

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

       UpperCamelCase__  :      int			=    True
except ImportError:
       UpperCamelCase__  :      Optional[Any]			=    False

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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Namespace       ):





    """simple docstring"""


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





class       __snake_case  (    _lowercase       ):

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

  def __init__(					self						,		_A						,		_A						,		_A=None						,		*_A):
      SCREAMING_SNAKE_CASE_									=						testing
      SCREAMING_SNAKE_CASE_									=						testing_file
      SCREAMING_SNAKE_CASE_									=						path


  def 						lowerCAmelCase__     (					self):
      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
      SCREAMING_SNAKE_CASE_									=						[directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
      if len(A_) > 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.')

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

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

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

      SCREAMING_SNAKE_CASE_									=						[directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]

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

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

      SCREAMING_SNAKE_CASE_									=						'PyTorch' in generate_tensorflow_pytorch_and_flax
      SCREAMING_SNAKE_CASE_									=						'TensorFlow' in generate_tensorflow_pytorch_and_flax
      SCREAMING_SNAKE_CASE_									=						'Flax' in generate_tensorflow_pytorch_and_flax

      SCREAMING_SNAKE_CASE_									=						f"""{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"""
      os.makedirs(A_						,		exist_ok=A_)
      os.makedirs(f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}"""						,		exist_ok=A_)

      # 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(_A):
          with open(A_						,		'r') as f:
              SCREAMING_SNAKE_CASE_									=						f.readlines()
          with open(A_						,		'w') as f:
              for line in lines:
                  if "# Copied from transformers." not in line:
                      f.write(A_)

      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(_A						,		_A						,		_A):
          # Create temp file
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						mkstemp()
          SCREAMING_SNAKE_CASE_									=						False
          with fdopen(A_						,		'w') as new_file:
              with open(A_) as old_file:
                  for line in old_file:
                      new_file.write(A_)
                      if line_to_copy_below in line:
                          SCREAMING_SNAKE_CASE_									=						True
                          for line_to_copy in lines_to_copy:
                              new_file.write(A_)

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

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

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

          remove(A_)

      replace_in_files(f"""{directory}/to_replace_{lowercase_model_name}.py""")
      os.rmdir(A_)




from typing import List

import numpy as np


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{key: len(_SCREAMING_SNAKE_CASE       ) for key, value in gen_kwargs.items() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    if len(set(lists_lengths.values()       )       ) > 1:
        raise RuntimeError(
            (
                'Sharding is ambiguous for this dataset: '
                + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                + '\n'.join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()       )
                + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
            )       )
    SCREAMING_SNAKE_CASE_									=						max(lists_lengths.values() ,   default=0       )
    return max(1 ,   _SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for group_idx in range(_SCREAMING_SNAKE_CASE       ):
        SCREAMING_SNAKE_CASE_									=						num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        SCREAMING_SNAKE_CASE_									=						shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        SCREAMING_SNAKE_CASE_									=						range(_SCREAMING_SNAKE_CASE ,   start + num_shards_to_add       )
        shards_indices_per_group.append(_SCREAMING_SNAKE_CASE       )
    return shards_indices_per_group


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						_number_of_shards_in_gen_kwargs(_SCREAMING_SNAKE_CASE       )
    if num_shards == 1:
        return [dict(_SCREAMING_SNAKE_CASE       )]
    else:
        SCREAMING_SNAKE_CASE_									=						_distribute_shards(num_shards=_SCREAMING_SNAKE_CASE ,   max_num_jobs=_SCREAMING_SNAKE_CASE       )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(_SCREAMING_SNAKE_CASE       )       )
        ]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[dict]       ):





    """simple docstring"""


    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key] ,   _SCREAMING_SNAKE_CASE       )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		np.random.Generator ,   _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{len(_SCREAMING_SNAKE_CASE       ) for value in gen_kwargs.values() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    SCREAMING_SNAKE_CASE_									=						{}
    for size in list_sizes:
        SCREAMING_SNAKE_CASE_									=						list(range(_SCREAMING_SNAKE_CASE       )       )
        rng.shuffle(indices_per_size[size]       )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    SCREAMING_SNAKE_CASE_									=						dict(_SCREAMING_SNAKE_CASE       )
    for key, value in shuffled_kwargs.items():
        if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ):
            SCREAMING_SNAKE_CASE_									=						[value[i] for i in indices_per_size[len(_SCREAMING_SNAKE_CASE       )]]
    return shuffled_kwargs


