nllb-clip-base / modeling_nllb_clip.py

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	""" PyTorch NLLB CLIP model."""


	import math
	from dataclasses import dataclass
	from typing import Any, Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from configuration_nllb_clip import NLLBCLIPConfig, NLLBCLIPTextConfig
	from torch import nn
	from transformers import CLIPVisionConfig
	from transformers.activations import ACT2FN
	from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
	from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
	from transformers.modeling_utils import PreTrainedModel
	from transformers.utils import ModelOutput, logging

	logger = logging.get_logger(__name__)


	# contrastive loss function, adapted from
	# https://sachinruk.github.io/blog/2021-03-07-clip.html
	def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
	return nn.functional.cross_entropy(
	logits, torch.arange(len(logits), device=logits.device)
	)


	def clip_loss(similarity: torch.Tensor) -> torch.Tensor:
	caption_loss = contrastive_loss(similarity)
	image_loss = contrastive_loss(similarity.t())
	return (caption_loss + image_loss) / 2.0


	class CLIPVisionEmbeddings(nn.Module):
	def __init__(self, config: CLIPVisionConfig):
	super().__init__()
	self.config = config
	self.embed_dim = config.hidden_size
	self.image_size = config.image_size
	self.patch_size = config.patch_size

	self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))

	self.patch_embedding = nn.Conv2d(
	in_channels=config.num_channels,
	out_channels=self.embed_dim,
	kernel_size=self.patch_size,
	stride=self.patch_size,
	bias=False,
	)

	self.num_patches = (self.image_size // self.patch_size) ** 2
	self.num_positions = self.num_patches + 1
	self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
	self.register_buffer(
	"position_ids",
	torch.arange(self.num_positions).expand((1, -1)),
	persistent=False,
	)

	def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
	batch_size = pixel_values.shape[0]
	target_dtype = self.patch_embedding.weight.dtype
	patch_embeds = self.patch_embedding(
	pixel_values.to(dtype=target_dtype)
	) # shape = [*, width, grid, grid]
	patch_embeds = patch_embeds.flatten(2).transpose(1, 2)

	class_embeds = self.class_embedding.expand(batch_size, 1, -1)
	embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
	embeddings = embeddings + self.position_embedding(self.position_ids)
	return embeddings


	class CLIPAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	def __init__(self, config):
	super().__init__()
	self.config = config
	self.embed_dim = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.embed_dim // self.num_heads
	if self.head_dim * self.num_heads != self.embed_dim:
	raise ValueError(
	f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
	f" {self.num_heads})."
	)
	self.scale = self.head_dim**-0.5
	self.dropout = config.attention_dropout

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

	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,
	attention_mask: Optional[torch.Tensor] = None,
	causal_attention_mask: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""Input shape: Batch x Time x Channel"""

	bsz, tgt_len, embed_dim = hidden_states.size()

	# get query proj
	query_states = self.q_proj(hidden_states) * self.scale
	key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
	value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

	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.view(*proj_shape)
	value_states = value_states.view(*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()}"
	)

	# apply the causal_attention_mask first
	if causal_attention_mask is not None:
	if causal_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"
	f" {causal_attention_mask.size()}"
	)
	attn_weights = (
	attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
	+ causal_attention_mask
	)
	attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

	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 = nn.functional.softmax(attn_weights, dim=-1)

	if output_attentions:
	# this operation is a bit akward, but it's required to
	# make sure that attn_weights keeps its gradient.
	# In order to do so, attn_weights have to 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 = nn.functional.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)
	attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)

	attn_output = self.out_proj(attn_output)

	return attn_output, attn_weights_reshaped


	class CLIPMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.activation_fn = ACT2FN[config.hidden_act]
	self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
	self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.fc1(hidden_states)
	hidden_states = self.activation_fn(hidden_states)
	hidden_states = self.fc2(hidden_states)
	return hidden_states


