


import unittest

import numpy as np

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

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 DPTImageProcessor







class 		_snake_case  (      unittest.TestCase							):




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase=7					,  _lowerCamelCase=3					,  _lowerCamelCase=18					,  _lowerCamelCase=30					,  _lowerCamelCase=400					,  _lowerCamelCase=True					,  _lowerCamelCase=None					,  _lowerCamelCase=True					,  _lowerCamelCase=[0.5, 0.5, 0.5]					,  _lowerCamelCase=[0.5, 0.5, 0.5]					,  ):
       a						:Optional[int] 							=       size if size is not None else {'''height''': 18, '''width''': 18}
       a						:int 							=       parent
       a						:str 							=       batch_size
       a						:Optional[int] 							=       num_channels
       a						:int 							=       image_size
       a						:Tuple 							=       min_resolution
       a						:Dict 							=       max_resolution
       a						:Optional[Any] 							=       do_resize
       a						:Dict 							=       size
       a						:int 							=       do_normalize
       a						:Any 							=       image_mean
       a						:Optional[int] 							=       image_std




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }







@require_torch
@require_vision
class 		_snake_case  (      _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       DPTImageProcessor if is_vision_available() else None




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       DPTImageProcessingTester(self    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return self.image_processor_tester.prepare_image_processor_dict()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''image_mean'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''image_std'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_normalize'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_resize'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''size'''    )    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}    )

       a						:Dict 							=       self.image_processing_class.from_dict(self.image_processor_dict					,  size=42    )
       self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:Union[str, Any] 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       a						:Dict 							=       prepare_image_inputs(self.image_processor_tester					,  equal_resolution=_lowerCamelCase    )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase					,  Image.Image    )

       # Test not batched input
       a						:Tuple 							=       image_processing(image_inputs[0]					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_images.shape					,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )					,  )

       # Test batched
       a						:Union[str, Any] 							=       image_processing(_lowerCamelCase					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_images.shape					,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )					,  )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:Union[str, Any] 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       a						:Optional[int] 							=       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
       a						:Optional[Any] 							=       image_processing(image_inputs[0]					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_images.shape					,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )					,  )

       # Test batched
       a						:Any 							=       image_processing(_lowerCamelCase					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_images.shape					,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )					,  )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:Any 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       a						:Any 							=       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
       a						:Any 							=       image_processing(image_inputs[0]					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_images.shape					,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )					,  )

       # Test batched
       a						:Dict 							=       image_processing(_lowerCamelCase					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_images.shape					,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )					,  )









import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

snake_case				:    Union[str, Any]       				=     '''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
     pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
     pkgs_to_check_at_runtime.append('''importlib_metadata''')

for pkg in pkgs_to_check_at_runtime:
     if pkg in deps:
          if pkg == "tokenizers":
               # must be loaded here, or else tqdm check may fail
               from .utils import is_tokenizers_available

               if not is_tokenizers_available():
                    continue  # not required, check version only if installed

          require_version_core(deps[pkg])
     else:
          raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Optional[int]=None						):




      """simple docstring"""





      require_version(deps[pkg] ,  UpperCAmelCase_						)





