"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A	     =					logging.get_logger(__name__)


if is_vision_available():
		import PIL




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					['''pixel_values''']







					def __init__(  self		,     _UpperCAmelCase = True		,     _UpperCAmelCase = None		,     _UpperCAmelCase = PILImageResampling.BICUBIC		,     _UpperCAmelCase = True		,     _UpperCAmelCase = None		,     _UpperCAmelCase = True		,     _UpperCAmelCase = 1 / 255		,     _UpperCAmelCase = True		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = True		,     **_UpperCAmelCase		,     ):
									super().__init__(**_UpperCAmelCase       )
									__a	:  Union[str, Any]         =					size if size is not None else {'''shortest_edge''': 224}
									__a	:  Union[str, Any]         =					get_size_dict(_UpperCAmelCase		,     default_to_square=_UpperCAmelCase       )
									__a	:  int         =					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
									__a	:  List[str]         =					get_size_dict(_UpperCAmelCase		,     default_to_square=_UpperCAmelCase		,     param_name='''crop_size'''       )

									__a	:  Dict         =					do_resize
									__a	:  Tuple         =					size
									__a	:  List[Any]         =					resample
									__a	:  str         =					do_center_crop
									__a	:  Dict         =					crop_size
									__a	:  Tuple         =					do_rescale
									__a	:  Optional[Any]         =					rescale_factor
									__a	:  Optional[Any]         =					do_normalize
									__a	:  Dict         =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									__a	:  Union[str, Any]         =					image_std if image_std is not None else OPENAI_CLIP_STD
									__a	:  Optional[Any]         =					do_convert_rgb







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = PILImageResampling.BICUBIC		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
									__a	:  Union[str, Any]         =					get_size_dict(_UpperCAmelCase		,     default_to_square=_UpperCAmelCase       )
									if "shortest_edge" not in size:
													raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""       )
									__a	:  Any         =					get_resize_output_image_size(_UpperCAmelCase		,     size=size['''shortest_edge''']		,     default_to_square=_UpperCAmelCase       )
									return resize(_UpperCAmelCase		,     size=_UpperCAmelCase		,     resample=_UpperCAmelCase		,     data_format=_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
									__a	:  Tuple         =					get_size_dict(_UpperCAmelCase       )
									if "height" not in size or "width" not in size:
													raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""       )
									return center_crop(_UpperCAmelCase		,     size=(size['''height'''], size['''width'''])		,     data_format=_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
									return rescale(_UpperCAmelCase		,     scale=_UpperCAmelCase		,     data_format=_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
									return normalize(_UpperCAmelCase		,     mean=_UpperCAmelCase		,     std=_UpperCAmelCase		,     data_format=_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = ChannelDimension.FIRST		,     **_UpperCAmelCase		,     ):
									__a	:  Optional[int]         =					do_resize if do_resize is not None else self.do_resize
									__a	:  Dict         =					size if size is not None else self.size
									__a	:  List[Any]         =					get_size_dict(_UpperCAmelCase		,     param_name='''size'''		,     default_to_square=_UpperCAmelCase       )
									__a	:  Tuple         =					resample if resample is not None else self.resample
									__a	:  Optional[int]         =					do_center_crop if do_center_crop is not None else self.do_center_crop
									__a	:  Optional[int]         =					crop_size if crop_size is not None else self.crop_size
									__a	:  List[Any]         =					get_size_dict(_UpperCAmelCase		,     param_name='''crop_size'''		,     default_to_square=_UpperCAmelCase       )
									__a	:  Dict         =					do_rescale if do_rescale is not None else self.do_rescale
									__a	:  Tuple         =					rescale_factor if rescale_factor is not None else self.rescale_factor
									__a	:  Optional[Any]         =					do_normalize if do_normalize is not None else self.do_normalize
									__a	:  Any         =					image_mean if image_mean is not None else self.image_mean
									__a	:  Any         =					image_std if image_std is not None else self.image_std
									__a	:  List[Any]         =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									__a	:  Dict         =					make_list_of_images(_UpperCAmelCase       )

