





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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_    =				logging.get_logger(__name__)



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Dict       			=      ['''pixel_values''']






			def __init__(self					,							__magic_name__ = True					,							__magic_name__ = None					,							__magic_name__ = PILImageResampling.BILINEAR					,							__magic_name__ = True					,							__magic_name__ = None					,							__magic_name__ = True					,							__magic_name__ = 1 / 255					,							__magic_name__ = True					,							__magic_name__ = None					,							__magic_name__ = None					,							**__magic_name__					,							)     ->   None:


							'''simple docstring'''



							super().__init__(**__magic_name__    )
							snake_case_						:    Optional[Any]				=  size if size is not None else {'''shortest_edge''': 256}
							snake_case_						:    List[str]				=  get_size_dict(__magic_name__					,							default_to_square=__magic_name__    )
							snake_case_						:    int				=  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							snake_case_						:    Optional[Any]				=  get_size_dict(__magic_name__    )
							snake_case_						:    Tuple				=  do_resize
							snake_case_						:    Tuple				=  size
							snake_case_						:    Dict				=  resample
							snake_case_						:    Optional[Any]				=  do_center_crop
							snake_case_						:    Union[str, Any]				=  crop_size
							snake_case_						:    Any				=  do_rescale
							snake_case_						:    List[str]				=  rescale_factor
							snake_case_						:    Any				=  do_normalize
							snake_case_						:    List[str]				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							snake_case_						:    Optional[int]				=  image_std if image_std is not None else IMAGENET_STANDARD_STD






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__ = PILImageResampling.BICUBIC					,							__magic_name__ = None					,							**__magic_name__					,							)     ->   np.ndarray:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  get_size_dict(__magic_name__					,							default_to_square=__magic_name__    )
							if "shortest_edge" not in size:
											raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
							snake_case_						:    int				=  get_resize_output_image_size(__magic_name__					,							size=size['''shortest_edge''']					,							default_to_square=__magic_name__    )
							return resize(__magic_name__					,							size=__magic_name__					,							resample=__magic_name__					,							data_format=__magic_name__					,							**__magic_name__    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							**__magic_name__					,							)     ->   np.ndarray:


							'''simple docstring'''



							snake_case_						:    int				=  get_size_dict(__magic_name__    )
							return center_crop(__magic_name__					,							size=(size['''height'''], size['''width'''])					,							data_format=__magic_name__					,							**__magic_name__    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							**__magic_name__    )     ->   np.ndarray:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							**__magic_name__					,							)     ->   np.ndarray:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = ChannelDimension.FIRST					,							**__magic_name__					,							)     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    str				=  do_resize if do_resize is not None else self.do_resize
							snake_case_						:    Dict				=  size if size is not None else self.size
							snake_case_						:    Dict				=  get_size_dict(__magic_name__					,							default_to_square=__magic_name__    )
							snake_case_						:    List[Any]				=  resample if resample is not None else self.resample
							snake_case_						:    Union[str, Any]				=  do_center_crop if do_center_crop is not None else self.do_center_crop
							snake_case_						:    int				=  crop_size if crop_size is not None else self.crop_size
							snake_case_						:    Tuple				=  get_size_dict(__magic_name__    )
							snake_case_						:    Dict				=  do_rescale if do_rescale is not None else self.do_rescale
							snake_case_						:    Optional[int]				=  rescale_factor if rescale_factor is not None else self.rescale_factor
							snake_case_						:    Optional[Any]				=  do_normalize if do_normalize is not None else self.do_normalize
							snake_case_						:    List[str]				=  image_mean if image_mean is not None else self.image_mean
							snake_case_						:    str				=  image_std if image_std is not None else self.image_std

							snake_case_						:    int				=  make_list_of_images(__magic_name__    )

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

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

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

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

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

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

							snake_case_						:    Optional[int]				=  [to_channel_dimension_format(__magic_name__					,							__magic_name__    ) for image in images]

							snake_case_						:    List[Any]				=  {'''pixel_values''': images}
							return BatchFeature(data=__magic_name__					,							tensor_type=__magic_name__    )







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import GLPNImageProcessor



class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def __init__(self					,							__magic_name__					,							__magic_name__=7					,							__magic_name__=3					,							__magic_name__=18					,							__magic_name__=30					,							__magic_name__=400					,							__magic_name__=True					,							__magic_name__=32					,							__magic_name__=True					,							)     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Tuple				=  parent
							snake_case_						:    Union[str, Any]				=  batch_size
							snake_case_						:    Union[str, Any]				=  num_channels
							snake_case_						:    Optional[Any]				=  image_size
							snake_case_						:    int				=  min_resolution
							snake_case_						:    Any				=  max_resolution
							snake_case_						:    Tuple				=  do_resize
							snake_case_						:    str				=  size_divisor
							snake_case_						:    Optional[Any]				=  do_rescale






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}




@require_torch
@require_vision
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[Any]       			=      GLPNImageProcessor if is_vision_available() else None






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


							'''simple docstring'''



							snake_case_						:    str				=  GLPNImageProcessingTester(self    )






			@property
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_resize'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''size_divisor'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''resample'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_rescale'''    )    )






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							snake_case_						:    Union[str, Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							Image.Image    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Optional[int]				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    str				=  self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							snake_case_						:    List[str]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							numpify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							np.ndarray    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Any				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






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


							'''simple docstring'''



							snake_case_						:    Dict				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							snake_case_						:    Optional[Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							torchify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							torch.Tensor    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Dict				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )
