_attentions or layer_head_mask is not None:
	# TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once this is implemented.
	logger.warning_once(
	"IndicTransModel is using IndicTransSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention"
	' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
	)
	return super().forward(
	hidden_states,
	key_value_states=key_value_states,
	past_key_value=past_key_value,
	attention_mask=attention_mask,
	layer_head_mask=layer_head_mask,
	output_attentions=output_attentions,
	)

	# if key_value_states are provided this layer is used as a cross-attention layer
	# for the decoder
	is_cross_attention = key_value_states is not None

	bsz, tgt_len, _ = hidden_states.size()

	# get query proj
	query_states = self.q_proj(hidden_states)
	# get key, value proj
	# `past_key_value[0].shape[2] == key_value_states.shape[1]`
	# is checking that the `sequence_length` of the `past_key_value` is the same as
	# the provided `key_value_states` to support prefix tuning
	if (
	is_cross_attention
	and past_key_value is not None
	and past_key_value[0].shape[2] == key_value_states.shape[1]
	):
	# reuse k,v, cross_attentions
	key_states = past_key_value[0]
	value_states = past_key_value[1]
	elif is_cross_attention:
	# cross_attentions
	key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
	value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
	elif past_key_value is not None:
	# reuse k, v, self_attention
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
	key_states = torch.cat([past_key_value[0], key_states], dim=2)
	value_states = torch.cat([past_key_value[1], value_states], dim=2)
	else:
	# self_attention
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

	if self.is_decoder:
	# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
	# Further calls to cross_attention layer can then reuse all cross-attention
	# key/value_states (first "if" case)
	# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
	# all previous decoder key/value_states. Further calls to uni-directional self-attention
	# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
	# if encoder bi-directional self-attention `past_key_value` is always `None`
	past_key_value = (key_states, value_states)

	query_states = self._shape(query_states, tgt_len, bsz)

	# NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask,
	# but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
	attn_output = F.scaled_dot_product_attention(
	query_states,
	key_states,
	value_states,
	attn_mask=attention_mask,
	dropout_p=self.dropout if self.training else 0.0,
	# The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case tgt_len == 1.
	is_causal=self.is_causal and attention_mask is None and tgt_len > 1,
	)

	if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim):
	raise ValueError(
	f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
	f" {attn_output.size()}"
	)

	attn_output = attn_output.transpose(1, 2)

	# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
	# partitioned across GPUs when using tensor-parallelism.
	attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

	attn_output = self.out_proj(attn_output)

	return attn_output, None, past_key_value


	INDICTRANS_ATTENTION_CLASSES = {
	"eager": IndicTransAttention,
	"sdpa": IndicTransSdpaAttention,
	"flash_attention_2": IndicTransFlashAttention2,
	}

	# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->IndicTrans
	class IndicTransEncoderLayer(nn.Module):
	def __init__(self, config: IndicTransConfig):
	super().__init__()
	self.embed_dim = config.encoder_embed_dim
	self.self_attn = INDICTRANS_ATTENTION_CLASSES[config._attn_implementation](
	embed_dim=self.embed_dim,
	num_heads=config.encoder_attention_heads,
	dropout=config.attention_dropout,
	config=config,
	)
	self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
	self.dropout = config.dropout
	self.activation_fn = ACT2FN[config.activation_function]
	self.activation_dropout = config.activation_dropout
	self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
	self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
	self.final_layer_norm = nn.LayerNorm(self.embed_dim)
	self.normalize_before = config.encoder_normalize_before

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: torch.Tensor,
	layer_head_mask: torch.Tensor,
	output_attentions: bool = False,
	) -> torch.Tensor:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
	attention_mask (`torch.FloatTensor`): attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
	`(encoder_attention_heads,)`.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	"""
	residual = hidden_states
	if self.normalize_before:
	hidden_states = self.self_attn_layer_norm(hidden_states)
	hidden_states, attn_weights, _ = self.self_attn(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	layer_head_mask=layer_head_mask,
	output_attentions=output_attentions,
	)
	hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states
	if not self.normalize_before:
	hidden_states = self.self_attn_layer_norm(hidden_states)

	residual = hidden_states
	if self.normalize_before:
	hidden_states = self.final_layer_norm(hidden_states)
	hidden_states = self.activation_fn(self.fc1(hidden_states))
	hidden_states = F.dropout(
	hidden_states, p=self.activation_dropout, training=self.training
	)
	hidden_states = self.fc2(hidden_states)
	hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states
	if not self.normalize_before:
	hidden_states = self.final_layer_norm(hidden_states)

	if hidden_states.dtype == torch.float16 and (
	torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
	):
	clamp_value = torch.finfo(hidden_states.dtype).max - 1000
	hidden_states = torch.clamp(
	hidden_states, min=-clamp_value, max=clamp_value
	)