	class CLIPEncoderLayer(nn.Module):
	def __init__(self, config: NLLBCLIPConfig):
	super().__init__()
	self.embed_dim = config.hidden_size
	self.self_attn = CLIPAttention(config)
	self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
	self.mlp = CLIPMLP(config)
	self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: torch.Tensor,
	causal_attention_mask: torch.Tensor,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.FloatTensor]:
	"""
	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.
	`(config.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

	hidden_states = self.layer_norm1(hidden_states)
	hidden_states, attn_weights = self.self_attn(
	hidden_states=hidden_states,
	attention_mask=attention_mask,
	causal_attention_mask=causal_attention_mask,
	output_attentions=output_attentions,
	)
	hidden_states = residual + hidden_states

	residual = hidden_states
	hidden_states = self.layer_norm2(hidden_states)
	hidden_states = self.mlp(hidden_states)
	hidden_states = residual + hidden_states

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (attn_weights,)

	return outputs


	class CLIPEncoder(nn.Module):
	"""
	Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
	[`CLIPEncoderLayer`].

	Args:
	config: CLIPConfig
	"""

	def __init__(self, config: NLLBCLIPConfig):
	super().__init__()
	self.config = config
	self.layers = nn.ModuleList(
	[CLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)]
	)
	self.gradient_checkpointing = False

	def forward(
	self,
	inputs_embeds,
	attention_mask: Optional[torch.Tensor] = None,
	causal_attention_mask: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutput]:
	r"""
	Args:
	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	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.
	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)
	causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Causal mask for the text model. 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)
	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
	)

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

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

	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,
	causal_attention_mask,
	)
	else:
	layer_outputs = encoder_layer(
	hidden_states,
	attention_mask,
	causal_attention_mask,
	output_attentions=output_attentions,
	)

	hidden_states = layer_outputs[0]

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

	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,
	)


	class CLIPVisionTransformer(nn.Module):
	def __init__(self, config: CLIPVisionConfig):
	super().__init__()
	self.config = config
	embed_dim = config.hidden_size

	self.embeddings = CLIPVisionEmbeddings(config)
	self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
	self.encoder = CLIPEncoder(config)
	self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)

	def forward(
	self,
	pixel_values: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPooling]:
	r"""
	Returns:

	"""
	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
	)

	if pixel_values is None:
	raise ValueError("You have to specify pixel_values")

	hidden_states = self.embeddings(pixel_values)
	hidden_states = self.pre_layrnorm(hidden_states)

	encoder_outputs = self.encoder(
	inputs_embeds=hidden_states,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	last_hidden_state = encoder_outputs[0]
	pooled_output = last_hidden_state[:, 0, :]
	pooled_output = self.post_layernorm(pooled_output)

	if not return_dict:
	return (last_hidden_state, pooled_output) + encoder_outputs[1:]

	return BaseModelOutputWithPooling(
	last_hidden_state=last_hidden_state,
	pooler_output=pooled_output,
	hidden_states=encoder_outputs.hidden_states,
	attentions=encoder_outputs.attentions,
	)


	@dataclass
	class NLLBCLIPOutput(ModelOutput):
	"""
	Args:
	loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `return_loss` is `True`):
	Contrastive loss for image-text similarity.
	logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
	The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
	similarity scores.
	logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
	The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
	similarity scores.
	text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
	The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPTextModel`].
	image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
	The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPVisionModel`].
	text_model_output(`BaseModelOutputWithPooling`):
	The output of the [`CLIPTextModel`].
	vision_model_output(`BaseModelOutputWithPooling`):
	The output of the [`CLIPVisionModel`].
	"""

	loss: Optional[torch.FloatTensor] = None
	logits_per_image: torch.FloatTensor = None
	logits_per_text: torch.FloatTensor = None
	text_embeds: torch.FloatTensor = None
	image_embeds: torch.FloatTensor = None
	text_model_output: BaseModelOutputWithPooling = None
	vision_model_output: BaseModelOutputWithPooling = None

	def to_tuple(self) -> Tuple[Any]:
	return tuple(
	self[k]
	if k not in ["text_model_output", "vision_model_output"]
	else getattr(self, k).to_tuple()
	for k in self.keys()
	)


	class M2M100Attention(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,
	):
	super().__init__()
	self.embed_dim = embed_dim
	self.num_heads = num_heads
	self.dropout = dropout
	self.head_dim = embed_dim // num_heads

