

'''simple docstring'''

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			logging.get_logger(__name__)






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""pixel_values"""]



				def __init__(    self,     _lowercase = True,     _lowercase = 1 / 255,     _lowercase = True,     _lowercase = 8,     **_lowercase,     )     ->							None:
											super().__init__(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				do_rescale
											SCREAMING_SNAKE_CASE_				=				rescale_factor
											SCREAMING_SNAKE_CASE_				=				do_pad
											SCREAMING_SNAKE_CASE_				=				pad_size



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = None,     **_lowercase       )     ->							np.ndarray:
											return rescale(_lowercase,     scale=_lowercase,     data_format=_lowercase,     **_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = None       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				get_image_size(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				(old_height // size + 1) * size - old_height
											SCREAMING_SNAKE_CASE_				=				(old_width // size + 1) * size - old_width

											return pad(_lowercase,     ((0, pad_height), (0, pad_width)),     mode='symmetric',     data_format=_lowercase       )





				def   a__					(    self,     _lowercase,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = None,     _lowercase = ChannelDimension.FIRST,     **_lowercase,     )     ->							str:
											SCREAMING_SNAKE_CASE_				=				do_rescale if do_rescale is not None else self.do_rescale
											SCREAMING_SNAKE_CASE_				=				rescale_factor if rescale_factor is not None else self.rescale_factor
											SCREAMING_SNAKE_CASE_				=				do_pad if do_pad is not None else self.do_pad
											SCREAMING_SNAKE_CASE_				=				pad_size if pad_size is not None else self.pad_size

											SCREAMING_SNAKE_CASE_				=				make_list_of_images(_lowercase       )

											if not valid_images(_lowercase       ):
																		raise ValueError(
																		    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																		    'torch.Tensor, tf.Tensor or jax.ndarray.'       )

											if do_rescale and rescale_factor is None:
																		raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

											# All transformations expect numpy arrays.
											SCREAMING_SNAKE_CASE_				=				[to_numpy_array(_lowercase       ) for image in images]

											if do_rescale:
																		SCREAMING_SNAKE_CASE_				=				[self.rescale(image=_lowercase,     scale=_lowercase       ) for image in images]

											if do_pad:
																		SCREAMING_SNAKE_CASE_				=				[self.pad(_lowercase,     size=_lowercase       ) for image in images]

											SCREAMING_SNAKE_CASE_				=				[to_channel_dimension_format(_lowercase,     _lowercase       ) for image in images]

											SCREAMING_SNAKE_CASE_				=				{'pixel_values': images}
											return BatchFeature(data=_lowercase,     tensor_type=_lowercase       )








'''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

							SCREAMING_SNAKE_CASE							:				Dict     	=			True
except ImportError:
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			False

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




def 							_UpperCamelCase				(   lowerCAmelCase__:					Namespace   )      -> Optional[Any]:
							return AddNewModelCommand(args.testing		,args.testing_file		,path=args.path   )






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@staticmethod
				def   a__					(    _lowercase       )     ->							Tuple:
											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=_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,     _lowercase,     _lowercase,     _lowercase=None,     *_lowercase       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				testing
											SCREAMING_SNAKE_CASE_				=				testing_file
											SCREAMING_SNAKE_CASE_				=				path





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

											SCREAMING_SNAKE_CASE_				=				(
											    Path(_lowercase       ).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(_lowercase       )       )
											else:
																		with open(self._testing_file,     'r'       ) as configuration_file:
																									SCREAMING_SNAKE_CASE_				=				json.load(_lowercase       )

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

											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(_lowercase       )

											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(_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       ):
																		with open(_lowercase,     'r'       ) as f:
																									SCREAMING_SNAKE_CASE_				=				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,     _lowercase,     _lowercase       ):
																		# Create temp file
																		SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				mkstemp()
																		SCREAMING_SNAKE_CASE_				=				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:
																																														SCREAMING_SNAKE_CASE_				=				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       ):
																		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       ):
																		with open(_lowercase       ) 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(_lowercase       )
																																elif "# Below: " in line and "##" not in line:
																																							SCREAMING_SNAKE_CASE_				=				line.split('"'       )[1]
																																							SCREAMING_SNAKE_CASE_				=				skip_units(_lowercase       )
																																elif "# End." in line and "##" not in line:
																																							if not skip_file and not skip_snippet:
																																														replace(_lowercase,     _lowercase,     _lowercase       )

																																							SCREAMING_SNAKE_CASE_				=				[]
																																elif "# Replace with" in line and "##" not in line:
																																							SCREAMING_SNAKE_CASE_				=				[]
																																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       )





