






'''simple docstring'''

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			,     __lowerCAmelCase			,     __lowerCAmelCase=7			,     __lowerCAmelCase=3			,     __lowerCAmelCase=18			,     __lowerCAmelCase=30			,     __lowerCAmelCase=4_00			,     __lowerCAmelCase=True			,     __lowerCAmelCase=32			,     __lowerCAmelCase=True			,     ):
       UpperCamelCase_				:   Optional[Any]					    =							parent
       UpperCamelCase_				:   List[str]					    =							batch_size
       UpperCamelCase_				:   Any					    =							num_channels
       UpperCamelCase_				:   Dict					    =							image_size
       UpperCamelCase_				:   int					    =							min_resolution
       UpperCamelCase_				:   Tuple					    =							max_resolution
       UpperCamelCase_				:   List[str]					    =							do_resize
       UpperCamelCase_				:   Union[str, Any]					    =							size_divisor
       UpperCamelCase_				:   List[Any]					    =							do_rescale





 def   _UpperCAmelCase       (							self ):
       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }






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






 """simple docstring"""





 __a			:						Optional[Any]      =						GLPNImageProcessor if is_vision_available() else None
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[str]					    =							GLPNImageProcessingTester(self )
 @property
 def   _UpperCAmelCase       (							self ):
       return self.image_processor_tester.prepare_image_processor_dict()
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(__lowerCAmelCase			,     """do_resize""" ) )
       self.assertTrue(hasattr(__lowerCAmelCase			,     """size_divisor""" ) )
       self.assertTrue(hasattr(__lowerCAmelCase			,     """resample""" ) )
       self.assertTrue(hasattr(__lowerCAmelCase			,     """do_rescale""" ) )
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       # Initialize image_processing
       UpperCamelCase_				:   List[str]					    =							self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       UpperCamelCase_				:   List[str]					    =							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=__lowerCAmelCase )
       for image in image_inputs:
             self.assertIsInstance(__lowerCAmelCase			,     Image.Image )

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

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

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










'''simple docstring'''

import pytest

import datasets


# Import fixture modules as plugins
UpperCamelCase							=["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]



def 							snake_case					(     a_							: List[str]    ,   a_							: Optional[Any]	)       ->				Union[str, Any]:





      """simple docstring"""

      for item in items:
            if any(marker in item.keywords for marker in ["""integration""", """unit"""]	):
                  continue
            item.add_marker(pytest.mark.unit	)



def 							snake_case					(     a_							: Optional[int]	)       ->				Optional[Any]:





      """simple docstring"""

      config.addinivalue_line("""markers"""    ,   """torchaudio_latest: mark test to run with torchaudio>=0.12"""	)



@pytest.fixture(autouse=a_	)
def 							snake_case					(     a_							: Any    ,   a_							: Optional[int]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							tmp_path_factory.getbasetemp() / """cache"""
      UpperCamelCase_				:   str					    =							test_hf_cache_home / """datasets"""
      UpperCamelCase_				:   Any					    =							test_hf_cache_home / """metrics"""
      UpperCamelCase_				:   List[Any]					    =							test_hf_cache_home / """modules"""
      monkeypatch.setattr("""datasets.config.HF_DATASETS_CACHE"""    ,   str(a_	)	)
      monkeypatch.setattr("""datasets.config.HF_METRICS_CACHE"""    ,   str(a_	)	)
      monkeypatch.setattr("""datasets.config.HF_MODULES_CACHE"""    ,   str(a_	)	)
      UpperCamelCase_				:   List[Any]					    =							test_hf_datasets_cache / """downloads"""
      monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH"""    ,   str(a_	)	)
      UpperCamelCase_				:   Any					    =							test_hf_datasets_cache / """downloads""" / """extracted"""
      monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""    ,   str(a_	)	)



@pytest.fixture(autouse=a_    ,   scope="""session"""	)
def 							snake_case					(     )       ->				Any:





      """simple docstring"""

      datasets.disable_progress_bar()



@pytest.fixture(autouse=a_	)
def 							snake_case					(     a_							: Optional[int]	)       ->				Union[str, Any]:





      """simple docstring"""

      monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"""    ,   a_	)



@pytest.fixture
def 							snake_case					(     a_							: int	)       ->				Union[str, Any]:





      """simple docstring"""

      monkeypatch.setattr("""sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"""    ,   a_	)






