"""simple docstring"""





from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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


lowerCAmelCase_     =	logging.get_logger(__name__)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						["pixel_values"]





				def __init__(      self							:							Union[str, Any]      ,    _A							:							bool = True      ,    _A							:							Dict[str, int] = None      ,    _A							:							PILImageResampling = PILImageResampling.BICUBIC      ,    _A							:							bool = True      ,    _A							:							Dict[str, int] = None      ,    _A							:							bool = True      ,    _A							:							Union[int, float] = 1 / 2_5_5      ,    _A							:							bool = True      ,    _A							:							Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN      ,    _A							:							Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD      ,    **_A							:							str      ,    ):



											"""simple docstring"""







											super().__init__(**_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    size if size is not None else {"""shortest_edge""": 2_2_4}
											_SCREAMING_SNAKE_CASE		:						Any			  =    get_size_dict(_A      ,    default_to_square=_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
											_SCREAMING_SNAKE_CASE		:						Dict			  =    get_size_dict(_A      ,    param_name="""crop_size""")

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    do_resize
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    size
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    resample
											_SCREAMING_SNAKE_CASE		:						str			  =    do_center_crop
											_SCREAMING_SNAKE_CASE		:						Dict			  =    crop_size
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    do_rescale
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    rescale_factor
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    do_normalize
											_SCREAMING_SNAKE_CASE		:						Any			  =    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    image_std if image_std is not None else IMAGENET_DEFAULT_STD





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							np.ndarray      ,    _A							:							Dict[str, int]      ,    _A							:							PILImageResampling = PILImageResampling.BICUBIC      ,    _A							:							Optional[Union[str, ChannelDimension]] = None      ,    **_A							:							int      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    get_size_dict(_A      ,    default_to_square=_A)
											# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
											if "shortest_edge" in size:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    int((2_5_6 / 2_2_4) * size["""shortest_edge"""])
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    get_resize_output_image_size(_A      ,    size=_A      ,    default_to_square=_A)
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {"""height""": output_size[0], """width""": output_size[1]}
											if "height" not in size_dict or "width" not in size_dict:
																		raise ValueError(
																		    f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""")
											return resize(
											    _A      ,    size=(size_dict["""height"""], size_dict["""width"""])      ,    resample=_A      ,    data_format=_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							np.ndarray      ,    _A							:							Dict[str, int]      ,    _A							:							Optional[Union[str, ChannelDimension]] = None      ,    **_A							:							Optional[int]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    get_size_dict(_A)
											if "height" not in size or "width" not in size:
																		raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""")
											return center_crop(_A      ,    size=(size["""height"""], size["""width"""])      ,    data_format=_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							np.ndarray      ,    _A							:							Union[int, float]      ,    _A							:							Optional[Union[str, ChannelDimension]] = None      ,    **_A							:							Optional[Any]      ,    ):



											"""simple docstring"""







											return rescale(_A      ,    scale=_A      ,    data_format=_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							np.ndarray      ,    _A							:							Union[float, List[float]]      ,    _A							:							Union[float, List[float]]      ,    _A							:							Optional[Union[str, ChannelDimension]] = None      ,    **_A							:							str      ,    ):



											"""simple docstring"""







											return normalize(_A      ,    mean=_A      ,    std=_A      ,    data_format=_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							ImageInput      ,    _A							:							Optional[bool] = None      ,    _A							:							Optional[Dict[str, int]] = None      ,    _A							:							PILImageResampling = None      ,    _A							:							Optional[bool] = None      ,    _A							:							Optional[Dict[str, int]] = None      ,    _A							:							Optional[bool] = None      ,    _A							:							Optional[float] = None      ,    _A							:							Optional[bool] = None      ,    _A							:							Optional[Union[float, Iterable[float]]] = None      ,    _A							:							Optional[Union[float, Iterable[float]]] = None      ,    _A							:							Optional[TensorType] = None      ,    _A							:							ChannelDimension = ChannelDimension.FIRST      ,    **_A							:							Tuple      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    do_resize if do_resize is not None else self.do_resize
											_SCREAMING_SNAKE_CASE		:						Dict			  =    resample if resample is not None else self.resample
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    do_center_crop if do_center_crop is not None else self.do_center_crop
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    do_rescale if do_rescale is not None else self.do_rescale
											_SCREAMING_SNAKE_CASE		:						int			  =    rescale_factor if rescale_factor is not None else self.rescale_factor
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    do_normalize if do_normalize is not None else self.do_normalize
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    image_mean if image_mean is not None else self.image_mean
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    image_std if image_std is not None else self.image_std

