

'''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

 lowerCAmelCase__      						=			True
except ImportError:
 lowerCAmelCase__      						=			False

lowerCAmelCase__      						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def 	_A  (					A__   ):






 """simple docstring"""



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






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  @staticmethod
  def       SCREAMING_SNAKE_CASE   (   lowercase__     :  ArgumentParser    ):
   __lowercase													=				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=lowercase__      ,help='''Configuration file on which to run.'''    )
   add_new_model_parser.add_argument(
       '''--path'''      ,type=lowercase__      ,help='''Path to cookiecutter. Should only be used for testing purposes.'''    )
   add_new_model_parser.set_defaults(func=lowercase__    )






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






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   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
   __lowercase													=				[directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:2_2]]
   if len(lowercase__    ) > 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.'''    )

   __lowercase													=				(
       Path(lowercase__    ).parent.parent.parent.parent if self._path is None else Path(self._path    ).parent.parent
   )
   __lowercase													=				path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

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

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

   __lowercase													=				[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:
    __lowercase													=				json.load(lowercase__    )

   __lowercase													=				configuration['''lowercase_modelname''']
   __lowercase													=				configuration['''generate_tensorflow_pytorch_and_flax''']
   os.remove(F"{directory}/configuration.json"    )

   __lowercase													=				'''PyTorch''' in generate_tensorflow_pytorch_and_flax
   __lowercase													=				'''TensorFlow''' in generate_tensorflow_pytorch_and_flax
   __lowercase													=				'''Flax''' in generate_tensorflow_pytorch_and_flax

   __lowercase													=				F"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
   os.makedirs(lowercase__      ,exist_ok=lowercase__    )
   os.makedirs(F"{path_to_transformer_root}/tests/models/{lowercase_model_name}"      ,exist_ok=lowercase__    )

   # 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(lowercase__     :  Optional[int]    ):
    with open(lowercase__      ,'''r'''    ) as f:
     __lowercase													=				f.readlines()
    with open(lowercase__      ,'''w'''    ) as f:
     for line in lines:
      if "# Copied from transformers." not in line:
       f.write(lowercase__    )

   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(lowercase__     :  str      ,lowercase__     :  str      ,lowercase__     :  List[str]    ):
    # Create temp file
    __lowercase						,			__lowercase													=				mkstemp()
    __lowercase													=				False
    with fdopen(lowercase__      ,'''w'''    ) as new_file:
     with open(lowercase__    ) as old_file:
      for line in old_file:
       new_file.write(lowercase__    )
       if line_to_copy_below in line:
        __lowercase													=				True
        for line_to_copy in lines_to_copy:
         new_file.write(lowercase__    )

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

   def skip_units(lowercase__     :  Union[str, Any]    ):
    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(lowercase__     :  Any    ):
    with open(lowercase__    ) as datafile:
     __lowercase													=				[]
     __lowercase													=				False
     __lowercase													=				False
     for line in datafile:
      if "# To replace in: " in line and "##" not in line:
       __lowercase													=				line.split('''"'''    )[1]
       __lowercase													=				skip_units(lowercase__    )
      elif "# Below: " in line and "##" not in line:
       __lowercase													=				line.split('''"'''    )[1]
       __lowercase													=				skip_units(lowercase__    )
      elif "# End." in line and "##" not in line:
       if not skip_file and not skip_snippet:
        replace(lowercase__      ,lowercase__      ,lowercase__    )

       __lowercase													=				[]
      elif "# Replace with" in line and "##" not in line:
       __lowercase													=				[]
      elif "##" not in line:
       lines_to_copy.append(lowercase__    )

    remove(lowercase__    )

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




'''simple docstring'''






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				TaConfig.from_json_file(A__   )
 print(F"Building PyTorch model from configuration: {config}"   )
 __lowercase													=				TaForConditionalGeneration(A__   )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(A__  ,       A__  ,       A__   )

 # Save pytorch-model
 print(F"Save PyTorch model to {pytorch_dump_path}"   )
 model.save_pretrained(A__   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