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (attn_weights,)

	return outputs


	# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer with MBart->IndicTrans
	class IndicTransDecoderLayer(nn.Module):
	def __init__(self, config: IndicTransConfig):
	super().__init__()
	self.embed_dim = config.decoder_embed_dim

	self.self_attn = INDICTRANS_ATTENTION_CLASSES[config._attn_implementation](
	embed_dim=self.embed_dim,
	num_heads=config.decoder_attention_heads,
	dropout=config.attention_dropout,
	is_decoder=True,
	is_causal=True,
	config=config,
	)
	self.dropout = config.dropout
	self.activation_fn = ACT2FN[config.activation_function]
	self.activation_dropout = config.activation_dropout

	self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
	self.encoder_attn = INDICTRANS_ATTENTION_CLASSES[config._attn_implementation](
	self.embed_dim,
	config.decoder_attention_heads,
	dropout=config.attention_dropout,
	is_decoder=True,
	config=config,
	)
	self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
	self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
	self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
	self.final_layer_norm = nn.LayerNorm(self.embed_dim)
	self.normalize_before = config.decoder_normalize_before

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	layer_head_mask: Optional[torch.Tensor] = None,
	cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = True,
	) -> torch.Tensor:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
	attention_mask (`torch.FloatTensor`): attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	encoder_hidden_states (`torch.FloatTensor`):
	cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
	encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
	`(encoder_attention_heads,)`.
	cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
	size `(decoder_attention_heads,)`.
	past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	"""
	residual = hidden_states
	if self.normalize_before:
	hidden_states = self.self_attn_layer_norm(hidden_states)

	# Self Attention
	# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
	self_attn_past_key_value = (
	past_key_value[:2] if past_key_value is not None else None
	)
	# add present self-attn cache to positions 1,2 of present_key_value tuple
	hidden_states, self_attn_weights, present_key_value = self.self_attn(
	hidden_states=hidden_states,
	past_key_value=self_attn_past_key_value,
	attention_mask=attention_mask,
	layer_head_mask=layer_head_mask,
	output_attentions=output_attentions,
	)
	hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states
	if not self.normalize_before:
	hidden_states = self.self_attn_layer_norm(hidden_states)

	# Cross-Attention Block
	cross_attn_present_key_value = None
	cross_attn_weights = None
	if encoder_hidden_states is not None:
	residual = hidden_states
	if self.normalize_before:
	hidden_states = self.encoder_attn_layer_norm(hidden_states)

	# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
	cross_attn_past_key_value = (
	past_key_value[-2:] if past_key_value is not None else None
	)
	(
	hidden_states,
	cross_attn_weights,
	cross_attn_present_key_value,
	) = self.encoder_attn(
	hidden_states=hidden_states,
	key_value_states=encoder_hidden_states,
	attention_mask=encoder_attention_mask,
	layer_head_mask=cross_attn_layer_head_mask,
	past_key_value=cross_attn_past_key_value,
	output_attentions=output_attentions,
	)
	hidden_states = F.dropout(
	hidden_states, p=self.dropout, training=self.training
	)
	hidden_states = residual + hidden_states
	if not self.normalize_before:
	hidden_states = self.encoder_attn_layer_norm(hidden_states)

	# add cross-attn to positions 3,4 of present_key_value tuple
	present_key_value = present_key_value + cross_attn_present_key_value

	# Fully Connected
	residual = hidden_states
	if self.normalize_before:
	hidden_states = self.final_layer_norm(hidden_states)
	hidden_states = self.activation_fn(self.fc1(hidden_states))
	hidden_states = F.dropout(
	hidden_states, p=self.activation_dropout, training=self.training
	)
	hidden_states = self.fc2(hidden_states)
	hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
	hidden_states = residual + hidden_states
	if not self.normalize_before:
	hidden_states = self.final_layer_norm(hidden_states)

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights, cross_attn_weights)

	if use_cache:
	outputs += (present_key_value,)

	return outputs


	# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100PretrainedModel->IndicTrans
	class IndicTransPreTrainedModel(PreTrainedModel):
	config_class = IndicTransConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = True
	_no_split_modules = ["IndicTransAttention"]

	def _init_weights(self, module):
	std = self.config.init_std
	if isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, (IndicTransDecoder, IndicTransEncoder)):
	module.gradient_checkpointing = value


	# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100EncoderLayer->IndicTrans
	class IndicTransEncoder(IndicTransPreTrainedModel):
	"""
	Transformer encoder consisting of config.encoder_layers self attention layers. Each layer is a
	[`IndicTransEncoderLayer`].

