
"""simple docstring"""




import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		str							=							['image_processor', 'tokenizer']
			lowerCamelCase__   :		Optional[int]							=							'AutoImageProcessor'
			lowerCamelCase__   :		Dict							=							'AutoTokenizer'

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						List[Any]=None    ,				__UpperCAmelCase				:						Union[str, Any]=None    ,				**__UpperCAmelCase				:						Union[str, Any]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""    ,				__A    ,				)
									SCREAMING_SNAKE_CASE__								=      kwargs.pop("""feature_extractor"""       )

						SCREAMING_SNAKE_CASE__								=      image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError("""You need to specify an `image_processor`."""       )
						if tokenizer is None:
									raise ValueError("""You need to specify a `tokenizer`."""       )

						super().__init__(__A    ,				__A       )
						SCREAMING_SNAKE_CASE__								=      self.image_processor
						SCREAMING_SNAKE_CASE__								=      False

			def __call__(					self				:						Union[str, Any]    ,				*__UpperCAmelCase				:						Optional[int]    ,				**__UpperCAmelCase				:						Tuple       )   ->    Any:
						# For backward compatibility
						if self._in_target_context_manager:
									return self.current_processor(*__A    ,				**__A       )

						SCREAMING_SNAKE_CASE__								=      kwargs.pop("""images"""    ,				__A       )
						SCREAMING_SNAKE_CASE__								=      kwargs.pop("""text"""    ,				__A       )
						if len(__A       ) > 0:
									SCREAMING_SNAKE_CASE__								=      args[0]
									SCREAMING_SNAKE_CASE__								=      args[1:]

						if images is None and text is None:
									raise ValueError("""You need to specify either an `images` or `text` input to process."""       )

						if images is not None:
									SCREAMING_SNAKE_CASE__								=      self.image_processor(__A    ,				*__A    ,				**__A       )
						if text is not None:
									SCREAMING_SNAKE_CASE__								=      self.tokenizer(__A    ,				**__A       )

						if text is None:
									return inputs
						elif images is None:
									return encodings
						else:
									SCREAMING_SNAKE_CASE__								=      encodings["""input_ids"""]
									return inputs

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				*__UpperCAmelCase				:						Union[str, Any]    ,				**__UpperCAmelCase				:						Optional[Any]       )   ->    int:
						return self.tokenizer.batch_decode(*__A    ,				**__A       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				*__UpperCAmelCase				:						Optional[Any]    ,				**__UpperCAmelCase				:						Union[str, Any]       )   ->    Optional[Any]:
						return self.tokenizer.decode(*__A    ,				**__A       )

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[int]:
						warnings.warn(
						    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
						    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
						    """your images inputs, or in a separate call."""       )
						SCREAMING_SNAKE_CASE__								=      True
						SCREAMING_SNAKE_CASE__								=      self.tokenizer
						yield
						SCREAMING_SNAKE_CASE__								=      self.image_processor
						SCREAMING_SNAKE_CASE__								=      False

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						List[Any]=False    ,				__UpperCAmelCase				:						Any=None       )   ->    Tuple:
						if added_vocab is None:
									SCREAMING_SNAKE_CASE__								=      self.tokenizer.get_added_vocab()

