
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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowerCamelCase_  :       int					      =     logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  Optional[int]    					=       ["""pixel_values"""]

			def __init__(				self       :				int  ,		snake_case_       :				bool = True  ,		snake_case_       :				Dict[str, int] = None  ,		snake_case_       :				PILImageResampling = PILImageResampling.BILINEAR  ,		snake_case_       :				bool = True  ,		snake_case_       :				bool = False  ,		snake_case_       :				bool = True  ,		snake_case_       :				bool = True  ,		snake_case_       :				Union[int, float] = 1 / 255  ,		snake_case_       :				bool = True  ,		snake_case_       :				Optional[Union[float, List[float]]] = None  ,		snake_case_       :				Optional[Union[float, List[float]]] = None  ,		**snake_case_       :				List[Any]  ,		):
							super().__init__(**snake_case_					)

							UpperCamelCase_:							Tuple		=  size if size is not None else {"""height""": 2560, """width""": 1920}
							if isinstance(snake_case_  ,		(tuple, list)					):
											# The previous feature extractor size parameter was in (width, height) format
											UpperCamelCase_:							List[str]		=  size[::-1]
							UpperCamelCase_:							Union[str, Any]		=  get_size_dict(snake_case_					)

							UpperCamelCase_:							Optional[Any]		=  do_resize
							UpperCamelCase_:							List[Any]		=  size
							UpperCamelCase_:							int		=  resample
							UpperCamelCase_:							List[str]		=  do_thumbnail
							UpperCamelCase_:							List[str]		=  do_align_long_axis
							UpperCamelCase_:							Dict		=  do_pad
							UpperCamelCase_:							List[str]		=  do_rescale
							UpperCamelCase_:							Any		=  rescale_factor
							UpperCamelCase_:							Tuple		=  do_normalize
							UpperCamelCase_:							Union[str, Any]		=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase_:							Optional[Any]		=  image_std if image_std is not None else IMAGENET_STANDARD_STD

			def 					lowerCAmelCase__		(				self       :				Dict  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Dict[str, int]  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None					):
							UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  get_image_size(snake_case_					)
							UpperCamelCase_,      UpperCamelCase_:							Tuple		=  size["""height"""], size["""width"""]

							if (output_width < output_height and input_width > input_height) or (
							    output_width > output_height and input_width < input_height
							):
											UpperCamelCase_:							Optional[Any]		=  np.rotaa(snake_case_  ,		3					)

							if data_format is not None:
											UpperCamelCase_:							List[Any]		=  to_channel_dimension_format(snake_case_  ,		snake_case_					)

							return image

			def 					lowerCAmelCase__		(				self       :				str  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Dict[str, int]  ,		snake_case_       :				bool = False  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None  ,		):
							UpperCamelCase_,      UpperCamelCase_:							List[str]		=  size["""height"""], size["""width"""]
							UpperCamelCase_,      UpperCamelCase_:							List[Any]		=  get_image_size(snake_case_					)

							UpperCamelCase_:							Any		=  output_width - input_width
							UpperCamelCase_:							Any		=  output_height - input_height

							if random_padding:
											UpperCamelCase_:							List[str]		=  np.random.randint(low=0  ,		high=delta_height + 1					)
											UpperCamelCase_:							Optional[Any]		=  np.random.randint(low=0  ,		high=delta_width + 1					)
							else:
											UpperCamelCase_:							Tuple		=  delta_height // 2
											UpperCamelCase_:							Optional[int]		=  delta_width // 2

							UpperCamelCase_:							List[Any]		=  delta_height - pad_top
							UpperCamelCase_:							List[str]		=  delta_width - pad_left

							UpperCamelCase_:							int		=  ((pad_top, pad_bottom), (pad_left, pad_right))
							return pad(snake_case_  ,		snake_case_  ,		data_format=snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		*snake_case_       :				str  ,		**snake_case_       :				Union[str, Any]					):
							logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""					)
							return self.pad_image(*snake_case_  ,		**snake_case_					)

			def 					lowerCAmelCase__		(				self       :				List[Any]  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Dict[str, int]  ,		snake_case_       :				PILImageResampling = PILImageResampling.BICUBIC  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_       :				Tuple  ,		):
							UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  get_image_size(snake_case_					)
							UpperCamelCase_,      UpperCamelCase_:							List[Any]		=  size["""height"""], size["""width"""]