	Args:
	config: IndicTransConfig
	embed_tokens (nn.Embedding): output embedding
	"""

	def __init__(
	self, config: IndicTransConfig, embed_tokens: Optional[nn.Embedding] = None
	):
	super().__init__(config)

	self.dropout = config.dropout
	self.layerdrop = config.encoder_layerdrop

	embed_dim = config.encoder_embed_dim
	self.padding_idx = config.pad_token_id
	self.max_source_positions = config.max_source_positions
	self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0

	self.embed_tokens = nn.Embedding(
	config.encoder_vocab_size, embed_dim, self.padding_idx
	)

	if embed_tokens is not None:
	self.embed_tokens.weight = embed_tokens.weight

	self.embed_positions = IndicTransSinusoidalPositionalEmbedding(
	config.max_source_positions,
	embed_dim,
	self.padding_idx,
	)
	self.layers = nn.ModuleList(
	[IndicTransEncoderLayer(config) for _ in range(config.encoder_layers)]
	)
	self.layer_norm = (
	nn.LayerNorm(embed_dim) if config.encoder_normalize_before else None
	)
	self.layernorm_embedding = (
	nn.LayerNorm(embed_dim) if config.layernorm_embedding else None
	)

	self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
	self._use_sdpa = config._attn_implementation == "sdpa"

	self.gradient_checkpointing = False
	# Initialize weights and apply final processing
	self.post_init()

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	):
	r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
	provide it.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
	This is useful if you want more control over how to convert `input_ids` indices into associated vectors
	than the model's internal embedding lookup matrix.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
	for more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""
	output_attentions = (
	output_attentions
	if output_attentions is not None
	else self.config.output_attentions
	)
	output_hidden_states = (
	output_hidden_states
	if output_hidden_states is not None
	else self.config.output_hidden_states
	)
	return_dict = (
	return_dict if return_dict is not None else self.config.use_return_dict
	)

	# retrieve input_ids and inputs_embeds
	if input_ids is not None and inputs_embeds is not None:
	raise ValueError(
	"You cannot specify both input_ids and inputs_embeds at the same time"
	)
	elif input_ids is not None:
	self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

	embed_pos = self.embed_positions(input_ids, inputs_embeds)
	embed_pos = embed_pos.to(inputs_embeds.device)

	hidden_states = inputs_embeds + embed_pos
	if self.layernorm_embedding is not None:
	hidden_states = self.layernorm_embedding(hidden_states)
	hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)

	if attention_mask is not None:
	if self._use_flash_attention_2:
	attention_mask = attention_mask if 0 in attention_mask else None
	elif self._use_sdpa and head_mask is None and not output_attentions:
	# output_attentions=True & head_mask can not be supported when using SDPA, fall back to
	# the manual implementation that requires a 4D causal mask in all cases.
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, inputs_embeds.dtype)
	else:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)


	encoder_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None

	# check if head_mask has a correct number of layers specified if desired
	if head_mask is not None:
	if head_mask.size()[0] != len(self.layers):
	raise ValueError(
	f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
	f" {head_mask.size()[0]}."
	)
	deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()

	for idx, encoder_layer in enumerate(self.layers):
	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)

	# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
	dropout_probability = torch.rand([])

	skip_the_layer = (
	True
	if self.training and (dropout_probability < self.layerdrop)
	else False
	)
	if not skip_the_layer or deepspeed_zero3_is_enabled:
	# under deepspeed zero3 all gpus must run in sync

	if self.gradient_checkpointing and self.training:
	# create gradient checkpointing function
	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(encoder_layer),
	hidden_states,
	attention_mask,
	(head_mask[idx] if head_mask is not None else None),
	)
	else:
	layer_outputs = encoder_layer(
	hidden_states,
	attention_mask,
	layer_head_mask=(
	head_mask[idx] if head_mask is not None else None
	),
	output_attentions=output_attentions,
	)

	hidden_states = layer_outputs[0]

	if skip_the_layer:
	layer_outputs = (None, None)

	if output_attentions:
	all_attentions = all_attentions + (layer_outputs[1],)

	if self.layer_norm is not None:
	hidden_states = self.layer_norm(hidden_states)