						SCREAMING_SNAKE_CASE__								=      {}

						while tokens:
									SCREAMING_SNAKE_CASE__								=      re.search(r"""<s_(.*?)>"""    ,				__A    ,				re.IGNORECASE       )
									if start_token is None:
												break
									SCREAMING_SNAKE_CASE__								=      start_token.group(1       )
									SCREAMING_SNAKE_CASE__								=      re.search(rF"""</s_{key}>"""    ,				__A    ,				re.IGNORECASE       )
									SCREAMING_SNAKE_CASE__								=      start_token.group()
									if end_token is None:
												SCREAMING_SNAKE_CASE__								=      tokens.replace(__A    ,				""""""       )
									else:
												SCREAMING_SNAKE_CASE__								=      end_token.group()
												SCREAMING_SNAKE_CASE__								=      re.escape(__A       )
												SCREAMING_SNAKE_CASE__								=      re.escape(__A       )
												SCREAMING_SNAKE_CASE__								=      re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""    ,				__A    ,				re.IGNORECASE       )
												if content is not None:
															SCREAMING_SNAKE_CASE__								=      content.group(1       ).strip()
															if r"<s_" in content and r"</s_" in content:  # non-leaf node
																		SCREAMING_SNAKE_CASE__								=      self.tokenajson(__A    ,				is_inner_value=__A    ,				added_vocab=__A       )
																		if value:
																					if len(__A       ) == 1:
																								SCREAMING_SNAKE_CASE__								=      value[0]
																					SCREAMING_SNAKE_CASE__								=      value
															else:  # leaf nodes
																		SCREAMING_SNAKE_CASE__								=      []
																		for leaf in content.split(r"""<sep/>"""       ):
																					SCREAMING_SNAKE_CASE__								=      leaf.strip()
																					if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																								SCREAMING_SNAKE_CASE__								=      leaf[1:-2]  # for categorical special tokens
																					output[key].append(__A       )
																		if len(output[key]       ) == 1:
																					SCREAMING_SNAKE_CASE__								=      output[key][0]

												SCREAMING_SNAKE_CASE__								=      tokens[tokens.find(__A       ) + len(__A       ) :].strip()
												if tokens[:6] == r"<sep/>":  # non-leaf nodes
															return [output] + self.tokenajson(tokens[6:]    ,				is_inner_value=__A    ,				added_vocab=__A       )

						if len(__A       ):
									return [output] if is_inner_value else output
						else:
									return [] if is_inner_value else {"text_sequence": tokens}

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,				__A    ,				)
						return self.image_processor_class





			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    int:
						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,				__A    ,				)
						return self.image_processor





"""simple docstring"""




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_							:			Tuple											= logging.getLogger(__name__)


A_							:			Tuple											= "Hello world! cécé herlolip"


A_							:			Dict											= 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 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      BertAbsConfig(
			    temp_dir="""."""		,				finetune_bert=snake_case__		,				large=snake_case__		,				share_emb=snake_case__		,				use_bert_emb=snake_case__		,				encoder="""bert"""		,				max_pos=5_12		,				enc_layers=6		,				enc_hidden_size=5_12		,				enc_heads=8		,				enc_ff_size=5_12		,				enc_dropout=0.2		,				dec_layers=6		,				dec_hidden_size=7_68		,				dec_heads=8		,				dec_ff_size=20_48		,				dec_dropout=0.2		,				)
			SCREAMING_SNAKE_CASE__								=      torch.load(snake_case__		,				lambda snake_case__		,				snake_case__    : storage    )
			SCREAMING_SNAKE_CASE__								=      AbsSummarizer(snake_case__		,				torch.device("""cpu"""    )		,				snake_case__    )
			original.eval()

			SCREAMING_SNAKE_CASE__								=      BertAbsSummarizer(snake_case__		,				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__								=      BertTokenizer.from_pretrained("""bert-base-uncased"""    )

			# prepare the model inputs
			SCREAMING_SNAKE_CASE__								=      tokenizer.encode("""This is sample éàalj'-."""    )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      torch.tensor(snake_case__    ).unsqueeze(0    )
			SCREAMING_SNAKE_CASE__								=      tokenizer.encode("""This is sample 3 éàalj'-."""    )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      torch.tensor(snake_case__    ).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__								=      encoder_input_ids
			SCREAMING_SNAKE_CASE__								=      decoder_input_ids
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      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__								=      original(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      original.generator(snake_case__    )

			SCREAMING_SNAKE_CASE__								=      new_model(
			    snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      new_model.generator(snake_case__    )

			SCREAMING_SNAKE_CASE__								=      torch.max(torch.abs(output_converted_model - output_original_model    )    ).item()
			print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case__    )    )
			SCREAMING_SNAKE_CASE__								=      torch.max(torch.abs(output_converted_generator - output_original_generator    )    ).item()
			print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case__    )    )

			SCREAMING_SNAKE_CASE__								=      torch.allclose(snake_case__		,				snake_case__		,				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_							:			Dict											= 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_							:			Tuple											= parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)