							# We always resize to the smallest of either the input or output size.
							UpperCamelCase_:							int		=  min(snake_case_  ,		snake_case_					)
							UpperCamelCase_:							str		=  min(snake_case_  ,		snake_case_					)

							if height == input_height and width == input_width:
											return image

							if input_height > input_width:
											UpperCamelCase_:							Dict		=  int(input_width * height / input_height					)
							elif input_width > input_height:
											UpperCamelCase_:							Optional[Any]		=  int(input_height * width / input_width					)

							return resize(
							    snake_case_  ,		size=(height, width)  ,		resample=snake_case_  ,		reducing_gap=2.0  ,		data_format=snake_case_  ,		**snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Any  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Dict[str, int]  ,		snake_case_       :				PILImageResampling = PILImageResampling.BICUBIC  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_       :				str  ,		):
							UpperCamelCase_:							List[Any]		=  get_size_dict(snake_case_					)
							UpperCamelCase_:							List[Any]		=  min(size["""height"""]  ,		size["""width"""]					)
							UpperCamelCase_:							Tuple		=  get_resize_output_image_size(snake_case_  ,		size=snake_case_  ,		default_to_square=snake_case_					)
							UpperCamelCase_:							str		=  resize(snake_case_  ,		size=snake_case_  ,		resample=snake_case_  ,		data_format=snake_case_  ,		**snake_case_					)
							return resized_image

			def 					lowerCAmelCase__		(				self       :				Any  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Union[int, float]  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_       :				Optional[Any]  ,		):
							return rescale(snake_case_  ,		scale=snake_case_  ,		data_format=snake_case_  ,		**snake_case_					)

			def 					lowerCAmelCase__		(				self       :				int  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Union[float, List[float]]  ,		snake_case_       :				Union[float, List[float]]  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_       :				List[str]  ,		):
							return normalize(snake_case_  ,		mean=snake_case_  ,		std=snake_case_  ,		data_format=snake_case_  ,		**snake_case_					)




			def 					lowerCAmelCase__		(				self       :				Dict  ,		snake_case_       :				ImageInput  ,		snake_case_       :				bool = None  ,		snake_case_       :				Dict[str, int] = None  ,		snake_case_       :				PILImageResampling = None  ,		snake_case_       :				bool = None  ,		snake_case_       :				bool = None  ,		snake_case_       :				bool = None  ,		snake_case_       :				bool = False  ,		snake_case_       :				bool = None  ,		snake_case_       :				float = None  ,		snake_case_       :				bool = None  ,		snake_case_       :				Optional[Union[float, List[float]]] = None  ,		snake_case_       :				Optional[Union[float, List[float]]] = None  ,		snake_case_       :				Optional[Union[str, TensorType]] = None  ,		snake_case_       :				Optional[ChannelDimension] = ChannelDimension.FIRST  ,		**snake_case_       :				Optional[int]  ,		):
							UpperCamelCase_:							Optional[Any]		=  do_resize if do_resize is not None else self.do_resize
							UpperCamelCase_:							Dict		=  size if size is not None else self.size
							if isinstance(snake_case_  ,		(tuple, list)					):
											# Previous feature extractor had size in (width, height) format
											UpperCamelCase_:							Any		=  size[::-1]
							UpperCamelCase_:							Tuple		=  get_size_dict(snake_case_					)
							UpperCamelCase_:							Union[str, Any]		=  resample if resample is not None else self.resample
							UpperCamelCase_:							List[Any]		=  do_thumbnail if do_thumbnail is not None else self.do_thumbnail
							UpperCamelCase_:							Tuple		=  do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
							UpperCamelCase_:							List[Any]		=  do_pad if do_pad is not None else self.do_pad
							UpperCamelCase_:							Union[str, Any]		=  do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase_:							Optional[int]		=  rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase_:							Dict		=  do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase_:							str		=  image_mean if image_mean is not None else self.image_mean
							UpperCamelCase_:							List[Any]		=  image_std if image_std is not None else self.image_std

