






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


UpperCAmelCase__			 =	logging.get_logger(__name__)




class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			["""pixel_values"""]



						def __init__(self:				Optional[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:				List[str]   ,				)						->    None:




											'''simple docstring'''



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

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



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




											'''simple docstring'''



											__a				:	Union[str, Any]			=				self.size if size is None else size
											__a				:	Union[str, Any]			=				get_size_dict(__UpperCAmelCase      )
											__a				:	List[Any]			=				get_size_dict(__UpperCAmelCase   ,				param_name="crop_size"      )

											__a				:	Any			=				get_image_size(__UpperCAmelCase      )
											__a				:	int			=				min(__UpperCAmelCase   ,				__UpperCAmelCase      )
											__a				:	List[Any]			=				(size["height"] / crop_size["height"]) * min_dim
											__a				:	int			=				(size["width"] / crop_size["width"]) * min_dim
											return center_crop(__UpperCAmelCase   ,				size=(cropped_height, cropped_width)   ,				data_format=__UpperCAmelCase   ,				**__UpperCAmelCase      )



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




											'''simple docstring'''



											__a				:	Dict			=				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 					UpperCAmelCase__				(self:				int   ,				__UpperCAmelCase:				np.ndarray   ,				__UpperCAmelCase:				Union[int, float]   ,				__UpperCAmelCase:				Optional[Union[str, ChannelDimension]] = None   ,				**__UpperCAmelCase:				Union[str, Any]   ,				)						->    Optional[int]:




											'''simple docstring'''



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



						def 					UpperCAmelCase__				(self:				str   ,				__UpperCAmelCase:				np.ndarray   ,				__UpperCAmelCase:				Union[float, List[float]]   ,				__UpperCAmelCase:				Union[float, List[float]]   ,				__UpperCAmelCase:				Optional[Union[str, ChannelDimension]] = None   ,				**__UpperCAmelCase:				Optional[int]   ,				)						->    np.ndarray:




											'''simple docstring'''



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



						def 					UpperCAmelCase__				(self:				str   ,				__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:				str   ,				)						->    PIL.Image.Image:




											'''simple docstring'''



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

											__a				:	List[str]			=				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.
											__a				:	str			=				[to_numpy_array(__UpperCAmelCase      ) for image in images]

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

											if do_resize:
																__a				:	Dict			=				[self.resize(image=__UpperCAmelCase   ,				size=__UpperCAmelCase   ,				resample=__UpperCAmelCase      ) for image in images]

											if do_rescale:
																__a				:	int			=				[self.rescale(image=__UpperCAmelCase   ,				scale=__UpperCAmelCase      ) for image in images]

											if do_normalize:
																__a				:	Optional[int]			=				[self.normalize(image=__UpperCAmelCase   ,				mean=__UpperCAmelCase   ,				std=__UpperCAmelCase      ) for image in images]

											__a				:	Any			=				[to_channel_dimension_format(__UpperCAmelCase   ,				__UpperCAmelCase      ) for image in images]

											__a				:	Dict			=				{"pixel_values": images}
											return BatchFeature(data=__UpperCAmelCase   ,				tensor_type=__UpperCAmelCase      )













import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput




@dataclass
class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			42
						snake_case__   					=			None
						snake_case__   					=			None




class        snake_case_					(						unittest.TestCase			):

						"""simple docstring"""



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Tuple:




											'''simple docstring'''



											__a				:	List[str]			=				ModelOutputTest(a=30      )
											self.assertEqual(x.a   ,				30      )
											self.assertIsNone(x.b      )
											self.assertIsNone(x.c      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Tuple			=				x.d



						def 					UpperCAmelCase__				(self:				Dict      )						->    Union[str, Any]:




											'''simple docstring'''



											__a				:	Union[str, Any]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(x[0]   ,				30      )
											self.assertEqual(x[1]   ,				10      )
											self.assertEqual(x[:2]   ,				(30, 10)      )
											self.assertEqual(x[:]   ,				(30, 10)      )

											__a				:	int			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(x[0]   ,				30      )
											self.assertEqual(x[1]   ,				10      )
											self.assertEqual(x[:2]   ,				(30, 10)      )
											self.assertEqual(x[:]   ,				(30, 10)      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    List[Any]:




											'''simple docstring'''



											__a				:	Optional[Any]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(x["a"]   ,				30      )
											self.assertEqual(x["b"]   ,				10      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	List[str]			=				x["c"]

											__a				:	Union[str, Any]			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(x["a"]   ,				30      )
											self.assertEqual(x["c"]   ,				10      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	int			=				x["b"]



						def 					UpperCAmelCase__				(self:				Union[str, Any]      )						->    Optional[int]:




											'''simple docstring'''



											__a				:	Union[str, Any]			=				ModelOutputTest(a=30      )
											self.assertEqual(list(x.keys()      )   ,				["a"]      )
											self.assertEqual(list(x.values()      )   ,				[30]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a"]      )

											__a				:	List[str]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(list(x.values()      )   ,				[30, 10]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30), ("b", 10)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a", "b"]      )

											__a				:	str			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(list(x.keys()      )   ,				["a", "c"]      )
											self.assertEqual(list(x.values()      )   ,				[30, 10]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30), ("c", 10)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a", "c"]      )

											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Dict			=				x.update({"d": 20}      )
											with self.assertRaises(__UpperCAmelCase      ):
																del x["a"]
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Any			=				x.pop("a"      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Any			=				x.setdefault("d"   ,				32      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Optional[Any]:




											'''simple docstring'''



											__a				:	Optional[Any]			=				ModelOutputTest(a=30      )
											__a				:	int			=				10
											self.assertEqual(x.a   ,				10      )
											self.assertEqual(x["a"]   ,				10      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Any:




											'''simple docstring'''



											__a				:	Optional[int]			=				ModelOutputTest(a=30      )
											__a				:	int			=				10
											self.assertEqual(x.a   ,				10      )
											self.assertEqual(x["a"]   ,				10      )



						def 					UpperCAmelCase__				(self:				List[str]      )						->    Any:




											'''simple docstring'''



											__a				:	Dict			=				ModelOutputTest({"a": 30, "b": 10}      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(x.a   ,				30      )
											self.assertEqual(x.b   ,				10      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    List[str]:




											'''simple docstring'''



											__a				:	List[Any]			=				ModelOutputTest([("a", 30), ("b", 10)]      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(x.a   ,				30      )
											self.assertEqual(x.b   ,				10      )

											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	List[Any]			=				ModelOutputTest([("a", 30), (10, 10)]      )

											__a				:	Optional[Any]			=				ModelOutputTest(a=(30, 30)      )
											self.assertEqual(list(x.keys()      )   ,				["a"]      )
											self.assertEqual(x.a   ,				(30, 30)      )





