






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 ViTImageProcessor




class 	lowerCamelCase__				(							unittest.TestCase ):






      def __init__(self					:    List[str]		,		_snake_case					:    List[Any]		,		_snake_case					:    List[str]=7		,		_snake_case					:    List[str]=3		,		_snake_case					:    str=18		,		_snake_case					:    List[Any]=30		,		_snake_case					:    Optional[int]=400		,		_snake_case					:    Optional[int]=True		,		_snake_case					:    Any=None		,		_snake_case					:    int=True		,		_snake_case					:    List[str]=[0.5, 0.5, 0.5]		,		_snake_case					:    str=[0.5, 0.5, 0.5]		,		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     size if size is not None else {'height': 18, 'width': 18}
       lowerCamelCase_							:    Union[str, Any]							=     parent
       lowerCamelCase_							:    Optional[Any]							=     batch_size
       lowerCamelCase_							:    List[Any]							=     num_channels
       lowerCamelCase_							:    Any							=     image_size
       lowerCamelCase_							:    List[Any]							=     min_resolution
       lowerCamelCase_							:    List[Any]							=     max_resolution
       lowerCamelCase_							:    List[str]							=     do_resize
       lowerCamelCase_							:    Dict							=     size
       lowerCamelCase_							:    int							=     do_normalize
       lowerCamelCase_							:    Dict							=     image_mean
       lowerCamelCase_							:    Tuple							=     image_std






      def      UpperCAmelCase_     (self					:    Optional[int]		)       ->   Union[str, Any]:


       """simple docstring"""






       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 	lowerCamelCase__				(							UpperCAmelCase,   unittest.TestCase ):
      lowerCamelCase_							:   Tuple											=	ViTImageProcessor if is_vision_available() else None






      def      UpperCAmelCase_     (self					:    int		)       ->   int:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     ViTImageProcessingTester(self		)






      @property
      def      UpperCAmelCase_     (self					:    List[str]		)       ->   List[Any]:


       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()






      def      UpperCAmelCase_     (self					:    Tuple		)       ->   Optional[int]:


       """simple docstring"""






       lowerCamelCase_							:    int							=     self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(_snake_case		,		'image_mean'		)		)
       self.assertTrue(hasattr(_snake_case		,		'image_std'		)		)
       self.assertTrue(hasattr(_snake_case		,		'do_normalize'		)		)
       self.assertTrue(hasattr(_snake_case		,		'do_resize'		)		)
       self.assertTrue(hasattr(_snake_case		,		'size'		)		)






      def      UpperCAmelCase_     (self					:    Dict		)       ->   int:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     self.image_processing_class.from_dict(self.image_processor_dict		)
       self.assertEqual(image_processor.size		,		{'height': 18, 'width': 18}		)

       lowerCamelCase_							:    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      UpperCAmelCase_     (self					:    Optional[Any]		)       ->   int:


       """simple docstring"""






       pass






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   str:


       """simple docstring"""






       lowerCamelCase_							:    Tuple							=     self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       lowerCamelCase_							:    Optional[int]							=     prepare_image_inputs(self.image_processor_tester		,		equal_resolution=_snake_case		)
       for image in image_inputs:
        self.assertIsInstance(_snake_case		,		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.size['height'],
               self.image_processor_tester.size['width'],
           )		,		)

       # Test batched
       lowerCamelCase_							:    List[str]							=     image_processing(_snake_case		,		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      UpperCAmelCase_     (self					:    str		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    Union[str, Any]							=     self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       lowerCamelCase_							:    Union[str, Any]							=     prepare_image_inputs(self.image_processor_tester		,		equal_resolution=_snake_case		,		numpify=_snake_case		)
       for image in image_inputs:
        self.assertIsInstance(_snake_case		,		np.ndarray		)

       # Test not batched input
       lowerCamelCase_							:    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
       lowerCamelCase_							:    Dict							=     image_processing(_snake_case		,		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      UpperCAmelCase_     (self					:    Optional[int]		)       ->   List[str]:


       """simple docstring"""






       lowerCamelCase_							:    Union[str, Any]							=     self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       lowerCamelCase_							:    Tuple							=     prepare_image_inputs(self.image_processor_tester		,		equal_resolution=_snake_case		,		torchify=_snake_case		)
       for image in image_inputs:
        self.assertIsInstance(_snake_case		,		torch.Tensor		)

       # Test not batched input
       lowerCamelCase_							:    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
       lowerCamelCase_							:    Union[str, Any]							=     image_processing(_snake_case		,		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 collections import namedtuple

UpperCamelCase											=      namedtuple('''from_to''', '''from_ to''')

UpperCamelCase											=      {
    '''millimeter''': '''mm''',
    '''centimeter''': '''cm''',
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''inch''': '''in''',
    '''inche''': '''in''',  # Trailing 's' has been stripped off
    '''feet''': '''ft''',
    '''foot''': '''ft''',
    '''yard''': '''yd''',
    '''mile''': '''mi''',
}

UpperCamelCase											=      {
    '''mm''': from_to(0.001, 1_0_0_0),
    '''cm''': from_to(0.01, 1_0_0),
    '''m''': from_to(1, 1),
    '''km''': from_to(1_0_0_0, 0.001),
    '''in''': from_to(0.0254, 39.3701),
    '''ft''': from_to(0.3048, 3.2_8084),
    '''yd''': from_to(0.9144, 1.0_9361),
    '''mi''': from_to(1609.34, 0.0_0062_1371),
}




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      float:
 lowerCamelCase_							:    List[Any]							=     from_type.lower().rstrip('s'				)
 lowerCamelCase_							:    Optional[Any]							=     TYPE_CONVERSION.get(lowerCamelCase__ ,			lowerCamelCase__				)
 lowerCamelCase_							:    List[Any]							=     to_type.lower().rstrip('s'				)
 lowerCamelCase_							:    Optional[int]							=     TYPE_CONVERSION.get(lowerCamelCase__ ,			lowerCamelCase__				)
 if new_from not in METRIC_CONVERSION:
  lowerCamelCase_							:    List[Any]							=     (
      F'Invalid \'from_type\' value: {from_type!r}.\n'
      F'Conversion abbreviations are: {", ".join(lowerCamelCase__				)}'
  )
  raise ValueError(lowerCamelCase__				)
 if new_to not in METRIC_CONVERSION:
  lowerCamelCase_							:    List[str]							=     (
      F'Invalid \'to_type\' value: {to_type!r}.\n'
      F'Conversion abbreviations are: {", ".join(lowerCamelCase__				)}'
  )
  raise ValueError(lowerCamelCase__				)
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
       import doctest

       doctest.testmod()
