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""" GPT Neo model configuration"""
import os
from typing import Tuple, Union

from transformers import AutoConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


logger = logging.get_logger(__name__)

GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "EleutherAI/gpt-neo-125M": "https://huggingface.co/EleutherAI/gpt-neo-125M/resolve/main/config.json",
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}


class VGPTNeoConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`GPTNeoModel`]. It is used to instantiate a GPT
    Neo model according to the specified arguments, defining the model architecture. Instantiating a configuration with
    the defaults will yield a similar configuration to that of the GPTNeo
    [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    TODO: this doc is completely out of sync with the actual args

    Args:
        vocab_size (`int`, *optional*, defaults to 50257):
            Vocabulary size of the GPT Neo model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GPTNeoModel`]. Vocabulary size of the model. Defines the different
            tokens that can be represented by the *inputs_ids* passed to the forward method of [`GPTNeoModel`].
        additional_vocab_size (`int`, *optional`, defaults to 0):
            Additional vocabulary size of the model, typically for the special "<img>" token. Additional vocab tokens
            are always trainable whereas regular vocab tokens can be frozen or not.
        attention_types (`List`, *optional*, defaults to `[[["global", "local"], 12]]`):
            The type of attention for each layer in a `List` of the following format `[[["attention_type"],
            num_layerss]]` e.g. for a 24 layer model `[[["global"], 24]]` or `[[["global", "local"], 12]]` Choose the
            value of `attention_type` from `["global", "local"]`
        hidden_size (`int`, *optional*, defaults to 2048):
            Dimensionality of the encoder layers and the pooler layer.
        num_layers (`int`, *optional*, defaults to 24):
            Number of hidden layers in the Transformer encoder.
        num_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 8192):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        activation_function (`str` or `function`, *optional*, defaults to `"gelu_new"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        embed_dropout (`float`, *optional*, defaults to 0.0):
            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`GPTNeoModel`].
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        alpha_initializer (`str`, *optional*, defaults to `"ones"`):
            Initialization type for the alphas.
        alphas_initializer_range (`float`, *optional*, defaults to 0.0):
            The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross Attention.
        alpha_type (`str`, *optional*, defaults to `"vector"`):
            Whether the gating alphas should be vectors or single floats.
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
            The epsilon used by the layer normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        cross_layer_interval (`int`, *optional*, default to 1)
            Interval for cross attention (from text to image) layers.
    Example:
    ```python
    >>> from transformers import GPTNeoConfig, GPTNeoModel
    >>> # Initializing a GPTNeo EleutherAI/gpt-neo-1.3B style configuration
    >>> configuration = GPTNeoConfig()
    >>> # Initializing a model (with random weights) from the EleutherAI/gpt-neo-1.3B style configuration
    >>> model = GPTNeoModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "vgpt_neo"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

    def __init__(
        self,
        vocab_size=50257,
        additional_vocab_size=0,
        max_position_embeddings=2048,
        hidden_size=2048,
        num_layers=24,
        attention_types=[[["global", "local"], 12]],
        num_heads=16,
        intermediate_size=None,
        window_size=256,
        activation_function="gelu_new",
        resid_dropout=0.0,
        embed_dropout=0.0,
        attention_dropout=0.0,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,
        alpha_initializer="ones",
        alphas_initializer_range=0.0,
        alpha_type="vector",
        summary_type="cls_index",
        summary_use_proj=True,
        summary_activation=None,
        summary_proj_to_labels=True,
        summary_first_dropout=0.1,
        use_cache=True,
        bos_token_id=50256,
        eos_token_id=50256,
        cross_layer_interval=1,
        tie_word_embeddings=False,
        freeze_text_layers=True,
        freeze_lm_head=False,
        freeze_vision_layers=True,
        vision_model_name="google/vit-base-patch16-224",
        vision_model_params="{}",
        vision_embed_dim=768,
        vision_image_size=224,
        image_token_index=50257,
        use_resampler=False,
        resampler_n_latents=64,
        resampler_depth=6,
        resampler_n_heads=16,
        resampler_head_dim=96,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.additional_vocab_size = additional_vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.intermediate_size = intermediate_size
        self.window_size = window_size
        self.activation_function = activation_function
        self.resid_dropout = resid_dropout
        self.embed_dropout = embed_dropout
        self.attention_dropout = attention_dropout
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range
        self.alpha_initializer = alpha_initializer
        self.alphas_initializer_range = alphas_initializer_range
        self.alpha_type = alpha_type
        self.summary_type = summary_type
        self.summary_use_proj = summary_use_proj
        self.summary_activation = summary_activation
        self.summary_first_dropout = summary_first_dropout
        self.summary_proj_to_labels = summary_proj_to_labels
        self.use_cache = use_cache

        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.cross_layer_interval = cross_layer_interval
        self.freeze_vision_layers = freeze_vision_layers
        self.vision_model_name = vision_model_name
        self.vision_model_params = vision_model_params

        self.tie_word_embeddings = tie_word_embeddings
        self.freeze_text_layers = freeze_text_layers
        self.freeze_lm_head = freeze_lm_head
        self.image_token_index = image_token_index
        self.attention_types = attention_types
        self.attention_layers = self.expand_attention_types_params(attention_types)

        self.vision_embed_dim = vision_embed_dim
        self.vision_image_size = vision_image_size

        # Resampler params
        self.use_resampler = use_resampler
        self.resampler_n_latents = resampler_n_latents
        self.resampler_depth = resampler_depth
        self.resampler_n_heads = resampler_n_heads
        self.resampler_head_dim = resampler_head_dim

        # IMPORTANT: Do not do any __init__ args-based checks in the constructor, since
        # PretrainedConfig.from_dict first instantiates the class with the config dict and only then
        # updates the config object with `kwargs` from from_pretrained, so during the instantiation
        # of this object many attributes have default values and haven't yet been overridden.
        # Do any required checks inside `from_pretrained` once the superclass' `from_pretrained` was run.

        super().__init__(
            bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
        )

    def check_compatibilities(self):
        if self.tie_word_embeddings and (self.freeze_text_layers != self.freeze_lm_head):
            raise ValueError(
                "if `tie_word_embeddings` is True, then `freeze_lm_head` and `freeze_text_layers` must be equal."
            )

        vision_model_params = eval(self.vision_model_params)
        config = AutoConfig.from_pretrained(self.vision_model_name, **vision_model_params)
        if hasattr(config, "vision_config"):
            vision_config = config.vision_config
        else:
            vision_config = config
        vision_embed_dim = vision_config.hidden_size
        if self.vision_embed_dim != vision_embed_dim:
            raise ValueError(
                f"vision_embed_dim ({self.vision_embed_dim}) must match the hidden size of the vision model"
                f" ({vision_embed_dim})"
            )
        vision_image_size = vision_config.image_size
        if self.vision_image_size != vision_image_size:
            raise ValueError(
                f"vision_image_size ({self.vision_image_size}) must match the hidden size of the vision model"
                f" ({vision_image_size})"
            )

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        outputs = super(VGPTNeoConfig, cls).from_pretrained(pretrained_model_name_or_path, **kwargs)
        if isinstance(outputs, Tuple):
            # When called with return_unused_kwargs=True, the first item will be the config
            outputs[0].check_compatibilities()
        else:
            outputs.check_compatibilities()
        return outputs

    @staticmethod
    def expand_attention_types_params(attention_types):
        attentions = []
        for item in attention_types:
            for _ in range(item[1]):
                attentions.extend(item[0])
        return attentions