	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)

	if not return_dict:
	return tuple(
	v
	for v in [hidden_states, encoder_states, all_attentions]
	if v is not None
	)
	return BaseModelOutput(
	last_hidden_state=hidden_states,
	hidden_states=encoder_states,
	attentions=all_attentions,
	)


	# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100DecoderLayer->IndicTrans
	class IndicTransDecoder(IndicTransPreTrainedModel):
	"""
	Transformer decoder consisting of config.decoder_layers layers. Each layer is a [`IndicTransDecoderLayer`]

	Args:
	config: IndicTransConfig
	embed_tokens (nn.Embedding): output embedding
	"""

	def __init__(
	self, config: IndicTransConfig, embed_tokens: Optional[nn.Embedding] = None
	):
	super().__init__(config)
	self.dropout = config.dropout
	self.layerdrop = config.decoder_layerdrop

	embed_dim = config.encoder_embed_dim
	self.padding_idx = config.pad_token_id
	self.max_target_positions = config.max_target_positions
	self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0

	self.embed_tokens = nn.Embedding(
	config.decoder_vocab_size, embed_dim, self.padding_idx
	)

	if embed_tokens is not None:
	self.embed_tokens.weight = embed_tokens.weight

	self.embed_positions = IndicTransSinusoidalPositionalEmbedding(
	config.max_target_positions,
	embed_dim,
	self.padding_idx,
	)
	self.layers = nn.ModuleList(
	[IndicTransDecoderLayer(config) for _ in range(config.decoder_layers)]
	)
	self.layer_norm = (
	nn.LayerNorm(embed_dim) if config.decoder_normalize_before else None
	)
	self.layernorm_embedding = (
	nn.LayerNorm(embed_dim) if config.layernorm_embedding else None
	)

	self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
	self._use_sdpa = config._attn_implementation == "sdpa"

	self.gradient_checkpointing = False
	# Initialize weights and apply final processing
	self.post_init()

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	cross_attn_head_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	):
	r"""
	Args:
	input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
	provide it.

	Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
	[`PreTrainedTokenizer.__call__`] for details.

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, optional):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
	of the decoder.
	encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, optional):
	Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
	selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional):
	Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, optional):
	Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
	cross-attention on hidden heads. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`):
	Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
	shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
	shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

	Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
	cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

	If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
	that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
	all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of
	shape `(batch_size, sequence_length, hidden_size)`, optional): Optionally, instead of passing
	`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
	control over how to convert `input_ids` indices into associated vectors than the model's internal
	embedding lookup matrix.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
	for more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""
	output_attentions = (
	output_attentions
	if output_attentions is not None
	else self.config.output_attentions
	)
	output_hidden_states = (
	output_hidden_states
	if output_hidden_states is not None
	else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	return_dict = (
	return_dict if return_dict is not None else self.config.use_return_dict
	)

	# retrieve input_ids and inputs_embeds
	if input_ids is not None and inputs_embeds is not None:
	raise ValueError(
	"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
	)
	elif input_ids is not None:
	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	raise ValueError(
	"You have to specify either decoder_input_ids or decoder_inputs_embeds"
	)

	# past_key_values_length
	past_key_values_length = (
	past_key_values[0][0].shape[2] if past_key_values is not None else 0
	)

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale


	if self._use_flash_attention_2:
	# 2d mask is passed through the layers
	attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
	elif self._use_sdpa and not output_attentions and cross_attn_head_mask is None:
	# output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
	# the manual implementation that requires a 4D causal mask in all cases.
	attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
	attention_mask,
	input_shape,
	inputs_embeds,
	past_key_values_length,
	)
	else:
	# 4d mask is passed through the layers
	attention_mask = _prepare_4d_causal_attention_mask(
	attention_mask, input_shape, inputs_embeds, past_key_values_length
	)

	# expand encoder attention mask
	if encoder_hidden_states is not None and encoder_attention_mask is not None:
	if self._use_flash_attention_2:
	encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
	elif self._use_sdpa and cross_attn_head_mask is None and not output_attentions:
	# output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
	# the manual implementation that requires a 4D causal mask in all cases.
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
	encoder_attention_mask,
	inputs_embeds.dtype,
	tgt_len=input_shape[-1],
	)
	else:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	encoder_attention_mask = _prepare_4d_attention_mask(
	encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
	)

	# embed positions
	positions = self.embed_positions(
	input_ids, inputs_embeds, past_key_values_length
	)
	positions = positions.to(inputs_embeds.device)

	hidden_states = inputs_embeds + positions
	if self.layernorm_embedding is not None:
	hidden_states = self.layernorm_embedding(hidden_states)

	hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)