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    size if size is not None else self.size
											_SCREAMING_SNAKE_CASE		:						Dict			  =    get_size_dict(_A      ,    default_to_square=_A)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    crop_size if crop_size is not None else self.crop_size
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    get_size_dict(_A      ,    param_name="""crop_size""")

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    make_list_of_images(_A)

											if not valid_images(_A):
																		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.""")

											# All transformations expect numpy arrays.
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [to_numpy_array(_A) for image in images]

											if do_resize:
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    [self.resize(_A      ,    _A      ,    _A) for image in images]

											if do_center_crop:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    [self.center_crop(_A      ,    _A) for image in images]

											if do_rescale:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    [self.rescale(_A      ,    _A) for image in images]

											if do_normalize:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [self.normalize(_A      ,    _A      ,    _A) for image in images]

											_SCREAMING_SNAKE_CASE		:						Any			  =    [to_channel_dimension_format(_A      ,    _A) for image in images]

											_SCREAMING_SNAKE_CASE		:						Dict			  =    {"""pixel_values""": images}
											return BatchFeature(data=_A      ,    tensor_type=_A)



"""simple docstring"""





import argparse
from collections import defaultdict



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						str			  =    F"""{file}_{class_name}_{test_name}"""
							done_test[_id] += 1

							with open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    f.readlines()

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    F"""class {class_name}("""
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    F"""{4 * " "}def {test_name}("""
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    F"""{8 * " "}{correct_line.split()[0]}"""
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    F"""{16 * " "}{correct_line.split()[0]}"""
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    False
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    False
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    False
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    False
							_SCREAMING_SNAKE_CASE		:						Any			  =    0
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0

							_SCREAMING_SNAKE_CASE		:						Dict			  =    []
							for line in lines:
														if line.startswith(__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE		:						Any			  =    True
														elif in_class and line.startswith(__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE		:						str			  =    True
														elif in_class and in_func and (line.startswith(__SCREAMING_SNAKE_CASE  ) or line.startswith(__SCREAMING_SNAKE_CASE  )):
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    len(line.split(correct_line.split()[0]  )[0]  )
																					count += 1

																					if count == done_test[_id]:
																												_SCREAMING_SNAKE_CASE		:						int			  =    True

														if in_class and in_func and in_line:
																					if ")" not in line:
																												continue
																					else:
																												_SCREAMING_SNAKE_CASE		:						Any			  =    True

														if in_class and in_func and in_line and insert_line:
																					new_lines.append(F"""{spaces * " "}{correct_line}"""  )
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    False
														else:
																					new_lines.append(__SCREAMING_SNAKE_CASE  )

							with open(__SCREAMING_SNAKE_CASE     ,	"""w"""  ) as f:
														for line in new_lines:
																					f.write(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None  )->    Optional[Any]:
							if fail is not None:
														with open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ) as f:
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {l.strip() for l in f.readlines()}
							else:
														_SCREAMING_SNAKE_CASE		:						str			  =    None

							with open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						str			  =    f.readlines()

							_SCREAMING_SNAKE_CASE		:						str			  =    defaultdict(__SCREAMING_SNAKE_CASE  )
							for line in correct_lines:
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    line.split(""";"""  )
														if test_failures is None or "::".join([file, class_name, test_name]  ) in test_failures:
																					overwrite_file(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
			lowerCAmelCase_     =	argparse.ArgumentParser()
			parser.add_argument('''--correct_filename''', help='''filename of tests with expected result''')
			parser.add_argument('''--fail_filename''', help='''filename of test failures''', type=str, default=None)
			lowerCAmelCase_     =	parser.parse_args()

			main(args.correct_filename, args.fail_filename)