							UpperCamelCase_:							Any		=  make_list_of_images(snake_case_					)

							if not valid_images(snake_case_					):
											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_rescale and rescale_factor is None:
											raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

							if do_pad and size is None:
											raise ValueError("""Size must be specified if do_pad 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.
							UpperCamelCase_:							Union[str, Any]		=  [to_numpy_array(snake_case_					) for image in images]

							if do_align_long_axis:
											UpperCamelCase_:							Optional[Any]		=  [self.align_long_axis(snake_case_  ,		size=snake_case_					) for image in images]

							if do_resize:
											UpperCamelCase_:							Any		=  [self.resize(image=snake_case_  ,		size=snake_case_  ,		resample=snake_case_					) for image in images]

							if do_thumbnail:
											UpperCamelCase_:							Dict		=  [self.thumbnail(image=snake_case_  ,		size=snake_case_					) for image in images]

							if do_pad:
											UpperCamelCase_:							List[Any]		=  [self.pad_image(image=snake_case_  ,		size=snake_case_  ,		random_padding=snake_case_					) for image in images]

							if do_rescale:
											UpperCamelCase_:							List[str]		=  [self.rescale(image=snake_case_  ,		scale=snake_case_					) for image in images]

							if do_normalize:
											UpperCamelCase_:							str		=  [self.normalize(image=snake_case_  ,		mean=snake_case_  ,		std=snake_case_					) for image in images]

							UpperCamelCase_:							Union[str, Any]		=  [to_channel_dimension_format(snake_case_  ,		snake_case_					) for image in images]

							UpperCamelCase_:							Any		=  {"""pixel_values""": images}
							return BatchFeature(data=snake_case_  ,		tensor_type=snake_case_					)













'''simple docstring'''







# Lint as: python3

import hashlib
from typing import Union





def    _lowerCAmelCase						(						lowercase	)					->					bytes:
			if isinstance(lowercase					,	lowercase	):
						# Data already in bytes, returns as it as
						return hash_data
			elif isinstance(lowercase					,	lowercase	):
						# We keep the data as it as for it ot be later encoded to UTF-8
						# However replace `\\` with `/` for Windows compatibility
						__lowerCAmelCase											=				hash_data.replace("""\\"""					,	"""/"""	)
			elif isinstance(lowercase					,	lowercase	):
						__lowerCAmelCase											=				str(lowercase	)
			else:
						# If data is not of the required type, raise error
						raise InvalidKeyError(lowercase	)

			return hash_data.encode("""utf-8"""	)





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		def __init__(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				"""\nFAILURE TO GENERATE DATASET: Invalid key type detected"""
					__lowerCAmelCase											=				f'\nFound Key {hash_data} of type {type(__SCREAMING_SNAKE_CASE			)}'
					__lowerCAmelCase											=				"""\nKeys should be either str, int or bytes type"""
					super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'			)





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		def __init__(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=""			):



					'''simple docstring'''

					__lowerCAmelCase											=				key
					__lowerCAmelCase											=				duplicate_key_indices
					__lowerCAmelCase											=				fix_msg
					__lowerCAmelCase											=				"""Found multiple examples generated with the same key"""
					if len(__SCREAMING_SNAKE_CASE			) <= 20:
								__lowerCAmelCase											=				f'\nThe examples at index {", ".join(__SCREAMING_SNAKE_CASE			)} have the key {key}'
					else:
								__lowerCAmelCase											=				f'\nThe examples at index {", ".join(duplicate_key_indices[:20]			)}... ({len(__SCREAMING_SNAKE_CASE			) - 20} more) have the key {key}'
					__lowerCAmelCase											=				"""\n""" + fix_msg if fix_msg else """"""
					super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'			)





class     _UpperCAmelCase							:
		def __init__(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				hashlib.mda(_as_bytes(__SCREAMING_SNAKE_CASE			)			)







		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				self._split_mda.copy()
					__lowerCAmelCase											=				_as_bytes(__SCREAMING_SNAKE_CASE			)
					mda.update(__SCREAMING_SNAKE_CASE			)
					# Convert to integer with hexadecimal conversion
					return int(mda.hexdigest(),16			)

