





"""simple docstring"""





import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
  from cookiecutter.main import cookiecutter

  __A											=						True
except ImportError:
  __A											=						False

__A											=						logging.get_logger(__name__)  # pylint: disable=invalid-name
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  return AddNewModelCommand(args.testing	,  args.testing_file	,  path=args.path     )






class    _lowerCAmelCase  (	a_			):
       """simple docstring"""




       @staticmethod
       def 	snake_case		(       __UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				parser.add_parser('add-new-model' )
         add_new_model_parser.add_argument('--testing'       ,					action='store_true'       ,					help='If in testing mode.' )
         add_new_model_parser.add_argument('--testing_file'       ,					type=_SCREAMING_SNAKE_CASE       ,					help='Configuration file on which to run.' )
         add_new_model_parser.add_argument(
             '--path'       ,					type=_SCREAMING_SNAKE_CASE       ,					help='Path to cookiecutter. Should only be used for testing purposes.' )
         add_new_model_parser.set_defaults(func=_SCREAMING_SNAKE_CASE )






       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None       ,					*__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				testing
         lowerCAmelCase__     :Optional[Any]											=				testing_file
         lowerCAmelCase__     :Dict											=				path






       def 	snake_case		(       self ):



         '''simple docstring'''

         warnings.warn(
             'The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '
             'It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '
             'checks, you should use `transformers-cli add-new-model-like` instead.' )
         if not _has_cookiecutter:
           raise ImportError(
               'Model creation dependencies are required to use the `add_new_model` command. Install them by running '
               'the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n' )
         # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
         lowerCAmelCase__     :Optional[Any]											=				[directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:2_2]]
         if len(_SCREAMING_SNAKE_CASE ) > 0:
           raise ValueError(
               'Several directories starting with `cookiecutter-template-` in current working directory. '
               'Please clean your directory by removing all folders starting with `cookiecutter-template-` or '
               'change your working directory.' )

         lowerCAmelCase__     :Optional[int]											=				(
             Path(_SCREAMING_SNAKE_CASE ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
         )
         lowerCAmelCase__     :Optional[Any]											=				path_to_transformer_root / 'templates' / 'adding_a_new_model'

         # Execute cookiecutter
         if not self._testing:
           cookiecutter(str(_SCREAMING_SNAKE_CASE ) )
         else:
           with open(self._testing_file       ,					'r' ) as configuration_file:
             lowerCAmelCase__     :str											=				json.load(_SCREAMING_SNAKE_CASE )

           cookiecutter(
               str(path_to_cookiecutter if self._path is None else self._path )       ,					no_input=_SCREAMING_SNAKE_CASE       ,					extra_context=_SCREAMING_SNAKE_CASE       ,					)

         lowerCAmelCase__     :Optional[Any]											=				[directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:2_2]][0]

         # Retrieve configuration
         with open(directory + '/configuration.json'       ,					'r' ) as configuration_file:
           lowerCAmelCase__     :Dict											=				json.load(_SCREAMING_SNAKE_CASE )

         lowerCAmelCase__     :Tuple											=				configuration['lowercase_modelname']
         lowerCAmelCase__     :Optional[int]											=				configuration['generate_tensorflow_pytorch_and_flax']
         os.remove(F"{directory}/configuration.json" )

         lowerCAmelCase__     :str											=				'PyTorch' in generate_tensorflow_pytorch_and_flax
         lowerCAmelCase__     :Optional[int]											=				'TensorFlow' in generate_tensorflow_pytorch_and_flax
         lowerCAmelCase__     :Optional[int]											=				'Flax' in generate_tensorflow_pytorch_and_flax

         lowerCAmelCase__     :str											=				F"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
         os.makedirs(_SCREAMING_SNAKE_CASE       ,					exist_ok=_SCREAMING_SNAKE_CASE )
         os.makedirs(F"{path_to_transformer_root}/tests/models/{lowercase_model_name}"       ,					exist_ok=_SCREAMING_SNAKE_CASE )

         # Tests require submodules as they have parent imports
         with open(F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py"       ,					'w' ):
           pass

         shutil.move(
             F"{directory}/__init__.py"       ,					F"{model_dir}/__init__.py"       ,					)
         shutil.move(
             F"{directory}/configuration_{lowercase_model_name}.py"       ,					F"{model_dir}/configuration_{lowercase_model_name}.py"       ,					)

         def remove_copy_lines(__UpperCAmelCase ):
           with open(_SCREAMING_SNAKE_CASE       ,					'r' ) as f:
             lowerCAmelCase__     :int											=				f.readlines()
           with open(_SCREAMING_SNAKE_CASE       ,					'w' ) as f:
             for line in lines:
               if "# Copied from transformers." not in line:
                 f.write(_SCREAMING_SNAKE_CASE )

         if output_pytorch:
           if not self._testing:
             remove_copy_lines(F"{directory}/modeling_{lowercase_model_name}.py" )

           shutil.move(
               F"{directory}/modeling_{lowercase_model_name}.py"       ,					F"{model_dir}/modeling_{lowercase_model_name}.py"       ,					)

           shutil.move(
               F"{directory}/test_modeling_{lowercase_model_name}.py"       ,					F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py"       ,					)
         else:
           os.remove(F"{directory}/modeling_{lowercase_model_name}.py" )
           os.remove(F"{directory}/test_modeling_{lowercase_model_name}.py" )

         if output_tensorflow:
           if not self._testing:
             remove_copy_lines(F"{directory}/modeling_tf_{lowercase_model_name}.py" )

           shutil.move(
               F"{directory}/modeling_tf_{lowercase_model_name}.py"       ,					F"{model_dir}/modeling_tf_{lowercase_model_name}.py"       ,					)

           shutil.move(
               F"{directory}/test_modeling_tf_{lowercase_model_name}.py"       ,					F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py"       ,					)
         else:
           os.remove(F"{directory}/modeling_tf_{lowercase_model_name}.py" )
           os.remove(F"{directory}/test_modeling_tf_{lowercase_model_name}.py" )

         if output_flax:
           if not self._testing:
             remove_copy_lines(F"{directory}/modeling_flax_{lowercase_model_name}.py" )

           shutil.move(
               F"{directory}/modeling_flax_{lowercase_model_name}.py"       ,					F"{model_dir}/modeling_flax_{lowercase_model_name}.py"       ,					)

           shutil.move(
               F"{directory}/test_modeling_flax_{lowercase_model_name}.py"       ,					F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py"       ,					)
         else:
           os.remove(F"{directory}/modeling_flax_{lowercase_model_name}.py" )
           os.remove(F"{directory}/test_modeling_flax_{lowercase_model_name}.py" )

         shutil.move(
             F"{directory}/{lowercase_model_name}.md"       ,					F"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md"       ,					)

         shutil.move(
             F"{directory}/tokenization_{lowercase_model_name}.py"       ,					F"{model_dir}/tokenization_{lowercase_model_name}.py"       ,					)

         shutil.move(
             F"{directory}/tokenization_fast_{lowercase_model_name}.py"       ,					F"{model_dir}/tokenization_{lowercase_model_name}_fast.py"       ,					)

         from os import fdopen, remove
         from shutil import copymode, move
         from tempfile import mkstemp

         def replace(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):
           # Create temp file
           lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				mkstemp()
           lowerCAmelCase__     :Union[str, Any]											=				False
           with fdopen(_SCREAMING_SNAKE_CASE       ,					'w' ) as new_file:
             with open(_SCREAMING_SNAKE_CASE ) as old_file:
               for line in old_file:
                 new_file.write(_SCREAMING_SNAKE_CASE )
                 if line_to_copy_below in line:
                   lowerCAmelCase__     :Optional[int]											=				True
                   for line_to_copy in lines_to_copy:
                     new_file.write(_SCREAMING_SNAKE_CASE )

           if not line_found:
             raise ValueError(F"Line {line_to_copy_below} was not found in file." )

           # Copy the file permissions from the old file to the new file
           copymode(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE )
           # Remove original file
           remove(_SCREAMING_SNAKE_CASE )
           # Move new file
           move(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE )

         def skip_units(__UpperCAmelCase ):
           return (
               ("generating PyTorch" in line and not output_pytorch)
               or ("generating TensorFlow" in line and not output_tensorflow)
               or ("generating Flax" in line and not output_flax)
           )

         def replace_in_files(__UpperCAmelCase ):
           with open(_SCREAMING_SNAKE_CASE ) as datafile:
             lowerCAmelCase__     :Optional[Any]											=				[]
             lowerCAmelCase__     :Dict											=				False
             lowerCAmelCase__     :List[str]											=				False
             for line in datafile:
               if "# To replace in: " in line and "##" not in line:
                 lowerCAmelCase__     :Union[str, Any]											=				line.split('"' )[1]
                 lowerCAmelCase__     :Optional[int]											=				skip_units(_SCREAMING_SNAKE_CASE )
               elif "# Below: " in line and "##" not in line:
                 lowerCAmelCase__     :Any											=				line.split('"' )[1]
                 lowerCAmelCase__     :int											=				skip_units(_SCREAMING_SNAKE_CASE )
               elif "# End." in line and "##" not in line:
                 if not skip_file and not skip_snippet:
                   replace(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE )

                 lowerCAmelCase__     :Union[str, Any]											=				[]
               elif "# Replace with" in line and "##" not in line:
                 lowerCAmelCase__     :Tuple											=				[]
               elif "##" not in line:
                 lines_to_copy.append(_SCREAMING_SNAKE_CASE )

           remove(_SCREAMING_SNAKE_CASE )

         replace_in_files(F"{directory}/to_replace_{lowercase_model_name}.py" )
         os.rmdir(_SCREAMING_SNAKE_CASE )



'''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)
_UpperCamelCase						:  Optional[Any]							       =				logging.getLogger(__name__)


_UpperCamelCase						:  List[Any]							       =				"Hello world! cécé herlolip"


_UpperCamelCase						:  int							       =				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       snake_case   (	snake_case      : int					,		snake_case      : Optional[Any]			) ->  Optional[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							BertAbsConfig(
        temp_dir='.'					,		finetune_bert=snake_case					,		large=snake_case					,		share_emb=snake_case					,		use_bert_emb=snake_case					,		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 						=							torch.load(snake_case					,		lambda snake_case					,		snake_case			: storage			)
    lowerCAmelCase 						=							AbsSummarizer(snake_case					,		torch.device('cpu'			)					,		snake_case			)
    original.eval()

    lowerCAmelCase 						=							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'			)
    lowerCAmelCase 						=							BertTokenizer.from_pretrained('bert-base-uncased'			)

    # prepare the model inputs
    lowerCAmelCase 						=							tokenizer.encode('This is sample éàalj\'-.'			)
    encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(snake_case			))			)
    lowerCAmelCase 						=							torch.tensor(snake_case			).unsqueeze(0			)
    lowerCAmelCase 						=							tokenizer.encode('This is sample 3 éàalj\'-.'			)
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(snake_case			))			)
    lowerCAmelCase 						=							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
    lowerCAmelCase 						=							encoder_input_ids
    lowerCAmelCase 						=							decoder_input_ids
    lowerCAmelCase 						=							lowerCAmelCase 						=							None
    lowerCAmelCase 						=							None
    lowerCAmelCase 						=							lowerCAmelCase 						=							None
    lowerCAmelCase 						=							lowerCAmelCase 						=							None
    lowerCAmelCase 						=							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 						=							original(snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)[0]
    lowerCAmelCase 						=							original.generator(snake_case			)

    lowerCAmelCase 						=							new_model(
        snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)[0]
    lowerCAmelCase 						=							new_model.generator(snake_case			)

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

    lowerCAmelCase 						=							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__":
     _UpperCamelCase						:  List[str]							       =				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.",
     )
     _UpperCamelCase						:  Optional[int]							       =				parser.parse_args()

     convert_bertabs_checkpoints(
         args.bertabs_checkpoint_path,
         args.pytorch_dump_folder_path,
     )





