




"""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													=      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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    int:
      lowerCAmelCase__      :			Tuple   =	BertAbsConfig(
          temp_dir="""."""    ,		finetune_bert=__UpperCAmelCase    ,		large=__UpperCAmelCase    ,		share_emb=__UpperCAmelCase    ,		use_bert_emb=__UpperCAmelCase    ,		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    ,		)
      lowerCAmelCase__      :			int   =	torch.load(__UpperCAmelCase    ,		lambda __UpperCAmelCase    ,		__UpperCAmelCase				: storage				)
      lowerCAmelCase__      :			List[str]   =	AbsSummarizer(__UpperCAmelCase    ,		torch.device("""cpu"""				)    ,		__UpperCAmelCase				)
      original.eval()

      lowerCAmelCase__      :			Any   =	BertAbsSummarizer(__UpperCAmelCase    ,		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"""				)
      lowerCAmelCase__      :			Any   =	BertTokenizer.from_pretrained("""bert-base-uncased"""				)

      # prepare the model inputs
      lowerCAmelCase__      :			List[Any]   =	tokenizer.encode("""This is sample éàalj'-."""				)
      encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__UpperCAmelCase				))				)
      lowerCAmelCase__      :			Any   =	torch.tensor(__UpperCAmelCase				).unsqueeze(0				)
      lowerCAmelCase__      :			Union[str, Any]   =	tokenizer.encode("""This is sample 3 éàalj'-."""				)
      decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__UpperCAmelCase				))				)
      lowerCAmelCase__      :			Dict   =	torch.tensor(__UpperCAmelCase				).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
      lowerCAmelCase__      :			Optional[Any]   =	encoder_input_ids
      lowerCAmelCase__      :			str   =	decoder_input_ids
      lowerCAmelCase__      :			Any   =	None
      lowerCAmelCase__      :			Optional[Any]   =	None
      lowerCAmelCase__      :			Tuple   =	None
      lowerCAmelCase__      :			int   =	None
      lowerCAmelCase__      :			Any   =	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
      lowerCAmelCase__      :			Any   =	original(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)[0]
      lowerCAmelCase__      :			Any   =	original.generator(__UpperCAmelCase				)

      lowerCAmelCase__      :			List[str]   =	new_model(
          __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)[0]
      lowerCAmelCase__      :			Optional[int]   =	new_model.generator(__UpperCAmelCase				)

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

      lowerCAmelCase__      :			str   =	torch.allclose(__UpperCAmelCase    ,		__UpperCAmelCase    ,		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,
  )











"""simple docstring"""


from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Dict     				=     ["image_processor", "tokenizer"]
   _lowerCamelCase    :Dict     				=     "BlipImageProcessor"
   _lowerCamelCase    :Any     				=     ("BertTokenizer", "BertTokenizerFast")






   def __init__(		self       :		Dict   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Union[str, Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	False
         super().__init__(UpperCamelCase   ,     UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	self.image_processor






   def __call__(		self       :		int   ,     UpperCamelCase       :		ImageInput = None   ,     UpperCamelCase       :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Union[bool, str, PaddingStrategy] = False   ,     UpperCamelCase       :		Union[bool, str, TruncationStrategy] = None   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		Optional[int] = None   ,     UpperCamelCase       :		Optional[bool] = None   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Optional[Union[str, TensorType]] = None   ,     **UpperCamelCase       :		Optional[int]   ,     )			->						BatchEncoding:

         """simple docstring"""
         if images is None and text is None:
               raise ValueError("""You have to specify either images or text."""   )

         # Get only text
         if images is None:
               lowerCAmelCase__      :			Any   =	self.tokenizer
               lowerCAmelCase__      :			Optional[int]   =	self.tokenizer(
                   text=UpperCamelCase   ,     add_special_tokens=UpperCamelCase   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=UpperCamelCase   ,     stride=UpperCamelCase   ,     pad_to_multiple_of=UpperCamelCase   ,     return_attention_mask=UpperCamelCase   ,     return_overflowing_tokens=UpperCamelCase   ,     return_special_tokens_mask=UpperCamelCase   ,     return_offsets_mapping=UpperCamelCase   ,     return_token_type_ids=UpperCamelCase   ,     return_length=UpperCamelCase   ,     verbose=UpperCamelCase   ,     return_tensors=UpperCamelCase   ,     **UpperCamelCase   ,     )
               return text_encoding

         # add pixel_values
         lowerCAmelCase__      :			Tuple   =	self.image_processor(UpperCamelCase   ,     return_tensors=UpperCamelCase   )

         if text is not None:
               lowerCAmelCase__      :			Optional[int]   =	self.tokenizer(
                   text=UpperCamelCase   ,     add_special_tokens=UpperCamelCase   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=UpperCamelCase   ,     stride=UpperCamelCase   ,     pad_to_multiple_of=UpperCamelCase   ,     return_attention_mask=UpperCamelCase   ,     return_overflowing_tokens=UpperCamelCase   ,     return_special_tokens_mask=UpperCamelCase   ,     return_offsets_mapping=UpperCamelCase   ,     return_token_type_ids=UpperCamelCase   ,     return_length=UpperCamelCase   ,     verbose=UpperCamelCase   ,     return_tensors=UpperCamelCase   ,     **UpperCamelCase   ,     )
         else:
               lowerCAmelCase__      :			Tuple   =	None

         if text_encoding is not None:
               encoding_image_processor.update(UpperCamelCase   )

         return encoding_image_processor






   def 	_lowerCAmelCase   (		self       :		int   ,     *UpperCamelCase       :		Union[str, Any]   ,     **UpperCamelCase       :		List[str]   )			->						Dict:

         """simple docstring"""
         return self.tokenizer.batch_decode(*UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     *UpperCamelCase       :		Tuple   ,     **UpperCamelCase       :		List[str]   )			->						List[Any]:

         """simple docstring"""
         return self.tokenizer.decode(*UpperCamelCase   ,     **UpperCamelCase   )






   @property
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.tokenizer.model_input_names
         lowerCAmelCase__      :			Union[str, Any]   =	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