	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.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 = nn.functional.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 = nn.functional.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

	# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->M2M100


	class M2M100EncoderLayer(nn.Module):
	def __init__(self, config: NLLBCLIPConfig):
	super().__init__()
	self.embed_dim = config.d_model
	self.self_attn = M2M100Attention(
	embed_dim=self.embed_dim,
	num_heads=config.encoder_attention_heads,
	dropout=config.attention_dropout,
	)
	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)

	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
	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 = nn.functional.dropout(
	hidden_states, p=self.dropout, training=self.training
	)
	hidden_states = residual + hidden_states

	residual = hidden_states
	hidden_states = self.final_layer_norm(hidden_states)
	hidden_states = self.activation_fn(self.fc1(hidden_states))
	hidden_states = nn.functional.dropout(
	hidden_states, p=self.activation_dropout, training=self.training
	)
	hidden_states = self.fc2(hidden_states)
	hidden_states = nn.functional.dropout(
	hidden_states, p=self.dropout, training=self.training
	)
	hidden_states = residual + 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


	def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
	"""
	Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
	"""
	bsz, src_len = mask.size()
	tgt_len = tgt_len if tgt_len is not None else src_len

	expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

	inverted_mask = 1.0 - expanded_mask

	return inverted_mask.masked_fill(
	inverted_mask.to(torch.bool), torch.finfo(dtype).min
	)


	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


	class M2M100SinusoidalPositionalEmbedding(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
	)


	class M2M100Encoder(PreTrainedModel):
	"""
	Transformer encoder consisting of config.encoder_layers self attention layers. Each layer is a
	[`M2M100EncoderLayer`].

	Args:
	config: M2M100Config
	embed_tokens (nn.Embedding): output embedding
	"""

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

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

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

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

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

	self.embed_positions = M2M100SinusoidalPositionalEmbedding(
	config.max_position_embeddings,
	embed_dim,
	self.padding_idx,
	)
	self.layers = nn.ModuleList(
	[M2M100EncoderLayer(config) for _ in range(config.encoder_layers)]
	)
	self.layer_norm = nn.LayerNorm(config.d_model)

	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
	hidden_states = nn.functional.dropout(
	hidden_states, p=self.dropout, training=self.training
	)

	# expand attention_mask
	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	attention_mask = _expand_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],)

	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,
	)


	class CLIPTextTransformer(nn.Module):
	def __init__(self, config: NLLBCLIPTextConfig):
	super().__init__()
	self.config = config
	embed_dim = config.hidden_size
	self.encoder = M2M100Encoder(config)
	self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)

	# For `pooled_output` computation
	self.eos_token_id = config.eos_token_id

	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPooling]:
	r"""
	Returns:

	"""
	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
	)

	if input_ids is None:
	raise ValueError("You have to specify input_ids")

	input_shape = input_ids.size()
	input_ids = input_ids.view(-1, input_shape[-1])

	encoder_outputs = self.encoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	last_hidden_state = encoder_outputs[0]
	last_hidden_state = self.final_layer_norm(last_hidden_state)

	pooled_output = last_hidden_state[
	torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
	0,
	]

	if not return_dict:
	return (last_hidden_state, pooled_output) + encoder_outputs[1:]

	return BaseModelOutputWithPooling(
	last_hidden_state=last_hidden_state,
	pooler_output=pooled_output,
	hidden_states=encoder_outputs.hidden_states,
	attentions=encoder_outputs.attentions,
	)


