
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 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	def __init__(			self   :					Any				,A   :					Optional[int]				,A   :					Optional[int]=7				,A   :					Optional[Any]=3				,A   :					List[str]=18				,A   :					Any=30				,A   :					Tuple=4_00				,A   :					Union[str, Any]=True				,A   :					Optional[Any]=32				,A   :					Union[str, Any]=True				,):
					__A       =					parent
					__A       =					batch_size
					__A       =					num_channels
					__A       =					image_size
					__A       =					min_resolution
					__A       =					max_resolution
					__A       =					do_resize
					__A       =					size_divisor
					__A       =					do_rescale







	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					return {
					    "do_resize": self.do_resize,
					    "size_divisor": self.size_divisor,
					    "do_rescale": self.do_rescale,
					}





@require_torch
@require_vision
class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	GLPNImageProcessor if is_vision_available() else None






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					GLPNImageProcessingTester(self					)






	@property
	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					return self.image_processor_tester.prepare_image_processor_dict()






	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					self.assertTrue(hasattr(A				,"do_resize"					)					)
					self.assertTrue(hasattr(A				,"size_divisor"					)					)
					self.assertTrue(hasattr(A				,"resample"					)					)
					self.assertTrue(hasattr(A				,"do_rescale"					)					)






	def 							UpperCamelCase_    (			self   :					str					):
					pass






	def 							UpperCamelCase_    (			self   :					Dict					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random PIL images
					__A       =					prepare_image_inputs(self.image_processor_tester				,equal_resolution=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,Image.Image					)

					# Test not batched input (GLPNImageProcessor doesn't support batching)
					__A       =					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 							UpperCamelCase_    (			self   :					Optional[Any]					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random numpy tensors
					__A       =					prepare_image_inputs(self.image_processor_tester				,equal_resolution=A				,numpify=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,np.ndarray					)

					# Test not batched input (GLPNImageProcessor doesn't support batching)
					__A       =					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 							UpperCamelCase_    (			self   :					int					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random PyTorch tensors
					__A       =					prepare_image_inputs(self.image_processor_tester				,equal_resolution=A				,torchify=A					)
					for image in image_inputs:
									self.assertIsInstance(A				,torch.Tensor					)

					# Test not batched input (GLPNImageProcessor doesn't support batching)
					__A       =					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					)



'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCamelCase_    						=       logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    ['pixel_values']







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		Union[int, float] = 1 / 255		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		int = 8		,					**UpperCAmelCase__     :		str		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=do_rescale
										lowercase :							List[Any]    		=rescale_factor
										lowercase :							Tuple    		=do_pad
										lowercase :							List[str]    		=pad_size







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		float		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Optional[int]   ):
										'''simple docstring'''
										return rescale(UpperCAmelCase__		,					scale=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Union[str, Any]    		=get_image_size(UpperCAmelCase__   )
										lowercase :							Tuple    		=(old_height // size + 1) * size - old_height
										lowercase :							Tuple    		=(old_width // size + 1) * size - old_width

										return pad(UpperCAmelCase__		,					((0, pad_height), (0, pad_width))		,					mode='''symmetric'''		,					data_format=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		ImageInput		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[float] = None		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[int] = None		,					UpperCAmelCase__     :		Optional[Union[str, TensorType]] = None		,					UpperCAmelCase__     :		Union[str, ChannelDimension] = ChannelDimension.FIRST		,					**UpperCAmelCase__     :		Optional[Any]		,					):
										'''simple docstring'''
										lowercase :							int    		=do_rescale if do_rescale is not None else self.do_rescale
										lowercase :							Tuple    		=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase :							int    		=do_pad if do_pad is not None else self.do_pad
										lowercase :							List[Any]    		=pad_size if pad_size is not None else self.pad_size

										lowercase :							Any    		=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_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

										# All transformations expect numpy arrays.
										lowercase :							Dict    		=[to_numpy_array(UpperCAmelCase__   ) for image in images]

										if do_rescale:
																lowercase :							Tuple    		=[self.rescale(image=UpperCAmelCase__		,					scale=UpperCAmelCase__   ) for image in images]

										if do_pad:
																lowercase :							Union[str, Any]    		=[self.pad(UpperCAmelCase__		,					size=UpperCAmelCase__   ) for image in images]

										lowercase :							Dict    		=[to_channel_dimension_format(UpperCAmelCase__		,					UpperCAmelCase__   ) for image in images]

										lowercase :							Any    		={'''pixel_values''': images}
										return BatchFeature(data=UpperCAmelCase__		,					tensor_type=UpperCAmelCase__   )





