




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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


_lowerCamelCase					:     Dict					  =  logging.get_logger(__name__)
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   ["pixel_values"]



				def __init__(					self					:						Any						,		_UpperCAmelCase					:						bool = True						,		_UpperCAmelCase					:						Dict[str, int] = None						,		_UpperCAmelCase					:						bool = True						,		_UpperCAmelCase					:						Dict[str, int] = None						,		_UpperCAmelCase					:						PILImageResampling = PILImageResampling.BICUBIC						,		_UpperCAmelCase					:						bool = True						,		_UpperCAmelCase					:						Union[int, float] = 1 / 255						,		_UpperCAmelCase					:						bool = True						,		_UpperCAmelCase					:						Optional[Union[float, List[float]]] = None						,		_UpperCAmelCase					:						Optional[Union[float, List[float]]] = None						,		**_UpperCAmelCase					:						int						,		)     ->			None:





								'''simple docstring'''




								super().__init__(**_UpperCAmelCase					)
								_lowerCAmelCase			:			Tuple        =					crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
								_lowerCAmelCase			:			List[str]        =					get_size_dict(_UpperCAmelCase						,		param_name="""crop_size"""					)
								_lowerCAmelCase			:			Any        =					size if size is not None else {"""height""": 224, """width""": 224}
								_lowerCAmelCase			:			Dict        =					get_size_dict(_UpperCAmelCase					)

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



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any						,		_UpperCAmelCase					:						np.ndarray						,		_UpperCAmelCase					:						Dict[str, int]						,		_UpperCAmelCase					:						Optional[int] = None						,		_UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None						,		**_UpperCAmelCase					:						Optional[int]						,		)     ->			np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					self.size if size is None else size
								_lowerCAmelCase			:			Tuple        =					get_size_dict(_UpperCAmelCase					)
								_lowerCAmelCase			:			str        =					get_size_dict(_UpperCAmelCase						,		param_name="""crop_size"""					)

								_lowerCAmelCase    ,			_lowerCAmelCase			:			Optional[Any]        =					get_image_size(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					min(_UpperCAmelCase						,		_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					(size["""height"""] / crop_size["""height"""]) * min_dim
								_lowerCAmelCase			:			Dict        =					(size["""width"""] / crop_size["""width"""]) * min_dim
								return center_crop(_UpperCAmelCase						,		size=(cropped_height, cropped_width)						,		data_format=_UpperCAmelCase						,		**_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						np.ndarray						,		_UpperCAmelCase					:						Dict[str, int]						,		_UpperCAmelCase					:						PILImageResampling = PIL.Image.BICUBIC						,		_UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None						,		**_UpperCAmelCase					:						Dict						,		)     ->			np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase			:			Tuple        =					get_size_dict(_UpperCAmelCase					)
								if "height" not in size or "width" not in size:
												raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"					)
								return resize(
								    _UpperCAmelCase						,		size=(size["""height"""], size["""width"""])						,		resample=_UpperCAmelCase						,		data_format=_UpperCAmelCase						,		**_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any						,		_UpperCAmelCase					:						np.ndarray						,		_UpperCAmelCase					:						Union[int, float]						,		_UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None						,		**_UpperCAmelCase					:						str						,		)     ->			Optional[int]:





								'''simple docstring'''




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



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any						,		_UpperCAmelCase					:						np.ndarray						,		_UpperCAmelCase					:						Union[float, List[float]]						,		_UpperCAmelCase					:						Union[float, List[float]]						,		_UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None						,		**_UpperCAmelCase					:						List[Any]						,		)     ->			np.ndarray:





								'''simple docstring'''




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



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





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					do_center_crop if do_center_crop is not None else self.do_center_crop
								_lowerCAmelCase			:			Any        =					crop_size if crop_size is not None else self.crop_size
								_lowerCAmelCase			:			Optional[Any]        =					get_size_dict(_UpperCAmelCase						,		param_name="""crop_size"""					)
								_lowerCAmelCase			:			Optional[int]        =					do_resize if do_resize is not None else self.do_resize
								_lowerCAmelCase			:			Optional[Any]        =					size if size is not None else self.size
								_lowerCAmelCase			:			Any        =					get_size_dict(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[Any]        =					resample if resample is not None else self.resample
								_lowerCAmelCase			:			List[Any]        =					do_rescale if do_rescale is not None else self.do_rescale
								_lowerCAmelCase			:			List[Any]        =					rescale_factor if rescale_factor is not None else self.rescale_factor
								_lowerCAmelCase			:			Any        =					do_normalize if do_normalize is not None else self.do_normalize
								_lowerCAmelCase			:			Union[str, Any]        =					image_mean if image_mean is not None else self.image_mean
								_lowerCAmelCase			:			Any        =					image_std if image_std is not None else self.image_std

								_lowerCAmelCase			:			Optional[int]        =					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_center_crop and crop_size is None:
												raise ValueError("""If `do_center_crop` is set to `True`, `crop_size` must be provided."""					)

								if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True."""					)

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

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

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

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

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

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

								_lowerCAmelCase			:			Any        =					{"""pixel_values""": images}
								return BatchFeature(data=_UpperCAmelCase						,		tensor_type=_UpperCAmelCase					)









"""simple docstring"""







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 					_lowerCAmelCase				(					lowerCAmelCase						):







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

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

       return isinstance(lowerCAmelCase		,					np.ndarray						)





class      UpperCamelCase_       (				a_     ):



  def 						UpperCamelCase_ (					self )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	fields(self )

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

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

         if other_fields_are_none and isinstance(snake_case__       ,						snake_case__ ):
                for key, value in first_field.items():
                       UpperCAmelCase					=	value
         else:
                for field in class_fields:
                       UpperCAmelCase					=	getattr(self       ,						field.name )
                       if v is not None:
                              UpperCAmelCase					=	v



  def __delitem__(					self       ,						*snake_case__       ,						**snake_case__ )	->	Optional[int]:



         """simple docstring"""





         raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )



  def 						UpperCamelCase_ (					self       ,						*snake_case__       ,						**snake_case__ )	->	List[str]:



         """simple docstring"""





         raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )



  def 						UpperCamelCase_ (					self       ,						*snake_case__       ,						**snake_case__ )	->	Optional[int]:



         """simple docstring"""





         raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )



  def 						UpperCamelCase_ (					self       ,						*snake_case__       ,						**snake_case__ )	->	Optional[int]:



         """simple docstring"""





         raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )



  def __getitem__(					self       ,						snake_case__ )	->	List[Any]:



         """simple docstring"""





         if isinstance(snake_case__       ,						snake_case__ ):
                UpperCAmelCase					=	dict(self.items() )
                return inner_dict[k]
         else:
                return self.to_tuple()[k]



  def __setattr__(					self       ,						snake_case__       ,						snake_case__ )	->	Union[str, Any]:



         """simple docstring"""





         if name in self.keys() and value is not None:
                # Don't call self.__setitem__ to avoid recursion errors
                super().__setitem__(snake_case__       ,						snake_case__ )
         super().__setattr__(snake_case__       ,						snake_case__ )



  def __setitem__(					self       ,						snake_case__       ,						snake_case__ )	->	Tuple:



         """simple docstring"""





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



  def 						UpperCamelCase_ (					self )	->	Tuple[Any]:



         """simple docstring"""





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