


"""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)
__snake_case         =					logging.getLogger(__name__)


__snake_case         =					"""Hello world! cécé herlolip"""


__snake_case         =					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		(		lowercase						:						Union[str, Any]		,							lowercase						:						str				)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   List[Any]         =					BertAbsConfig(
        temp_dir="."		,							finetune_bert=lowercase		,							large=lowercase		,							share_emb=lowercase		,							use_bert_emb=lowercase		,							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		,							)
    snake_case					:   Dict         =					torch.load(lowercase		,							lambda lowercase		,							lowercase				: storage				)
    snake_case					:   str         =					AbsSummarizer(lowercase		,							torch.device("cpu"				)		,							lowercase				)
    original.eval()

    snake_case					:   List[str]         =					BertAbsSummarizer(lowercase		,							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"				)
    snake_case					:   Dict         =					BertTokenizer.from_pretrained("bert-base-uncased"				)

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

    snake_case					:   str         =					new_model(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase				)[0]
    snake_case					:   List[Any]         =					new_model.generator(lowercase				)

    snake_case					:   Dict         =					torch.max(torch.abs(output_converted_model - output_original_model				)				).item()
    print("Maximum absolute difference beween weights: {:.2f}".format(lowercase				)				)
    snake_case					:   Optional[int]         =					torch.max(torch.abs(output_converted_generator - output_original_generator				)				).item()
    print("Maximum absolute difference beween weights: {:.2f}".format(lowercase				)				)

    snake_case					:   Optional[Any]         =					torch.allclose(lowercase		,							lowercase		,							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__":
  __snake_case         =					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.""",
  )
  __snake_case         =					parser.parse_args()

  convert_bertabs_checkpoints(
      args.bertabs_checkpoint_path,
      args.pytorch_dump_folder_path,
  )









"""simple docstring"""



import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizer
   __UpperCAmelCase				: List[Any]													=						LayoutLMTokenizerFast
   __UpperCAmelCase				: List[Any]													=						True
   __UpperCAmelCase				: Dict													=						True







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       super().setUp()

       snake_case					:   Dict         =					[
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       snake_case					:   List[str]         =					os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES["vocab_file"]		)
       with open(self.vocab_file		,			"w"		,			encoding="utf-8"		) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)







   def 				lowerCamelCase	(			self		,			**UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,			**UpperCamelCase__		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   str         =					"UNwant\u00E9d,running"
       snake_case					:   Optional[int]         =					"unwanted, running"
       return input_text, output_text







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.tokenizer_class(self.vocab_file		)

       snake_case					:   Optional[Any]         =					tokenizer.tokenize("UNwant\u00E9d,running"		)
       self.assertListEqual(UpperCamelCase__		,			["un", "##want", "##ed", ",", "runn", "##ing"]		)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)		,			[7, 4, 5, 10, 8, 9]		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       pass





