


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


UpperCamelCase			:					int         =							logging.get_logger(__name__)


if is_vision_available():
					import PIL

class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       Tuple								=			["pixel_values"]
					def __init__(						self       :						Optional[int]	,      _lowerCAmelCase       :						bool = True	,      _lowerCAmelCase       :						Dict[str, int] = None	,      _lowerCAmelCase       :						PILImageResampling = PILImageResampling.BICUBIC	,      _lowerCAmelCase       :						bool = True	,      _lowerCAmelCase       :						Dict[str, int] = None	,      _lowerCAmelCase       :						bool = True	,      _lowerCAmelCase       :						Union[int, float] = 1 / 2_5_5	,      _lowerCAmelCase       :						bool = True	,      _lowerCAmelCase       :						Optional[Union[float, List[float]]] = None	,      _lowerCAmelCase       :						Optional[Union[float, List[float]]] = None	,      _lowerCAmelCase       :						bool = True	,      **_lowerCAmelCase       :						Dict	,      )							->					None:



											'''simple docstring'''





											super().__init__(**_lowerCAmelCase  )
											__magic_name__					=      size if size is not None else {'shortest_edge': 2_2_4}
											__magic_name__					=      get_size_dict(_lowerCAmelCase	,      default_to_square=_lowerCAmelCase  )
											__magic_name__					=      crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
											__magic_name__					=      get_size_dict(_lowerCAmelCase  )

											__magic_name__					=      do_resize
											__magic_name__					=      size
											__magic_name__					=      resample
											__magic_name__					=      do_center_crop
											__magic_name__					=      crop_size
											__magic_name__					=      do_rescale
											__magic_name__					=      rescale_factor
											__magic_name__					=      do_normalize
											__magic_name__					=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											__magic_name__					=      image_std if image_std is not None else OPENAI_CLIP_STD
											__magic_name__					=      do_convert_rgb
					def 	a			(						self       :						Optional[int]	,      _lowerCAmelCase       :						np.ndarray	,      _lowerCAmelCase       :						Dict[str, int]	,      _lowerCAmelCase       :						PILImageResampling = PILImageResampling.BICUBIC	,      _lowerCAmelCase       :						Optional[Union[str, ChannelDimension]] = None	,      **_lowerCAmelCase       :						Dict	,      )							->					np.ndarray:



											'''simple docstring'''





											__magic_name__					=      get_size_dict(_lowerCAmelCase	,      default_to_square=_lowerCAmelCase  )
											__magic_name__					=      get_resize_output_image_size(
											    _lowerCAmelCase	,      size=(size['height'], size['width'])	,      default_to_square=_lowerCAmelCase  )
											return resize(_lowerCAmelCase	,      size=_lowerCAmelCase	,      resample=_lowerCAmelCase	,      data_format=_lowerCAmelCase	,      **_lowerCAmelCase  )
					def 	a			(						self       :						str	,      _lowerCAmelCase       :						np.ndarray	,      _lowerCAmelCase       :						Dict[str, int]	,      _lowerCAmelCase       :						Optional[Union[str, ChannelDimension]] = None	,      **_lowerCAmelCase       :						List[Any]	,      )							->					np.ndarray:



											'''simple docstring'''





											__magic_name__					=      get_size_dict(_lowerCAmelCase  )
											return center_crop(_lowerCAmelCase	,      size=(size['height'], size['width'])	,      data_format=_lowerCAmelCase	,      **_lowerCAmelCase  )
					def 	a			(						self       :						Optional[Any]	,      _lowerCAmelCase       :						np.ndarray	,      _lowerCAmelCase       :						Union[int, float]	,      _lowerCAmelCase       :						Optional[Union[str, ChannelDimension]] = None	,      **_lowerCAmelCase       :						str	,      )							->					str:



											'''simple docstring'''





											return rescale(_lowerCAmelCase	,      scale=_lowerCAmelCase	,      data_format=_lowerCAmelCase	,      **_lowerCAmelCase  )
					def 	a			(						self       :						Dict	,      _lowerCAmelCase       :						np.ndarray	,      _lowerCAmelCase       :						Union[float, List[float]]	,      _lowerCAmelCase       :						Union[float, List[float]]	,      _lowerCAmelCase       :						Optional[Union[str, ChannelDimension]] = None	,      **_lowerCAmelCase       :						Tuple	,      )							->					np.ndarray:



											'''simple docstring'''





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







					def 	a			(						self       :						List[str]	,      _lowerCAmelCase       :						ImageInput	,      _lowerCAmelCase       :						bool = None	,      _lowerCAmelCase       :						Dict[str, int] = None	,      _lowerCAmelCase       :						PILImageResampling = None	,      _lowerCAmelCase       :						bool = None	,      _lowerCAmelCase       :						int = None	,      _lowerCAmelCase       :						bool = None	,      _lowerCAmelCase       :						float = None	,      _lowerCAmelCase       :						bool = None	,      _lowerCAmelCase       :						Optional[Union[float, List[float]]] = None	,      _lowerCAmelCase       :						Optional[Union[float, List[float]]] = None	,      _lowerCAmelCase       :						bool = None	,      _lowerCAmelCase       :						Optional[Union[str, TensorType]] = None	,      _lowerCAmelCase       :						Optional[ChannelDimension] = ChannelDimension.FIRST	,      **_lowerCAmelCase       :						Optional[Any]	,      )							->					PIL.Image.Image:



											'''simple docstring'''





											__magic_name__					=      do_resize if do_resize is not None else self.do_resize
											__magic_name__					=      size if size is not None else self.size
											__magic_name__					=      get_size_dict(_lowerCAmelCase	,      default_to_square=_lowerCAmelCase  )
											__magic_name__					=      resample if resample is not None else self.resample
											__magic_name__					=      do_center_crop if do_center_crop is not None else self.do_center_crop
											__magic_name__					=      crop_size if crop_size is not None else self.crop_size
											__magic_name__					=      get_size_dict(_lowerCAmelCase  )
											__magic_name__					=      do_rescale if do_rescale is not None else self.do_rescale
											__magic_name__					=      rescale_factor if rescale_factor is not None else self.rescale_factor
											__magic_name__					=      do_normalize if do_normalize is not None else self.do_normalize
											__magic_name__					=      image_mean if image_mean is not None else self.image_mean
											__magic_name__					=      image_std if image_std is not None else self.image_std
											__magic_name__					=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											__magic_name__					=      make_list_of_images(_lowerCAmelCase  )

											if not valid_images(_lowerCAmelCase  ):
																	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:
																	__magic_name__					=      [convert_to_rgb(_lowerCAmelCase  ) for image in images]

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

											if do_resize:
																	__magic_name__					=      [self.resize(image=_lowerCAmelCase	,      size=_lowerCAmelCase	,      resample=_lowerCAmelCase  ) for image in images]

											if do_center_crop:
																	__magic_name__					=      [self.center_crop(image=_lowerCAmelCase	,      size=_lowerCAmelCase  ) for image in images]

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

											if do_normalize:
																	__magic_name__					=      [self.normalize(image=_lowerCAmelCase	,      mean=_lowerCAmelCase	,      std=_lowerCAmelCase  ) for image in images]

											__magic_name__					=      [to_channel_dimension_format(_lowerCAmelCase	,      _lowerCAmelCase  ) for image in images]

											__magic_name__					=      {'pixel_values': images}
											return BatchFeature(data=_lowerCAmelCase	,      tensor_type=_lowerCAmelCase  )





from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available
def 				UpperCamelCase_	(					__snake_case      :    Dict  )->   str:




						'''simple docstring'''
						if is_torch_available():
												import torch

												if isinstance(__snake_case						,      torch.Tensor  ):
																		return True

						return isinstance(__snake_case						,      np.ndarray  )




class      __snake_case				(   _UpperCAmelCase):
					def 	a			(						self       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      fields(self  )

											# Safety and consistency checks
											if not len(_lowerCAmelCase  ):
																	raise ValueError(F'''{self.__class__.__name__} has no fields.'''  )

											__magic_name__					=      getattr(self	,      class_fields[0].name  )
											__magic_name__					=      all(getattr(self	,      field.name  ) is None for field in class_fields[1:]  )

											if other_fields_are_none and isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	for key, value in first_field.items():
																							__magic_name__					=      value
											else:
																	for field in class_fields:
																							__magic_name__					=      getattr(self	,      field.name  )
																							if v is not None:
																													__magic_name__					=      v
					def __delitem__(						self       :						Dict	,      *_lowerCAmelCase       :						List[str]	,      **_lowerCAmelCase       :						List[str]  )							->					List[str]:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Optional[int]	,      *_lowerCAmelCase       :						Optional[Any]	,      **_lowerCAmelCase       :						Optional[Any]  )							->					str:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Union[str, Any]	,      *_lowerCAmelCase       :						List[str]	,      **_lowerCAmelCase       :						Any  )							->					str:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Any	,      *_lowerCAmelCase       :						str	,      **_lowerCAmelCase       :						Optional[int]  )							->					List[Any]:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''  )
					def __getitem__(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Any  )							->					Tuple:



											'''simple docstring'''





											if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	__magic_name__					=      dict(self.items()  )
																	return inner_dict[k]
											else:
																	return self.to_tuple()[k]
					def __setattr__(						self       :						str	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						Optional[int]  )							->					int:



											'''simple docstring'''





											if name in self.keys() and value is not None:
																	# Don't call self.__setitem__ to avoid recursion errors
																	super().__setitem__(_lowerCAmelCase	,      _lowerCAmelCase  )
											super().__setattr__(_lowerCAmelCase	,      _lowerCAmelCase  )
					def __setitem__(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						List[str]  )							->					Optional[int]:



											'''simple docstring'''





											super().__setitem__(_lowerCAmelCase	,      _lowerCAmelCase  )
											# Don't call self.__setattr__ to avoid recursion errors
											super().__setattr__(_lowerCAmelCase	,      _lowerCAmelCase  )







					def 	a			(						self       :						str  )							->					Tuple[Any]:



											'''simple docstring'''





											return tuple(self[k] for k in self.keys()  )

