






'''simple docstring'''





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


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






     def 				_A       (				self       :	Union[str, Any]      ):
       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  SCREAMING_SNAKE_CASE			(			__SCREAMING_SNAKE_CASE      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[Any]			=	DPTImageProcessor if is_vision_available() else None


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    DPTImageProcessingTester(self      )


     @property
     def 				_A       (				self       :	Dict      ):
       return self.image_processor_tester.prepare_image_processor_dict()


     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(_a					, "image_mean"      )      )
       self.assertTrue(hasattr(_a					, "image_std"      )      )
       self.assertTrue(hasattr(_a					, "do_normalize"      )      )
       self.assertTrue(hasattr(_a					, "do_resize"      )      )
       self.assertTrue(hasattr(_a					, "size"      )      )


     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size					, {"height": 18, "width": 18}      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=42      )
       self.assertEqual(image_processor.size					, {"height": 42, "width": 42}      )


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

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    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
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_processing(_a					, 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 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      str							=    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    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
       SCREAMING_SNAKE_CASE       :      List[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
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    image_processing(_a					, 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 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE       :      str							=    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
       SCREAMING_SNAKE_CASE       :      List[str]							=    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
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_processing(_a					, 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"],
           )					, )














from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[Any]							=    HfArgumentParser(lowercase				)
  SCREAMING_SNAKE_CASE       :      Any							=    parser.parse_args_into_dataclasses()[0]
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    TensorFlowBenchmark(args=lowercase				)
  try:
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    parser.parse_args_into_dataclasses()[0]
  except ValueError as e:
    SCREAMING_SNAKE_CASE       :      int							=    "Arg --no_{0} is no longer used, please use --no-{0} instead."
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    " ".join(str(lowercase				).split(" "				)[:-1]				)
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    ""
    SCREAMING_SNAKE_CASE       :      Any							=    eval(str(lowercase				).split(" "				)[-1]				)
    SCREAMING_SNAKE_CASE       :      List[str]							=    []
    for arg in depreciated_args:
      # arg[2:] removes '--'
      if arg[2:] in TensorFlowBenchmark.deprecated_args:
        # arg[5:] removes '--no_'
        full_error_msg += arg_error_msg.format(arg[5:]				)
      else:
        wrong_args.append(lowercase				)
    if len(lowercase				) > 0:
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    full_error_msg + begin_error_msg + str(lowercase				)
    raise ValueError(lowercase				)
  benchmark.run()


if __name__ == "__main__":
   main()






