

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_      =     logging.get_logger(__name__)

a_      =     {
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='bridgetower_vision_model'
			def __init__(      self				:			Tuple   ,   a				:			Any=768   ,   a				:			Optional[Any]=12   ,   a				:			Dict=3   ,   a				:			Dict=16   ,   a				:			Optional[Any]=288   ,   a				:			List[Any]=1   ,   a				:			Tuple=1e-05   ,   a				:			Union[str, Any]=False   ,   a				:			Any=True   ,   a				:			Optional[int]=False   ,   **a				:			Optional[Any]   ,   )				->							Optional[int]:

				"""simple docstring"""

				super().__init__(**a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Dict					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   Any					  =							num_channels
				SCREAMING_SNAKE_CASE :   int					  =							patch_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							image_size
				SCREAMING_SNAKE_CASE :   Dict					  =							initializer_factor
				SCREAMING_SNAKE_CASE :   Any					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							stop_gradient
				SCREAMING_SNAKE_CASE :   Tuple					  =							share_layernorm
				SCREAMING_SNAKE_CASE :   Any					  =							remove_last_layer






			@classmethod
			def 				__UpperCamelCase			(      cls				:			str   ,   a				:			Union[str, os.PathLike]   ,   **a				:			Dict						)				->							"PretrainedConfig":

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Any					  =							cls.get_config_dict(a   ,   **a						)

				if config_dict.get("model_type"						) == "bridgetower":
					SCREAMING_SNAKE_CASE :   Dict					  =							config_dict["text_config"]

				if "model_type" in config_dict and hasattr(cls   ,   "model_type"						) and config_dict["model_type"] != cls.model_type:
					logger.warning(
					    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
					    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."						)

				return cls.from_dict(a   ,   **a						)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='bridgetower_text_model'
			def __init__(      self				:			str   ,   a				:			int=5_0265   ,   a				:			List[Any]=768   ,   a				:			int=12   ,   a				:			Optional[Any]=12   ,   a				:			Union[str, Any]=1   ,   a				:			str=3072   ,   a				:			List[Any]="gelu"   ,   a				:			Any=0.1   ,   a				:			List[str]=0.1   ,   a				:			Tuple=514   ,   a				:			Optional[int]=1   ,   a				:			Tuple=1e-05   ,   a				:			Union[str, Any]=1   ,   a				:			Any=0   ,   a				:			str=2   ,   a				:			str="absolute"   ,   a				:			Any=True   ,   **a				:			Dict   ,   )				->							Optional[int]:

				"""simple docstring"""

				super().__init__(**a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							vocab_size
				SCREAMING_SNAKE_CASE :   Any					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   List[Any]					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   int					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_factor
				SCREAMING_SNAKE_CASE :   List[str]					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Dict					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   Tuple					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Any					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   List[Any]					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							position_embedding_type
				SCREAMING_SNAKE_CASE :   List[str]					  =							use_cache
				SCREAMING_SNAKE_CASE :   List[str]					  =							pad_token_id
				SCREAMING_SNAKE_CASE :   Tuple					  =							bos_token_id
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							eos_token_id






			@classmethod
			def 				__UpperCamelCase			(      cls				:			Tuple   ,   a				:			Union[str, os.PathLike]   ,   **a				:			List[str]						)				->							"PretrainedConfig":

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							cls.get_config_dict(a   ,   **a						)

				if config_dict.get("model_type"						) == "bridgetower":
					SCREAMING_SNAKE_CASE :   str					  =							config_dict["text_config"]

				if "model_type" in config_dict and hasattr(cls   ,   "model_type"						) and config_dict["model_type"] != cls.model_type:
					logger.warning(
					    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
					    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."						)

				return cls.from_dict(a   ,   **a						)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='bridgetower'
			def __init__(      self				:			List[str]   ,   a				:			List[Any]=True   ,   a				:			List[Any]="gelu"   ,   a				:			Union[str, Any]=768   ,   a				:			Tuple=1   ,   a				:			List[Any]=1e-05   ,   a				:			Dict=False   ,   a				:			Optional[int]="add"   ,   a				:			Any=12   ,   a				:			Optional[int]=6   ,   a				:			int=False   ,   a				:			Optional[int]=False   ,   a				:			Dict=None   ,   a				:			Tuple=None   ,   **a				:			Union[str, Any]   ,   )				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							kwargs.pop("text_config_dict"   ,   a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							kwargs.pop("vision_config_dict"   ,   a						)

				super().__init__(**a						)
				SCREAMING_SNAKE_CASE :   Any					  =							share_cross_modal_transformer_layers
				SCREAMING_SNAKE_CASE :   Any					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Any					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							initializer_factor
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   int					  =							share_link_tower_layers
				SCREAMING_SNAKE_CASE :   str					  =							link_tower_type
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Dict					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							tie_word_embeddings
				SCREAMING_SNAKE_CASE :   Any					  =							init_layernorm_from_vision_encoder

				if text_config is None:
					SCREAMING_SNAKE_CASE :   Any					  =							{}
					logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."						)

				if vision_config is None:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{}
					logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							BridgeTowerTextConfig(**a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							BridgeTowerVisionConfig(**a						)
			@classmethod
			def 				__UpperCamelCase			(      cls				:			List[Any]   ,   a				:			BridgeTowerTextConfig   ,   a				:			BridgeTowerVisionConfig   ,   **a				:			List[str]						)				->							Optional[int]:

				"""simple docstring"""

				return cls(text_config=text_config.to_dict()   ,   vision_config=vision_config.to_dict()   ,   **a						)






			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							copy.deepcopy(self.__dict__						)
				SCREAMING_SNAKE_CASE :   str					  =							self.text_config.to_dict()
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.vision_config.to_dict()
				SCREAMING_SNAKE_CASE :   Dict					  =							self.__class__.model_type
				return output


import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
a_      =     logging.getLogger(__name__)


a_      =     'Hello world! cécé herlolip'


a_      =     namedtuple(
    'BertAbsConfig',
    [
        'temp_dir',
        'large',
        'use_bert_emb',
        'finetune_bert',
        'encoder',
        'share_emb',
        'max_pos',
        'enc_layers',
        'enc_hidden_size',
        'enc_heads',
        'enc_ff_size',
        'enc_dropout',
        'dec_layers',
        'dec_hidden_size',
        'dec_heads',
        'dec_ff_size',
        'dec_dropout',
    ],
)

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   List[Any]					  =							BertAbsConfig(
	    temp_dir="."				,      finetune_bert=_a				,      large=_a				,      share_emb=_a				,      use_bert_emb=_a				,      encoder="bert"				,      max_pos=512				,      enc_layers=6				,      enc_hidden_size=512				,      enc_heads=8				,      enc_ff_size=512				,      enc_dropout=0.2				,      dec_layers=6				,      dec_hidden_size=768				,      dec_heads=8				,      dec_ff_size=2048				,      dec_dropout=0.2				,      )
	SCREAMING_SNAKE_CASE :   Dict					  =							torch.load(_a				,      lambda _a				,      _a: storage)
	SCREAMING_SNAKE_CASE :   str					  =							AbsSummarizer(_a				,      torch.device("cpu")				,      _a)
	original.eval()

	SCREAMING_SNAKE_CASE :   List[str]					  =							BertAbsSummarizer(_a				,      torch.device("cpu"))
	new_model.eval()

	# -------------------
	# Convert the weights
	# -------------------

	logging.info("convert the model")
	new_model.bert.load_state_dict(original.bert.state_dict())
	new_model.decoder.load_state_dict(original.decoder.state_dict())
	new_model.generator.load_state_dict(original.generator.state_dict())

	# ----------------------------------
	# Make sure the outpus are identical
	# ----------------------------------

	logging.info("Make sure that the models' outputs are identical")
	SCREAMING_SNAKE_CASE :   List[str]					  =							BertTokenizer.from_pretrained("bert-base-uncased")

	# prepare the model inputs
	SCREAMING_SNAKE_CASE :   List[str]					  =							tokenizer.encode("This is sample éàalj'-.")
	encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
	SCREAMING_SNAKE_CASE :   int					  =							torch.tensor(_a).unsqueeze(0)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							tokenizer.encode("This is sample 3 éàalj'-.")
	decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
	SCREAMING_SNAKE_CASE :   int					  =							torch.tensor(_a).unsqueeze(0)

	# failsafe to make sure the weights reset does not affect the
	# loaded weights.
	assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0

	# forward pass
	SCREAMING_SNAKE_CASE :   List[Any]					  =							encoder_input_ids
	SCREAMING_SNAKE_CASE :   List[Any]					  =							decoder_input_ids
	SCREAMING_SNAKE_CASE :   Dict					  =							None
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							None
	SCREAMING_SNAKE_CASE :   List[Any]					  =							None
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							None
	SCREAMING_SNAKE_CASE :   Dict					  =							None

	# The original model does not apply the geneator layer immediatly but rather in
	# the beam search (where it combines softmax + linear layer). Since we already
	# apply the softmax in our generation process we only apply the linear layer here.
	# We make sure that the outputs of the full stack are identical
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							original(_a				,      _a				,      _a				,      _a				,      _a				,      _a				,      _a)[0]
	SCREAMING_SNAKE_CASE :   Dict					  =							original.generator(_a)

	SCREAMING_SNAKE_CASE :   Any					  =							new_model(
	    _a				,      _a				,      _a				,      _a				,      _a)[0]
	SCREAMING_SNAKE_CASE :   Tuple					  =							new_model.generator(_a)

	SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.max(torch.abs(output_converted_model - output_original_model)).item()
	print("Maximum absolute difference beween weights: {:.2f}".format(_a))
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
	print("Maximum absolute difference beween weights: {:.2f}".format(_a))

	SCREAMING_SNAKE_CASE :   int					  =							torch.allclose(_a				,      _a				,      atol=1E-3)
	if are_identical:
		logging.info("all weights are equal up to 1e-3")
	else:
		raise ValueError("the weights are different. The new model is likely different from the original one.")

	# The model has been saved with torch.save(model) and this is bound to the exact
	# directory structure. We save the state_dict instead.
	logging.info("saving the model's state dictionary")
	torch.save(
	    new_model.state_dict()				,      "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")


if __name__ == "__main__":
			a_      =     argparse.ArgumentParser()
			parser.add_argument(
			    '--bertabs_checkpoint_path',
			    default=None,
			    type=str,
			    required=True,
			    help='Path the official PyTorch dump.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=str,
			    required=True,
			    help='Path to the output PyTorch model.',
			)
			a_      =     parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)