






'''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 PoolFormerImageProcessor







class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def __init__(					self      : str	,    A__      : Union[str, Any]	,    A__      : Dict=7	,    A__      : List[str]=3	,    A__      : List[str]=30	,    A__      : List[str]=400	,    A__      : List[Any]=True	,    A__      : Union[str, Any]=None	,    A__      : Union[str, Any]=0.9	,    A__      : Any=None	,    A__      : List[Any]=True	,    A__      : Optional[int]=[0.5, 0.5, 0.5]	,    A__      : str=[0.5, 0.5, 0.5]	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				size if size is not None else {"""shortest_edge""": 30}
            __lowerCamelCase						:       Optional[int]				   =				crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
            __lowerCamelCase						:       List[Any]				   =				parent
            __lowerCamelCase						:       List[str]				   =				batch_size
            __lowerCamelCase						:       Any				   =				num_channels
            __lowerCamelCase						:       Union[str, Any]				   =				min_resolution
            __lowerCamelCase						:       Union[str, Any]				   =				max_resolution
            __lowerCamelCase						:       Dict				   =				do_resize_and_center_crop
            __lowerCamelCase						:       Optional[int]				   =				size
            __lowerCamelCase						:       List[str]				   =				crop_pct
            __lowerCamelCase						:       int				   =				crop_size
            __lowerCamelCase						:       Optional[int]				   =				do_normalize
            __lowerCamelCase						:       Optional[Any]				   =				image_mean
            __lowerCamelCase						:       int				   =				image_std




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            return {
                "size": self.size,
                "do_resize_and_center_crop": self.do_resize_and_center_crop,
                "crop_pct": self.crop_pct,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }







@require_torch
@require_vision
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     List[str]   		=			PoolFormerImageProcessor if is_vision_available() else None




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				PoolFormerImageProcessingTester(self					)




       @property
       def 					a_					(					self      : Any					):


            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()




       def 					a_					(					self      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(A__	,    """do_resize_and_center_crop"""					)					)
            self.assertTrue(hasattr(A__	,    """size"""					)					)
            self.assertTrue(hasattr(A__	,    """crop_pct"""					)					)
            self.assertTrue(hasattr(A__	,    """do_normalize"""					)					)
            self.assertTrue(hasattr(A__	,    """image_mean"""					)					)
            self.assertTrue(hasattr(A__	,    """image_std"""					)					)




       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size	,    {"""shortest_edge""": 30}					)
            self.assertEqual(image_processor.crop_size	,    {"""height""": 30, """width""": 30}					)

            __lowerCamelCase						:       Any				   =				self.image_processing_class.from_dict(self.image_processor_dict	,    size=42	,    crop_size=84					)
            self.assertEqual(image_processor.size	,    {"""shortest_edge""": 42}					)
            self.assertEqual(image_processor.crop_size	,    {"""height""": 84, """width""": 84}					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            pass




       def 					a_					(					self      : Optional[Any]					):


            """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=A__					)
            for image in image_inputs:
                 self.assertIsInstance(A__	,    Image.Image					)

            # Test not batched input
            __lowerCamelCase						:       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.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )

            # Test batched
            __lowerCamelCase						:       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.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            __lowerCamelCase						:       Optional[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
            __lowerCamelCase						:       Dict				   =				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.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )

            # Test batched
            __lowerCamelCase						:       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.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            __lowerCamelCase						:       Union[str, Any]				   =				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
            __lowerCamelCase						:       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.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )

            # Test batched
            __lowerCamelCase						:       int				   =				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.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )











'''simple docstring'''

from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def     __lowercase  ()   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				HfArgumentParser(_lowercase     )
     __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     __lowerCamelCase						:       Dict				   =				TensorFlowBenchmark(args=_lowercase     )
     try:
          __lowerCamelCase						:       List[str]				   =				parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          __lowerCamelCase						:       Tuple				   =				"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
          __lowerCamelCase						:       List[Any]				   =				""" """.join(str(_lowercase     ).split(""" """     )[:-1]     )
          __lowerCamelCase						:       Tuple				   =				""""""
          __lowerCamelCase						:       List[str]				   =				eval(str(_lowercase     ).split(""" """     )[-1]     )
          __lowerCamelCase						:       Tuple				   =				[]
          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:
               __lowerCamelCase						:       Tuple				   =				full_error_msg + begin_error_msg + str(_lowercase     )
          raise ValueError(_lowercase     )
     benchmark.run()


if __name__ == "__main__":
   main()



