





"""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        lowercase__     (       lowercase_      )       ->		List[Any]:




      """simple docstring"""

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



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  @staticmethod
  def  __SCREAMING_SNAKE_CASE					(		__a :							ArgumentParser						)		->					Optional[Any]:
        _UpperCamelCase   :						Dict    =							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 :							Tuple       , __a :							bool       , __a :							str       , __a :							Optional[Any]=None       , *__a :							List[Any]						)		->					Optional[Any]:
        _UpperCamelCase   :						List[str]    =							testing
        _UpperCamelCase   :						List[str]    =							testing_file
        _UpperCamelCase   :						Optional[Any]    =							path


  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					Dict:
        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
        _UpperCamelCase   :						int    =							[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."						)

        _UpperCamelCase   :						int    =							(
            Path(__a						).parent.parent.parent.parent if self._path is None else Path(self._path						).parent.parent
        )
        _UpperCamelCase   :						Optional[Any]    =							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:
                    _UpperCamelCase   :						List[Any]    =							json.load(__a						)

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

        _UpperCamelCase   :						List[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:
              _UpperCamelCase   :						List[Any]    =							json.load(__a						)

        _UpperCamelCase   :						str    =							configuration["lowercase_modelname"]
        _UpperCamelCase   :						List[Any]    =							configuration["generate_tensorflow_pytorch_and_flax"]
        os.remove(F'''{directory}/configuration.json'''						)

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

        _UpperCamelCase   :						int    =							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 :							Union[str, Any]						):
              with open(__a       , "r"						) as f:
                    _UpperCamelCase   :						int    =							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 :							str       , __a :							str       , __a :							List[str]						):
              # Create temp file
              _UpperCamelCase,	_UpperCamelCase   :						Optional[Any]    =							mkstemp()
              _UpperCamelCase   :						Tuple    =							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:
                                      _UpperCamelCase   :						int    =							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 :							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(__a :							int						):
              with open(__a						) as datafile:
                    _UpperCamelCase   :						Optional[int]    =							[]
                    _UpperCamelCase   :						Dict    =							False
                    _UpperCamelCase   :						Dict    =							False
                    for line in datafile:
                          if "# To replace in: " in line and "##" not in line:
                                _UpperCamelCase   :						str    =							line.split("\""						)[1]
                                _UpperCamelCase   :						List[Any]    =							skip_units(__a						)
                          elif "# Below: " in line and "##" not in line:
                                _UpperCamelCase   :						str    =							line.split("\""						)[1]
                                _UpperCamelCase   :						Optional[int]    =							skip_units(__a						)
                          elif "# End." in line and "##" not in line:
                                if not skip_file and not skip_snippet:
                                      replace(__a       , __a       , __a						)

                                _UpperCamelCase   :						Any    =							[]
                          elif "# Replace with" in line and "##" not in line:
                                _UpperCamelCase   :						Optional[Any]    =							[]
                          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						)







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__						= {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ResNetForImageClassification",
      "ResNetModel",
      "ResNetPreTrainedModel",
      "ResNetBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFResNetForImageClassification",
      "TFResNetModel",
      "TFResNetPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "FlaxResNetForImageClassification",
      "FlaxResNetModel",
      "FlaxResNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_resnet import (
        RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        ResNetBackbone,
        ResNetForImageClassification,
        ResNetModel,
        ResNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_resnet import (
        TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFResNetForImageClassification,
        TFResNetModel,
        TFResNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
  import sys

  lowerCamelCase__						= _LazyModule(__name__, globals()["__file__"], _import_structure)
