






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


_A     :      int   					='''Hello world! cécé herlolip'''


_A     :      Any   					=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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Union[str, Any]:
							lowerCamelCase__ :     List[Any]	  =	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__ :     Tuple	  =	torch.load(UpperCamelCase    ,      lambda UpperCamelCase    ,      UpperCamelCase				: storage				)
							lowerCamelCase__ :     Optional[Any]	  =	AbsSummarizer(UpperCamelCase    ,      torch.device("""cpu"""				)    ,      UpperCamelCase				)
							original.eval()

							lowerCamelCase__ :     Dict	  =	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__ :     str	  =	BertTokenizer.from_pretrained("""bert-base-uncased"""				)

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

							lowerCamelCase__ :     List[Any]	  =	new_model(
							    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)[0]
							lowerCamelCase__ :     int	  =	new_model.generator(UpperCamelCase				)

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

							lowerCamelCase__ :     List[Any]	  =	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     :      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     :      int   					=parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)









'''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     :      List[Any]   					=True
except ImportError:
			_A     :      int   					=False

_A     :      Union[str, Any]   					=logging.get_logger(__name__)  # pylint: disable=invalid-name





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Tuple:
							return AddNewModelCommand(args.testing    ,      args.testing_file    ,      path=args.path				)



class _lowercase     (    _lowercase		):




							@staticmethod
							def 		lowerCamelCase_    (							UpperCamelCase__:    ArgumentParser					):
														lowerCamelCase__ :     List[str]	  =	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=UpperCamelCase__      ,							help="""Configuration file on which to run."""					)
														add_new_model_parser.add_argument(
														    """--path"""      ,							type=UpperCamelCase__      ,							help="""Path to cookiecutter. Should only be used for testing purposes."""					)
														add_new_model_parser.set_defaults(func=UpperCamelCase__					)




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    bool      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    str=None      ,							*UpperCamelCase__:    Optional[int]					):
														lowerCamelCase__ :     List[Any]	  =	testing
														lowerCamelCase__ :     Tuple	  =	testing_file
														lowerCamelCase__ :     int	  =	path




							def 		lowerCamelCase_    (							self:    int					):
														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__ :     List[Any]	  =	[directory for directory in os.listdir() if """cookiecutter-template-""" == directory[:22]]
														if len(UpperCamelCase__					) > 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__ :     int	  =	(
														    Path(UpperCamelCase__					).parent.parent.parent.parent if self._path is None else Path(self._path					).parent.parent
														)
														lowerCamelCase__ :     int	  =	path_to_transformer_root / """templates""" / """adding_a_new_model"""

														# Execute cookiecutter
														if not self._testing:
																					cookiecutter(str(UpperCamelCase__					)					)
														else:
																					with open(self._testing_file      ,							"""r"""					) as configuration_file:
																												lowerCamelCase__ :     List[str]	  =	json.load(UpperCamelCase__					)

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

														lowerCamelCase__ :     Optional[Any]	  =	[directory for directory in os.listdir() if """cookiecutter-template-""" in directory[:22]][0]

														# Retrieve configuration
														with open(directory + """/configuration.json"""      ,							"""r"""					) as configuration_file:
																					lowerCamelCase__ :     int	  =	json.load(UpperCamelCase__					)

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

														lowerCamelCase__ :     Union[str, Any]	  =	"""PyTorch""" in generate_tensorflow_pytorch_and_flax
														lowerCamelCase__ :     Union[str, Any]	  =	"""TensorFlow""" in generate_tensorflow_pytorch_and_flax
														lowerCamelCase__ :     Tuple	  =	"""Flax""" in generate_tensorflow_pytorch_and_flax

														lowerCamelCase__ :     List[str]	  =	F'''{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'''
														os.makedirs(UpperCamelCase__      ,							exist_ok=UpperCamelCase__					)
														os.makedirs(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}'''      ,							exist_ok=UpperCamelCase__					)

														# 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__:    Optional[int]					):
																					with open(UpperCamelCase__      ,							"""r"""					) as f:
																												lowerCamelCase__ :     Union[str, Any]	  =	f.readlines()
																					with open(UpperCamelCase__      ,							"""w"""					) as f:
																												for line in lines:
																																			if "# Copied from transformers." not in line:
																																										f.write(UpperCamelCase__					)

														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__:    str      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    List[str]					):
																					# Create temp file
																					lowerCamelCase__  ,		lowerCamelCase__ :     Any	  =	mkstemp()
																					lowerCamelCase__ :     Tuple	  =	False
																					with fdopen(UpperCamelCase__      ,							"""w"""					) as new_file:
																												with open(UpperCamelCase__					) as old_file:
																																			for line in old_file:
																																										new_file.write(UpperCamelCase__					)
																																										if line_to_copy_below in line:
																																																	lowerCamelCase__ :     int	  =	True
																																																	for line_to_copy in lines_to_copy:
																																																								new_file.write(UpperCamelCase__					)

																					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(UpperCamelCase__      ,							UpperCamelCase__					)
																					# Remove original file
																					remove(UpperCamelCase__					)
																					# Move new file
																					move(UpperCamelCase__      ,							UpperCamelCase__					)

														def skip_units(UpperCamelCase__:    Optional[int]					):
																					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__:    List[str]					):
																					with open(UpperCamelCase__					) as datafile:
																												lowerCamelCase__ :     int	  =	[]
																												lowerCamelCase__ :     Tuple	  =	False
																												lowerCamelCase__ :     int	  =	False
																												for line in datafile:
																																			if "# To replace in: " in line and "##" not in line:
																																										lowerCamelCase__ :     List[str]	  =	line.split("""\""""					)[1]
																																										lowerCamelCase__ :     List[str]	  =	skip_units(UpperCamelCase__					)
																																			elif "# Below: " in line and "##" not in line:
																																										lowerCamelCase__ :     List[Any]	  =	line.split("""\""""					)[1]
																																										lowerCamelCase__ :     Union[str, Any]	  =	skip_units(UpperCamelCase__					)
																																			elif "# End." in line and "##" not in line:
																																										if not skip_file and not skip_snippet:
																																																	replace(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)

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

																					remove(UpperCamelCase__					)

														replace_in_files(F'''{directory}/to_replace_{lowercase_model_name}.py'''					)
														os.rmdir(UpperCamelCase__					)

