

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 SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Tuple=7	,							lowerCAmelCase			:   List[Any]=3	,							lowerCAmelCase			:   str=18	,							lowerCAmelCase			:   Optional[Any]=30	,							lowerCAmelCase			:   Dict=4_00	,							lowerCAmelCase			:   int=True	,							lowerCAmelCase			:   List[Any]=32	,							lowerCAmelCase			:   List[str]=True	,							)							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						parent
     __lowerCAmelCase  :      List[Any]    =						batch_size
     __lowerCAmelCase  :      Tuple    =						num_channels
     __lowerCAmelCase  :      Union[str, Any]    =						image_size
     __lowerCAmelCase  :      Any    =						min_resolution
     __lowerCAmelCase  :      Union[str, Any]    =						max_resolution
     __lowerCAmelCase  :      List[str]    =						do_resize
     __lowerCAmelCase  :      List[str]    =						size_divisor
     __lowerCAmelCase  :      List[Any]    =						do_rescale







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Dict:






     """simple docstring"""







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






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE     (  _UpperCAmelCase       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[Any]										=GLPNImageProcessor if is_vision_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						GLPNImageProcessingTester(self   )







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     int:






     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()







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






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(lowercase_	,							"""do_resize"""   )   )
     self.assertTrue(hasattr(lowercase_	,							"""size_divisor"""   )   )
     self.assertTrue(hasattr(lowercase_	,							"""resample"""   )   )
     self.assertTrue(hasattr(lowercase_	,							"""do_rescale"""   )   )







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






     """simple docstring"""







     pass







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     __lowerCAmelCase  :      Optional[int]    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowercase_   )
     for image in image_inputs:
        self.assertIsInstance(lowercase_	,							Image.Image   )

     # Test not batched input (GLPNImageProcessor doesn't support batching)
     __lowerCAmelCase  :      str    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Optional[int]:






     """simple docstring"""







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

     # Test not batched input (GLPNImageProcessor doesn't support batching)
     __lowerCAmelCase  :      str    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Dict:






     """simple docstring"""







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

     # Test not batched input (GLPNImageProcessor doesn't support batching)
     __lowerCAmelCase  :      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   )





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   Optional[Any]	,							*lowerCAmelCase			:   int	,							**lowerCAmelCase			:   Optional[int]   )							->     None:






     """simple docstring"""







     warnings.warn(
         """The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use GLPNImageProcessor instead."""	,							lowerCAmelCase	,							)
     super().__init__(*lowerCAmelCase	,							**lowerCAmelCase   )


