




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   A(						unittest.TestCase       ):



	'''simple docstring'''


	def __init__(    self :  Union[str, Any]						,							A_ :  Optional[Any]						,							A_ :  Union[str, Any]=7						,							A_ :  Optional[int]=3						,							A_ :  Tuple=18						,							A_ :  Dict=30						,							A_ :  int=400						,							A_ :  int=True						,							A_ :  Tuple=32						,							A_ :  str=True						,							)       ->     List[str]:

							"""simple docstring"""






							lowerCamelCase_							=   parent
							lowerCamelCase_							=   batch_size
							lowerCamelCase_							=   num_channels
							lowerCamelCase_							=   image_size
							lowerCamelCase_							=   min_resolution
							lowerCamelCase_							=   max_resolution
							lowerCamelCase_							=   do_resize
							lowerCamelCase_							=   size_divisor
							lowerCamelCase_							=   do_rescale





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

							"""simple docstring"""






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







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



	'''simple docstring'''


	UpperCamelCase						       =						GLPNImageProcessor if is_vision_available() else None

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

							"""simple docstring"""






							lowerCamelCase_							=   GLPNImageProcessingTester(self  )

	@property
	def     a__			(    self :  List[str]  )       ->     Optional[Any]:

							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()

	def     a__			(    self :  Dict  )       ->     Optional[Any]:

							"""simple docstring"""






							lowerCamelCase_							=   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     a__			(    self :  Union[str, Any]  )       ->     Optional[Any]:

							"""simple docstring"""






							pass

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

							"""simple docstring"""






							lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCamelCase_							=   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)
							lowerCamelCase_							=   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     a__			(    self :  List[str]  )       ->     Optional[Any]:

							"""simple docstring"""






							lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowerCamelCase_							=   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)
							lowerCamelCase_							=   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     a__			(    self :  Optional[Any]  )       ->     List[str]:

							"""simple docstring"""






							lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowerCamelCase_							=   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)
							lowerCamelCase_							=   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  )









from ..utils import DummyObject, requires_backends







class   A(						metaclass=UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''keras_nlp''']

 def __init__(    self :  Optional[int]						,							*A_ :  Any						,							**A_ :  Dict  )       ->     Optional[int]:

       """simple docstring"""






       requires_backends(self						,							['keras_nlp']  )



