






"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A		:       Union[str, Any]  				=		logging.get_logger(__name__)





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				str							    =   ["pixel_values"]




					def __init__(				self     :						str    ,						A     :						bool = True    ,						A     :						Optional[Dict[str, int]] = None    ,						A     :						PILImageResampling = PILImageResampling.BICUBIC    ,						A     :						bool = True    ,						A     :						bool = True    ,						A     :						Union[int, float] = 1 / 2_55    ,						A     :						Dict[str, int] = None    ,						A     :						bool = True    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						**A     :						Dict    ,						)  ->  None:
							super().__init__(**A   )
							lowercase_ : Tuple							       =   size if size is not None else {'''height''': 2_24, '''width''': 2_24}
							lowercase_ : Dict							       =   get_size_dict(A   )
							lowercase_ : Union[str, Any]							       =   crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							lowercase_ : List[Any]							       =   get_size_dict(A    ,						default_to_square=A    ,						param_name='''crop_size'''   )

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




					def   A  (				self     :						str    ,						A     :						np.ndarray    ,						A     :						Dict[str, int]    ,						A     :						PILImageResampling = PILImageResampling.BILINEAR    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						List[str]    ,						)  ->  np.ndarray:
							lowercase_ : str							       =   get_size_dict(A   )

							if "shortest_edge" in size:
									lowercase_ : Dict							       =   get_resize_output_image_size(A    ,						size=size['''shortest_edge''']    ,						default_to_square=A   )
									# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
									lowercase_ : List[Any]							       =   (size['''height'''], size['''width'''])
							else:
									raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''   )
							return resize(A    ,						size=A    ,						resample=A    ,						data_format=A    ,						**A   )




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




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




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






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

							lowercase_ : Union[str, Any]							       =   size if size is not None else self.size
							lowercase_ : str							       =   get_size_dict(A   )

							if not is_batched(A   ):
									lowercase_ : List[Any]							       =   [images]

							if not valid_images(A   ):
									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.'''   )

							# All transformations expect numpy arrays.
							lowercase_ : Tuple							       =   [to_numpy_array(A   ) for image in images]

							if do_resize:
									lowercase_ : List[str]							       =   [self.resize(image=A    ,						size=A    ,						resample=A   ) for image in images]

							if do_center_crop:
									lowercase_ : List[Any]							       =   [self.center_crop(image=A    ,						size=A   ) for image in images]

							if do_rescale:
									lowercase_ : List[Any]							       =   [self.rescale(image=A    ,						scale=A   ) for image in images]

							if do_normalize:
									lowercase_ : Dict							       =   [self.normalize(image=A    ,						mean=A    ,						std=A   ) for image in images]

							lowercase_ : List[str]							       =   [to_channel_dimension_format(A    ,						A   ) for image in images]

							lowercase_ : Optional[Any]							       =   {'''pixel_values''': images}
							return BatchFeature(data=A    ,						tensor_type=A   )









import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
						import torch

						from transformers import AutoModelForImageClassification

if is_vision_available():
						from transformers import AutoImageProcessor


@require_torch
@require_vision
class  A		(  unittest.TestCase     ):



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								lowerCAmelCase_      			=  AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								model.to(UpperCamelCase__   )

								from datasets import load_dataset

								lowerCAmelCase_      			=  load_dataset('''nielsr/rvlcdip-demo'''   )

								lowerCAmelCase_      			=  dataset['''train'''][0]['''image'''].convert('''RGB'''   )

								lowerCAmelCase_      			=  image_processor(UpperCamelCase__,	return_tensors='''pt'''   ).to(UpperCamelCase__   )

								# forward pass
								with torch.no_grad():
														lowerCAmelCase_      			=  model(**UpperCamelCase__   )
														lowerCAmelCase_      			=  outputs.logits

								lowerCAmelCase_      			=  torch.Size((1, 16)   )
								self.assertEqual(logits.shape,	UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347],	device=UpperCamelCase__,	dtype=torch.float,	)
								self.assertTrue(torch.allclose(logits[0, :3],	UpperCamelCase__,	atol=1E-4   )   )