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

									if do_resize and size is None:
													raise ValueError('''Size must be specified if do_resize is True.'''       )

									if do_center_crop and crop_size is None:
													raise ValueError('''Crop size must be specified if do_center_crop is True.'''       )

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

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('''Image mean and std must be specified if do_normalize is True.'''       )

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
													__a	:  int         =					[convert_to_rgb(_UpperCAmelCase       ) for image in images]

									# All transformations expect numpy arrays.
									__a	:  List[str]         =					[to_numpy_array(_UpperCAmelCase       ) for image in images]

									if do_resize:
													__a	:  Optional[int]         =					[self.resize(image=_UpperCAmelCase		,     size=_UpperCAmelCase		,     resample=_UpperCAmelCase       ) for image in images]

									if do_center_crop:
													__a	:  Optional[int]         =					[self.center_crop(image=_UpperCAmelCase		,     size=_UpperCAmelCase       ) for image in images]

									if do_rescale:
													__a	:  int         =					[self.rescale(image=_UpperCAmelCase		,     scale=_UpperCAmelCase       ) for image in images]

									if do_normalize:
													__a	:  List[str]         =					[self.normalize(image=_UpperCAmelCase		,     mean=_UpperCAmelCase		,     std=_UpperCAmelCase       ) for image in images]

									__a	:  Tuple         =					[to_channel_dimension_format(_UpperCAmelCase		,     _UpperCAmelCase       ) for image in images]

									__a	:  Tuple         =					{'''pixel_values''': images}
									return BatchFeature(data=_UpperCAmelCase		,     tensor_type=_UpperCAmelCase       )






"""simple docstring"""






import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A	     =					'''src/transformers'''
A	     =					'''docs/source/en/tasks'''

def       __A (     a_  :List[Any]				,    a_  :List[Any]				,    a_  :List[str])						->						List[str]:
				with open(a_				,    '''r'''				,    encoding='''utf-8'''				,    newline='''\n''') as f:
								__a	:  List[str]         =					f.readlines()
				# Find the start prompt.
				__a	:  Optional[Any]         =					0
				while not lines[start_index].startswith(a_):
								start_index += 1
				start_index += 1

				__a	:  int         =					start_index
				while not lines[end_index].startswith(a_):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]) <= 1:
								start_index += 1
				while len(lines[end_index]) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
A	     =					direct_transformers_import(TRANSFORMERS_PATH)

A	     =					{
    '''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    '''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    '''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    '''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    '''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    '''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    '''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    '''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    '''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    '''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    '''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    '''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    '''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    '''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A	     =					{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}

def       __A (     a_  :Optional[Any])						->						Any:
				__a	:  List[Any]         =					TASK_GUIDE_TO_MODELS[task_guide]
				__a	:  List[Any]         =					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_				,    set())
				__a	:  Any         =					{
				    code: name
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if (code in model_maping_names or code in special_model_types)
				}
				return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]) + "\n"

def       __A (     a_  :Optional[int]				,    a_  :Dict=False)						->						Any:
				__a    ,  __a    ,  __a    ,  __a	:  Any         =					_find_text_in_file(
				    filename=os.path.join(a_				,    a_)				,    start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''				,    end_prompt='''<!--End of the generated tip-->'''				,    )

				__a	:  Optional[Any]         =					get_model_list_for_task(a_)

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(a_				,    a_)				,    '''w'''				,    encoding='''utf-8'''				,    newline='''\n''') as f:
																f.writelines(lines[:start_index] + [new_list] + lines[end_index:])
								else:
												raise ValueError(
												    F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
												    ''' to fix this.''')


if __name__ == "__main__":
		A	     =					argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		A	     =					parser.parse_args()

		for task_guide in TASK_GUIDE_TO_MODELS.keys():
				check_model_list_for_task(task_guide, args.fix_and_overwrite)