




'''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 MobileNetVaImageProcessor

class 						lowerCAmelCase_   ( unittest.TestCase   ):







		'''simple docstring'''







		def __init__(						self    :	Tuple						,		_UpperCAmelCase    :	Tuple						,		_UpperCAmelCase    :	Optional[Any]=7						,		_UpperCAmelCase    :	Tuple=3						,		_UpperCAmelCase    :	List[Any]=18						,		_UpperCAmelCase    :	List[str]=30						,		_UpperCAmelCase    :	List[str]=4_00						,		_UpperCAmelCase    :	List[str]=True						,		_UpperCAmelCase    :	Optional[Any]=None						,		_UpperCAmelCase    :	Optional[Any]=True						,		_UpperCAmelCase    :	Dict=None						,		):






			"""simple docstring"""






			UpperCAmelCase__										=    size if size is not None else {"""shortest_edge""": 20}
			UpperCAmelCase__										=    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
			UpperCAmelCase__										=    parent
			UpperCAmelCase__										=    batch_size
			UpperCAmelCase__										=    num_channels
			UpperCAmelCase__										=    image_size
			UpperCAmelCase__										=    min_resolution
			UpperCAmelCase__										=    max_resolution
			UpperCAmelCase__										=    do_resize
			UpperCAmelCase__										=    size
			UpperCAmelCase__										=    do_center_crop
			UpperCAmelCase__										=    crop_size



		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






			"""simple docstring"""






			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			}

@require_torch
@require_vision
class 						lowerCAmelCase_   ( SCREAMING_SNAKE_CASE__			,  unittest.TestCase   ):







		'''simple docstring'''







		lowerCAmelCase_				:					List[Any]				     =						MobileNetVaImageProcessor if is_vision_available() else None


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






			"""simple docstring"""






			UpperCAmelCase__										=    MobileNetVaImageProcessingTester(self )


		@property
		def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






			"""simple docstring"""






			return self.image_processor_tester.prepare_image_processor_dict()


		def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.image_processing_class(**self.image_processor_dict )
			self.assertTrue(hasattr(_UpperCAmelCase						,		"""do_resize""" ) )
			self.assertTrue(hasattr(_UpperCAmelCase						,		"""size""" ) )
			self.assertTrue(hasattr(_UpperCAmelCase						,		"""do_center_crop""" ) )
			self.assertTrue(hasattr(_UpperCAmelCase						,		"""center_crop""" ) )


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.image_processing_class.from_dict(self.image_processor_dict )
			self.assertEqual(image_processor.size						,		{"""shortest_edge""": 20} )
			self.assertEqual(image_processor.crop_size						,		{"""height""": 18, """width""": 18} )

			UpperCAmelCase__										=    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 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






			"""simple docstring"""






			pass


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.image_processing_class(**self.image_processor_dict )
			# create random PIL images
			UpperCAmelCase__										=    prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_UpperCAmelCase )
			for image in image_inputs:
				self.assertIsInstance(_UpperCAmelCase						,		Image.Image )

			# Test not batched input
			UpperCAmelCase__										=    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
			UpperCAmelCase__										=    image_processing(_UpperCAmelCase						,		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 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.image_processing_class(**self.image_processor_dict )
			# create random numpy tensors
			UpperCAmelCase__										=    prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_UpperCAmelCase						,		numpify=_UpperCAmelCase )
			for image in image_inputs:
				self.assertIsInstance(_UpperCAmelCase						,		np.ndarray )

			# Test not batched input
			UpperCAmelCase__										=    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
			UpperCAmelCase__										=    image_processing(_UpperCAmelCase						,		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 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.image_processing_class(**self.image_processor_dict )
			# create random PyTorch tensors
			UpperCAmelCase__										=    prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_UpperCAmelCase						,		torchify=_UpperCAmelCase )
			for image in image_inputs:
				self.assertIsInstance(_UpperCAmelCase						,		torch.Tensor )

			# Test not batched input
			UpperCAmelCase__										=    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
			UpperCAmelCase__										=    image_processing(_UpperCAmelCase						,		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 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    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      float       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      str       ):
 '''simple docstring'''





 UpperCAmelCase__										=    from_type.lower().rstrip("""s"""       )
 UpperCAmelCase__										=    TYPE_CONVERSION.get(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    to_type.lower().rstrip("""s"""       )
 UpperCAmelCase__										=    TYPE_CONVERSION.get(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )
 if new_from not in METRIC_CONVERSION:
  UpperCAmelCase__										=    (
      F'''Invalid \'from_type\' value: {from_type!r}.\n'''
      F'''Conversion abbreviations are: {', '.join(SCREAMING_SNAKE_CASE__       )}'''
  )
  raise ValueError(SCREAMING_SNAKE_CASE__       )
 if new_to not in METRIC_CONVERSION:
  UpperCAmelCase__										=    (
      F'''Invalid \'to_type\' value: {to_type!r}.\n'''
      F'''Conversion abbreviations are: {', '.join(SCREAMING_SNAKE_CASE__       )}'''
  )
  raise ValueError(SCREAMING_SNAKE_CASE__       )
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
    import doctest

    doctest.testmod()
