





"""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 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase=7 ,						__lowerCamelCase=3 ,						__lowerCamelCase=30 ,						__lowerCamelCase=400 ,						__lowerCamelCase=True ,						__lowerCamelCase=None ,						__lowerCamelCase=0.9 ,						__lowerCamelCase=None ,						__lowerCamelCase=True ,						__lowerCamelCase=[0.5, 0.5, 0.5] ,						__lowerCamelCase=[0.5, 0.5, 0.5] ,						):



     '''simple docstring'''




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







 def  UpperCamelCase__(   self  ):



     '''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 		__snake_case (		__UpperCamelCase							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					PoolFormerImageProcessor if is_vision_available() else None

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						str									=					PoolFormerImageProcessingTester(self  )

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						str									=					self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(__lowerCamelCase ,						'''do_resize_and_center_crop'''  )  )
     self.assertTrue(hasattr(__lowerCamelCase ,						'''size'''  )  )
     self.assertTrue(hasattr(__lowerCamelCase ,						'''crop_pct'''  )  )
     self.assertTrue(hasattr(__lowerCamelCase ,						'''do_normalize'''  )  )
     self.assertTrue(hasattr(__lowerCamelCase ,						'''image_mean'''  )  )
     self.assertTrue(hasattr(__lowerCamelCase ,						'''image_std'''  )  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						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}  )

     __A   :						Optional[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  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A   :						Tuple									=					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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ) ,						)

     # Test batched
     __A   :						List[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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ) ,						)

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A   :						Dict									=					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   :						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
     __A   :						Optional[int]									=					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.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         ) ,						)











"""simple docstring"""



a_			=      {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
a_			=      {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}







def 		__lowercase					(       snake_case_  :  float    ,snake_case_  :  str    ,snake_case_  :  str						)       ->float:



    '''simple docstring'''







    __A   :						Tuple									=					from_type.lower().strip('''s'''						)
    __A   :						Optional[int]									=					to_type.lower().strip('''s'''						)

    __A   :						List[str]									=					UNIT_SYMBOL.get(snake_case_    ,snake_case_						)
    __A   :						Any									=					UNIT_SYMBOL.get(snake_case_    ,snake_case_						)

    if from_sanitized not in METRIC_CONVERSION:
        __A   :						int									=					(
            F"""Invalid 'from_type' value: {from_type!r}.\n"""
            F"""Conversion abbreviations are: {', '.join(snake_case_						)}"""
        )
        raise ValueError(snake_case_						)
    if to_sanitized not in METRIC_CONVERSION:
        __A   :						str									=					(
            F"""Invalid 'to_type' value: {to_type!r}.\n"""
            F"""Conversion abbreviations are: {', '.join(snake_case_						)}"""
        )
        raise ValueError(snake_case_						)
    __A   :						Optional[Any]									=					METRIC_CONVERSION[from_sanitized]
    __A   :						Optional[int]									=					METRIC_CONVERSION[to_sanitized]
    __A   :						Union[str, Any]									=					1

    if from_exponent > to_exponent:
        __A   :						Dict									=					from_exponent - to_exponent
    else:
        __A   :						Union[str, Any]									=					-(to_exponent - from_exponent)

    return value * pow(10    ,snake_case_						)


if __name__ == "__main__":
       from doctest import testmod

       testmod()




