



'''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


UpperCamelCase__:   Optional[Any]								=			logging.get_logger(__name__)


if is_vision_available():
						import PIL

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       ["""pixel_values"""]







	def __init__(						self			:     List[Any]		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = PILImageResampling.BICUBIC		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__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			:     bool = True		,			**__snake_case			:     List[str]		,			)			->		None:
								super().__init__(**__snake_case		)
								UpperCAmelCase :       Any 							=		size if size is not None else {'''shortest_edge''': 224}
								UpperCAmelCase :       int 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		)
								UpperCAmelCase :       List[Any] 							=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								UpperCAmelCase :       List[str] 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		,			param_name='''crop_size'''		)

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







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     PILImageResampling = PILImageResampling.BICUBIC		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     List[Any]		,			)			->		np.ndarray:
								UpperCAmelCase :       List[str] 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		)
								if "shortest_edge" not in size:
															raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
								UpperCAmelCase :       Union[str, Any] 							=		get_resize_output_image_size(__snake_case		,			size=size['''shortest_edge''']		,			default_to_square=__snake_case		)
								return resize(__snake_case		,			size=__snake_case		,			resample=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Tuple		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Union[str, Any]		,			)			->		np.ndarray:
								UpperCAmelCase :       Tuple 							=		get_size_dict(__snake_case		)
								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(__snake_case		,			size=(size['''height'''], size['''width'''])		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Any		,			__snake_case			:     np.ndarray		,			__snake_case			:     Union[int, float]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Optional[Any]		,			)			->		List[str]:
								return rescale(__snake_case		,			scale=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[Any]		,			__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			:     Dict		,			)			->		np.ndarray:
								return normalize(__snake_case		,			mean=__snake_case		,			std=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[str]		,			__snake_case			:     ImageInput		,			__snake_case			:     bool = None		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = None		,			__snake_case			:     bool = None		,			__snake_case			:     int = None		,			__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			:     bool = None		,			__snake_case			:     Optional[Union[str, TensorType]] = None		,			__snake_case			:     Optional[ChannelDimension] = ChannelDimension.FIRST		,			**__snake_case			:     Optional[int]		,			)			->		PIL.Image.Image:
								UpperCAmelCase :       Any 							=		do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase :       str 							=		size if size is not None else self.size
								UpperCAmelCase :       Dict 							=		get_size_dict(__snake_case		,			param_name='''size'''		,			default_to_square=__snake_case		)
								UpperCAmelCase :       List[str] 							=		resample if resample is not None else self.resample
								UpperCAmelCase :       Tuple 							=		do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase :       Optional[Any] 							=		crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase :       Any 							=		get_size_dict(__snake_case		,			param_name='''crop_size'''		,			default_to_square=__snake_case		)
								UpperCAmelCase :       List[Any] 							=		do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase :       Union[str, Any] 							=		rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase :       Tuple 							=		do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase :       Any 							=		image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase :       Union[str, Any] 							=		image_std if image_std is not None else self.image_std
								UpperCAmelCase :       Any 							=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								UpperCAmelCase :       List[str] 							=		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_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:
															UpperCAmelCase :       str 							=		[convert_to_rgb(__snake_case		) for image in images]

								# All transformations expect numpy arrays.
								UpperCAmelCase :       Union[str, Any] 							=		[to_numpy_array(__snake_case		) for image in images]

								if do_resize:
															UpperCAmelCase :       int 							=		[self.resize(image=__snake_case		,			size=__snake_case		,			resample=__snake_case		) for image in images]

								if do_center_crop:
															UpperCAmelCase :       Optional[Any] 							=		[self.center_crop(image=__snake_case		,			size=__snake_case		) for image in images]

								if do_rescale:
															UpperCAmelCase :       Optional[int] 							=		[self.rescale(image=__snake_case		,			scale=__snake_case		) for image in images]

								if do_normalize:
															UpperCAmelCase :       int 							=		[self.normalize(image=__snake_case		,			mean=__snake_case		,			std=__snake_case		) for image in images]

								UpperCAmelCase :       List[Any] 							=		[to_channel_dimension_format(__snake_case		,			__snake_case		) for image in images]

								UpperCAmelCase :       Tuple 							=		{'''pixel_values''': images}
								return BatchFeature(data=__snake_case		,			tensor_type=__snake_case		)










'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
						import jax.numpy as jnp

if is_tf_available():
						import tensorflow as tf

if is_torch_available():
						import torch

class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     Any		)			->		str:
								UpperCAmelCase :       Any 							=		{
								    '''task_specific_params''': {
								        '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
								        '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
								        '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
								    }
								}
								UpperCAmelCase :       int 							=		{
								    '''task_specific_params.summarization.length_penalty''': 1.0,
								    '''task_specific_params.summarization.max_length''': 128,
								    '''task_specific_params.summarization.min_length''': 12,
								    '''task_specific_params.summarization.num_beams''': 4,
								    '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
								    '''task_specific_params.summarization_cnn.max_length''': 142,
								    '''task_specific_params.summarization_cnn.min_length''': 56,
								    '''task_specific_params.summarization_cnn.num_beams''': 4,
								    '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
								    '''task_specific_params.summarization_xsum.max_length''': 62,
								    '''task_specific_params.summarization_xsum.min_length''': 11,
								    '''task_specific_params.summarization_xsum.num_beams''': 6,
								}

								self.assertEqual(flatten_dict(__snake_case		)		,			__snake_case		)







	def      A	(						self			:     int		)			->		str:
								UpperCAmelCase :       Any 							=		np.random.randn(3		,			4		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			x.transpose()		)		)

								UpperCAmelCase :       str 							=		np.random.randn(3		,			4		,			5		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			x.transpose((1, 2, 0)		)		)		)







	@require_torch
	def      A	(						self			:     str		)			->		Union[str, Any]:
								UpperCAmelCase :       Any 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       List[Any] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			transpose(__snake_case		).numpy()		)		)

								UpperCAmelCase :       Tuple 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       Any 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			transpose(__snake_case		,			axes=(1, 2, 0)		).numpy()		)		)







	@require_tf
	def      A	(						self			:     List[str]		)			->		Optional[Any]:
								UpperCAmelCase :       int 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Optional[int] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			transpose(__snake_case		).numpy()		)		)

								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       str 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			transpose(__snake_case		,			axes=(1, 2, 0)		).numpy()		)		)







	@require_flax
	def      A	(						self			:     Tuple		)			->		Any:
								UpperCAmelCase :       List[Any] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       List[str] 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			np.asarray(transpose(__snake_case		)		)		)		)

								UpperCAmelCase :       Dict 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       int 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			np.asarray(transpose(__snake_case		,			axes=(1, 2, 0)		)		)		)		)







	def      A	(						self			:     Optional[Any]		)			->		Any:
								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			np.reshape(__snake_case		,			(4, 3)		)		)		)

								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		,			5		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			np.reshape(__snake_case		,			(12, 5)		)		)		)







	@require_torch
	def      A	(						self			:     Union[str, Any]		)			->		int:
								UpperCAmelCase :       Dict 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Optional[Any] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			reshape(__snake_case		,			(4, 3)		).numpy()		)		)

								UpperCAmelCase :       List[str] 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       List[Any] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			reshape(__snake_case		,			(12, 5)		).numpy()		)		)







	@require_tf
	def      A	(						self			:     int		)			->		List[str]:
								UpperCAmelCase :       List[Any] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       List[str] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			reshape(__snake_case		,			(4, 3)		).numpy()		)		)

								UpperCAmelCase :       List[Any] 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       Optional[Any] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			reshape(__snake_case		,			(12, 5)		).numpy()		)		)







	@require_flax
	def      A	(						self			:     Any		)			->		Dict:
								UpperCAmelCase :       Tuple 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Union[str, Any] 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			np.asarray(reshape(__snake_case		,			(4, 3)		)		)		)		)

								UpperCAmelCase :       Any 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       Optional[Any] 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			np.asarray(reshape(__snake_case		,			(12, 5)		)		)		)		)







	def      A	(						self			:     List[Any]		)			->		List[Any]:
								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(1		,			3		,			4		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			np.squeeze(__snake_case		)		)		)

								UpperCAmelCase :       str 							=		np.random.randn(1		,			4		,			1		,			5		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			np.squeeze(__snake_case		,			axis=2		)		)		)







	@require_torch
	def      A	(						self			:     Optional[int]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[int] 							=		np.random.randn(1		,			3		,			4		)
								UpperCAmelCase :       List[str] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			squeeze(__snake_case		).numpy()		)		)

								UpperCAmelCase :       Any 							=		np.random.randn(1		,			4		,			1		,			5		)
								UpperCAmelCase :       str 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			squeeze(__snake_case		,			axis=2		).numpy()		)		)







	@require_tf
	def      A	(						self			:     Optional[Any]		)			->		Dict:
								UpperCAmelCase :       int 							=		np.random.randn(1		,			3		,			4		)
								UpperCAmelCase :       Optional[int] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			squeeze(__snake_case		).numpy()		)		)

								UpperCAmelCase :       List[str] 							=		np.random.randn(1		,			4		,			1		,			5		)
								UpperCAmelCase :       Optional[int] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			squeeze(__snake_case		,			axis=2		).numpy()		)		)







	@require_flax
	def      A	(						self			:     List[Any]		)			->		Dict:
								UpperCAmelCase :       Optional[int] 							=		np.random.randn(1		,			3		,			4		)
								UpperCAmelCase :       int 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			np.asarray(squeeze(__snake_case		)		)		)		)

								UpperCAmelCase :       str 							=		np.random.randn(1		,			4		,			1		,			5		)
								UpperCAmelCase :       int 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			np.asarray(squeeze(__snake_case		,			axis=2		)		)		)		)







	def      A	(						self			:     Optional[Any]		)			->		int:
								UpperCAmelCase :       Optional[Any] 							=		np.random.randn(3		,			4		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			np.expand_dims(__snake_case		,			axis=1		)		)		)







	@require_torch
	def      A	(						self			:     List[str]		)			->		Tuple:
								UpperCAmelCase :       Tuple 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Tuple 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			expand_dims(__snake_case		,			axis=1		).numpy()		)		)







	@require_tf
	def      A	(						self			:     List[str]		)			->		Union[str, Any]:
								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Any 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			expand_dims(__snake_case		,			axis=1		).numpy()		)		)







	@require_flax
	def      A	(						self			:     Any		)			->		List[Any]:
								UpperCAmelCase :       List[str] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       str 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			np.asarray(expand_dims(__snake_case		,			axis=1		)		)		)		)