	class NLLBCLIPModel(PreTrainedModel):
	config_class = NLLBCLIPConfig

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

	if not isinstance(config.text_config, NLLBCLIPTextConfig):
	raise ValueError(
	"config.text_config is expected to be of type CLIPTextConfig but is of type"
	f" {type(config.text_config)}."
	)

	if not isinstance(config.vision_config, CLIPVisionConfig):
	raise ValueError(
	"config.vision_config is expected to be of type CLIPVisionConfig but is of type"
	f" {type(config.vision_config)}."
	)

	text_config = config.text_config
	vision_config = config.vision_config

	self.projection_dim = config.projection_dim
	self.text_embed_dim = text_config.hidden_size
	self.vision_embed_dim = vision_config.hidden_size

	self.text_model = CLIPTextTransformer(text_config)
	self.vision_model = CLIPVisionTransformer(vision_config)

	self.visual_projection = nn.Linear(
	self.vision_embed_dim, self.projection_dim, bias=False
	)
	self.text_projection = nn.Linear(
	self.text_embed_dim, self.projection_dim, bias=False
	)
	self.logit_scale = nn.Parameter(
	torch.tensor(self.config.logit_scale_init_value)
	)

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

	def get_text_features(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> torch.FloatTensor:
	r"""
	Returns:
	text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
	applying the projection layer to the pooled output of [`CLIPTextModel`].

	Examples:

	```python
	>>> from transformers import AutoTokenizer, CLIPModel

	>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
	>>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")

	>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
	>>> text_features = model.get_text_features(**inputs)
	```"""
	# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
	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
	)

	text_outputs = self.text_model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	pooled_output = text_outputs[1]
	text_features = self.text_projection(pooled_output)

	return text_features

	def get_image_features(
	self,
	pixel_values: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> torch.FloatTensor:
	r"""
	Returns:
	image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
	applying the projection layer to the pooled output of [`CLIPVisionModel`].

	Examples:

	```python
	>>> from PIL import Image
	>>> import requests
	>>> from transformers import AutoProcessor, CLIPModel

	>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
	>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> image = Image.open(requests.get(url, stream=True).raw)

	>>> inputs = processor(images=image, return_tensors="pt")

	>>> image_features = model.get_image_features(**inputs)
	```"""
	# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
	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
	)

	vision_outputs = self.vision_model(
	pixel_values=pixel_values,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	pooled_output = vision_outputs[1] # pooled_output
	image_features = self.visual_projection(pooled_output)

	return image_features

	def forward(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	pixel_values: Optional[torch.FloatTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	return_loss: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, NLLBCLIPOutput]:
	r"""
	Returns:

	Examples:

	```python
	>>> from PIL import Image
	>>> import requests
	>>> from transformers import AutoProcessor, CLIPModel

	>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
	>>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> image = Image.open(requests.get(url, stream=True).raw)

	>>> inputs = processor(
	... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
	... )

	>>> outputs = model(**inputs)
	>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
	>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
	```"""
	# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
	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
	)

	vision_outputs = self.vision_model(
	pixel_values=pixel_values,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	text_outputs = self.text_model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	image_embeds = vision_outputs[1]
	image_embeds = self.visual_projection(image_embeds)

	text_embeds = text_outputs[1]
	text_embeds = self.text_projection(text_embeds)

	# normalized features
	image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
	text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)

	# cosine similarity as logits
	logit_scale = self.logit_scale.exp()
	logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
	logits_per_image = logits_per_text.t()

	loss = None
	if return_loss:
	loss = clip_loss(logits_per_text)

	if not return_dict:
	output = (
	logits_per_image,
	logits_per_text,
	text_embeds,
	image_embeds,
	text_outputs,
	vision_outputs,
	)
	return ((loss,) + output) if loss is not None else output

	return NLLBCLIPOutput(
	loss=loss,
	logits_per_image=logits_per_image,
	logits_per_text=logits_per_text,
	text_embeds=text_embeds,
	image_embeds=image_embeds,
	text_model_output=text_outputs,
	vision_model_output=vision_outputs,
	)