


from ...processing_utils import ProcessorMixin

class 						A__							(		SCREAMING_SNAKE_CASE__  ):
						UpperCAmelCase             =						["image_processor", "feature_extractor"]
						UpperCAmelCase             =						"TvltImageProcessor"
						UpperCAmelCase             =						"TvltFeatureExtractor"




						def __init__(       self					:      Optional[int]				,    _a					:      Union[str, Any]				,    _a					:      Tuple  )							->     Any:


										"""simple docstring"""





										super().__init__(image_processor=snake_case__				,    feature_extractor=snake_case__  )

										_SCREAMING_SNAKE_CASE			  =image_processor
										_SCREAMING_SNAKE_CASE			  =feature_extractor




						def __call__(       self					:      int				,    _a					:      Tuple=None				,    _a					:      List[str]=None				,    _a					:      Optional[Any]=None				,    _a					:      int=None				,    _a					:      str=False				,    _a					:      Union[str, Any]=False				,    *_a					:      Union[str, Any]				,    **_a					:      str				,    )							->     Dict:


										"""simple docstring"""





										if images is None and audio is None:
														raise ValueError('''You need to specify either an `images` or `audio` input to process.'''  )

										_SCREAMING_SNAKE_CASE			  =None
										if images is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(snake_case__				,    mask_pixel=snake_case__				,    *snake_case__				,    **snake_case__  )
										if images_mixed is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(snake_case__				,    is_mixed=snake_case__				,    *snake_case__				,    **snake_case__  )
										if audio is not None:
														_SCREAMING_SNAKE_CASE			  =self.feature_extractor(
														    snake_case__				,    *snake_case__				,    sampling_rate=snake_case__				,    mask_audio=snake_case__				,    **snake_case__  )

										_SCREAMING_SNAKE_CASE			  ={}
										if audio is not None:
														output_dict.update(snake_case__  )
										if images is not None:
														output_dict.update(snake_case__  )
										if images_mixed_dict is not None:
														output_dict.update(snake_case__  )
										return output_dict




						@property
						def 							__UpperCamelCase				(       self					:      int  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processor.model_input_names
										_SCREAMING_SNAKE_CASE			  =self.feature_extractor.model_input_names
										return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names  )  )



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

							snake_case_						:			str													=							True
except ImportError:
							snake_case_						:			Optional[Any]													=							False

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







def 						lowerCamelCase(   a__):
				return AddNewModelCommand(args.testing	,args.testing_file	,path=args.path)


class 						A__							(		UpperCamelCase__  ):




						@staticmethod
						def 							__UpperCamelCase				(       _a					:      ArgumentParser  )							->     int:


										"""simple docstring"""





										_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					:      List[str]				,    _a					:      bool				,    _a					:      str				,    _a					:      Any=None				,    *_a					:      Union[str, Any]  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =testing
										_SCREAMING_SNAKE_CASE			  =testing_file
										_SCREAMING_SNAKE_CASE			  =path




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     int:


										"""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
										_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					:      int  ):
														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					:      str				,    _a					:      str				,    _a					:      List[str]  ):
														# 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					:      str  ):
														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					:      Any  ):
														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  )