	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warning_once(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting"
	" `use_cache=False`..."
	)
	use_cache = False

	# decoder layers
	all_hidden_states = () if output_hidden_states else None
	all_self_attns = () if output_attentions else None
	all_cross_attentions = () if output_attentions else None
	next_decoder_cache = () if use_cache else None

	# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
	for attn_mask, mask_name in zip(
	[head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]
	):
	if attn_mask is not None:
	if attn_mask.size()[0] != len(self.layers):
	raise ValueError(
	f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
	f" {head_mask.size()[0]}."
	)
	deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()

	for idx, decoder_layer in enumerate(self.layers):
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
	dropout_probability = torch.rand([])

	skip_the_layer = (
	True
	if self.training and (dropout_probability < self.layerdrop)
	else False
	)
	if not skip_the_layer or deepspeed_zero3_is_enabled:
	# under deepspeed zero3 all gpus must run in sync

	past_key_value = (
	past_key_values[idx] if past_key_values is not None else None
	)

	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	# None for past_key_value
	return module(*inputs, output_attentions, use_cache)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(decoder_layer),
	hidden_states,
	attention_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	head_mask[idx] if head_mask is not None else None,
	cross_attn_head_mask[idx]
	if cross_attn_head_mask is not None
	else None,
	None,
	)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=attention_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	layer_head_mask=(
	head_mask[idx] if head_mask is not None else None
	),
	cross_attn_layer_head_mask=(
	cross_attn_head_mask[idx]
	if cross_attn_head_mask is not None
	else None
	),
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)

	hidden_states = layer_outputs[0]

	if skip_the_layer:
	continue

	if use_cache:
	next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)

	if output_attentions:
	all_self_attns += (layer_outputs[1],)
	all_cross_attentions += (layer_outputs[2],)

	if self.layer_norm is not None:
	hidden_states = self.layer_norm(hidden_states)

	# add hidden states from the last decoder layer
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	next_cache = next_decoder_cache if use_cache else None
	if not return_dict:
	return tuple(
	v
	for v in [
	hidden_states,
	next_cache,
	all_hidden_states,
	all_self_attns,
	all_cross_attentions,
	]
	if v is not None
	)
	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=next_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attns,
	cross_attentions=all_cross_attentions,
	)


	# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100Model->IndicTrans
	class IndicTransModel(IndicTransPreTrainedModel):
	_tied_weights_keys = None

	def __init__(self, config: IndicTransConfig):
	super().__init__(config)

	self.encoder = IndicTransEncoder(config)
	self.decoder = IndicTransDecoder(config)

	# Initialize weights and apply final processing
	self.post_init()

	def get_encoder(self):
	return self.encoder

	def get_decoder(self):
	return self.decoder

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	decoder_input_ids: Optional[torch.LongTensor] = None,
	decoder_attention_mask: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	decoder_head_mask: Optional[torch.Tensor] = None,
	cross_attn_head_mask: Optional[torch.Tensor] = None,
	encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]:
	output_attentions = (
	output_attentions
	if output_attentions is not None
	else self.config.output_attentions
	)
	output_hidden_states = (
	output_hidden_states
	if output_hidden_states is not None
	else self.config.output_hidden_states
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	return_dict = (
	return_dict if return_dict is not None else self.config.use_return_dict
	)

	if encoder_outputs is None:
	encoder_outputs = self.encoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
	elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
	encoder_outputs = BaseModelOutput(
	last_hidden_state=encoder_outputs[0],
	hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
	attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
	)

	# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
	decoder_outputs = self.decoder(
	input_ids=decoder_input_ids,
	attention_mask=decoder_attention_mask,
	encoder_hidden_states=encoder_outputs[0],
	encoder_attention_mask=attention_mask,
	head_mask=decoder_head_mask,
	cross_attn_head_mask=cross_attn_head_mask,
	past_key_values=past_key_values,
	inputs_embeds=decoder_inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	if not return_dict:
	return decoder_outputs + encoder_outputs

	return Seq2SeqModelOutput(
	last_hidden_state=decoder_outputs.last_hidden_state,
	past_key_values=decoder_outputs.past_key_values,
	decoder_hidden_states=decoder_outputs.hidden_states,
	decoder_attentions=decoder_outputs.attentions,
	cross_attentions=decoder_outputs.cross_attentions,
	encoder_last_hidden_state=encoder_outputs.last_hidden_state,
	encoder_hidden_states=encoder_outputs.hidden_states,
	encoder_attentions=encoder_outputs.attentions,
	)


	# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100ForConditionalGeneration->IndicTrans
	class IndicTransForConditionalGeneration(IndicTransPreTrainedModel):
	base_model_prefix = "model"
	_tied_weights_keys = None
	_label_smoothing = 0.0

	def __init__(self, config: IndicTransConfig):
	super().__init__(config)
	self.model = IndicTransModel(config)
	self.lm_head = nn.Linear(
	config.decoder_embed_dim, config.decoder_vocab_size, bias=False
	)

	if config.share_decoder_input_output_embed:
	self.lm_head.weight = self.model.decoder.embed_tokens.weight

	self.post_init()

	def tie_weights(self):
	pass

	def get_encoder(self):
	return self.model.get_encoder()

	def get_decoder(self):
	return self.model.get_decoder()

	def get_output_embeddings(self):
	return self.lm_head

	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

	def set_label_smoothing(self, label_smoothing):
	self._label_smoothing = label_smoothing

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	decoder_input_ids: Optional[torch.LongTensor] = None,
	decoder_attention_mask: Optional[torch.LongTensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	decoder_head_mask: Optional[torch.Tensor] = None,
	cross_attn_head_mask: Optional[torch.Tensor] = None,
	encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
	labels: Optional[torch.LongTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]:
	r"""
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
	config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
	(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

	Returns:
	"""
	return_dict = (
	return_dict if return_dict is not None else self.config.use_return_dict
	)

	if labels is not None:
	if decoder_input_ids is None:
	decoder_input_ids = shift_tokens_right(
	labels, self.config.pad_token_id, self.config.decoder_start_token_id
	)

	outputs = self.model(
	input_ids,
	attention_mask=attention_mask,
	decoder_input_ids=decoder_input_ids,
	encoder_outputs=encoder_outputs,
	decoder_attention_mask=decoder_attention_mask,
	head_mask=head_mask,
	decoder_head_mask=decoder_head_mask,
	cross_attn_head_mask=cross_attn_head_mask,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	decoder_inputs_embeds=decoder_inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	lm_logits = self.lm_head(outputs[0])

	masked_lm_loss = None
	if labels is not None:
	# move labels to the correct device to enable PP
	labels = labels.to(lm_logits.device)
	masked_lm_loss = F.cross_entropy(
	input=lm_logits.view(-1, self.config.decoder_vocab_size),
	target=labels.view(-1),
	ignore_index=-100,
	label_smoothing=self._label_smoothing,
	)

	if not return_dict:
	output = (lm_logits,) + outputs[1:]
	return (
	((masked_lm_loss,) + output) if masked_lm_loss is not None else output
	)

	return Seq2SeqLMOutput(
	loss=masked_lm_loss,
	logits=lm_logits,
	past_key_values=outputs.past_key_values,
	decoder_hidden_states=outputs.decoder_hidden_states,
	decoder_attentions=outputs.decoder_attentions,
	cross_attentions=outputs.cross_attentions,
	encoder_last_hidden_state=outputs.encoder_last_hidden_state,
	encoder_hidden_states=outputs.encoder_hidden_states,
	encoder_attentions=outputs.encoder_attentions,
	)

	def prepare_inputs_for_generation(
	self,
	decoder_input_ids,
	past_key_values=None,
	attention_mask=None,
	head_mask=None,
	decoder_head_mask=None,
	cross_attn_head_mask=None,
	use_cache=None,
	encoder_outputs=None,
	**kwargs,
	):
	# cut decoder_input_ids if past is used
	if past_key_values is not None:
	decoder_input_ids = decoder_input_ids[:, -1:]

	return {
	"input_ids": None, # encoder_outputs is defined. input_ids not needed
	"encoder_outputs": encoder_outputs,
	"past_key_values": past_key_values,
	"decoder_input_ids": decoder_input_ids,
	"attention_mask": attention_mask,
	"head_mask": head_mask,
	"decoder_head_mask": decoder_head_mask,
	"cross_attn_head_mask": cross_attn_head_mask,
	"use_cache": use_cache, # change this to avoid caching (presumably for debugging)
	}

	@staticmethod
	def _reorder_cache(past_key_values, beam_idx):
	reordered_past = ()
	for layer_past in past_key_values:
	reordered_past += (
	tuple(
	past_state.index_select(0, beam_idx) for past_state in layer_past
	),
	)
	return reordered_past