





import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor



class    __lowerCAmelCase			(     unittest.TestCase	):






			def __init__(self   ,				__magic_name__   ,				__magic_name__=7   ,				__magic_name__=3   ,				__magic_name__=18   ,				__magic_name__=30   ,				__magic_name__=400   ,				__magic_name__=True   ,				__magic_name__=None   ,				__magic_name__=True   ,				__magic_name__=None   ,				__magic_name__=True   ,				__magic_name__=[0.5, 0.5, 0.5]   ,				__magic_name__=[0.5, 0.5, 0.5]   ,				__magic_name__=False   ,				)	->  int:


							'''simple docstring'''



							snake_case_		:       int           =    size if size is not None else {'''height''': 20, '''width''': 20}
							snake_case_		:       int           =    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							snake_case_		:       str           =    parent
							snake_case_		:       Optional[int]           =    batch_size
							snake_case_		:       Dict           =    num_channels
							snake_case_		:       List[Any]           =    image_size
							snake_case_		:       Union[str, Any]           =    min_resolution
							snake_case_		:       Tuple           =    max_resolution
							snake_case_		:       str           =    do_resize
							snake_case_		:       Tuple           =    size
							snake_case_		:       int           =    do_center_crop
							snake_case_		:       Tuple           =    crop_size
							snake_case_		:       int           =    do_normalize
							snake_case_		:       Optional[Any]           =    image_mean
							snake_case_		:       List[str]           =    image_std
							snake_case_		:       str           =    do_reduce_labels






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_reduce_labels": self.do_reduce_labels,
							}






def 	lowerCamelCase_		(							)						->		List[Any]:






				"""simple docstring"""


				snake_case_		:       Any           =    load_dataset('''hf-internal-testing/fixtures_ade20k'''					,							split='''test'''					)

				snake_case_		:       Union[str, Any]           =    Image.open(dataset[0]['''file''']					)
				snake_case_		:       str           =    Image.open(dataset[1]['''file''']					)

				return image, map






def 	lowerCamelCase_		(							)						->		List[Any]:






				"""simple docstring"""


				snake_case_		:       str           =    load_dataset('''hf-internal-testing/fixtures_ade20k'''					,							split='''test'''					)

				snake_case_		:       Optional[Any]           =    Image.open(ds[0]['''file''']					)
				snake_case_		:       Optional[Any]           =    Image.open(ds[1]['''file''']					)
				snake_case_		:       List[str]           =    Image.open(ds[2]['''file''']					)
				snake_case_		:       str           =    Image.open(ds[3]['''file''']					)

				return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class    __lowerCAmelCase			(     _a,	unittest.TestCase	):
			lowerCamelCase_	:	List[Any]			     = BeitImageProcessor if is_vision_available() else None






			def      lowerCamelCase    (self       )	->  Optional[Any]:


							'''simple docstring'''



							snake_case_		:       int           =    BeitImageProcessingTester(self       )






			@property
			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_		:       Dict           =    self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(__magic_name__   ,				'''do_resize'''       )       )
							self.assertTrue(hasattr(__magic_name__   ,				'''size'''       )       )
							self.assertTrue(hasattr(__magic_name__   ,				'''do_center_crop'''       )       )
							self.assertTrue(hasattr(__magic_name__   ,				'''center_crop'''       )       )
							self.assertTrue(hasattr(__magic_name__   ,				'''do_normalize'''       )       )
							self.assertTrue(hasattr(__magic_name__   ,				'''image_mean'''       )       )
							self.assertTrue(hasattr(__magic_name__   ,				'''image_std'''       )       )






			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							snake_case_		:       Any           =    self.image_processing_class.from_dict(self.image_processor_dict       )
							self.assertEqual(image_processor.size   ,				{'''height''': 20, '''width''': 20}       )
							self.assertEqual(image_processor.crop_size   ,				{'''height''': 18, '''width''': 18}       )
							self.assertEqual(image_processor.do_reduce_labels   ,				__magic_name__       )

							snake_case_		:       Union[str, Any]           =    self.image_processing_class.from_dict(
							    self.image_processor_dict   ,				size=42   ,				crop_size=84   ,				reduce_labels=__magic_name__       )
							self.assertEqual(image_processor.size   ,				{'''height''': 42, '''width''': 42}       )
							self.assertEqual(image_processor.crop_size   ,				{'''height''': 84, '''width''': 84}       )
							self.assertEqual(image_processor.do_reduce_labels   ,				__magic_name__       )






			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							pass






			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							snake_case_		:       Dict           =    self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							snake_case_		:       Optional[Any]           =    prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__magic_name__       )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__   ,				Image.Image       )

							# Test not batched input
							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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )   ,				)

							# Test batched
							snake_case_		:       Any           =    image_processing(__magic_name__   ,				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      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



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

							# Test not batched input
							snake_case_		:       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
							snake_case_		:       Optional[int]           =    image_processing(__magic_name__   ,				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      lowerCamelCase    (self       )	->  Optional[Any]:


							'''simple docstring'''



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

							# Test not batched input
							snake_case_		:       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
							snake_case_		:       List[str]           =    image_processing(__magic_name__   ,				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      lowerCamelCase    (self       )	->  Optional[Any]:


							'''simple docstring'''



							snake_case_		:       Any           =    self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							snake_case_		:       List[Any]           =    prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__magic_name__   ,				torchify=__magic_name__       )
							snake_case_		:       Union[str, Any]           =    []
							for image in image_inputs:
											self.assertIsInstance(__magic_name__   ,				torch.Tensor       )
											maps.append(torch.zeros(image.shape[-2:]       ).long()       )

							# Test not batched input
							snake_case_		:       List[str]           =    image_processing(image_inputs[0]   ,				maps[0]   ,				return_tensors='''pt'''       )
							self.assertEqual(
							    encoding['''pixel_values'''].shape   ,				(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )   ,				)
							self.assertEqual(
							    encoding['''labels'''].shape   ,				(
							        1,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )   ,				)
							self.assertEqual(encoding['''labels'''].dtype   ,				torch.long       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 255       )

							# Test batched
							snake_case_		:       Any           =    image_processing(__magic_name__   ,				__magic_name__   ,				return_tensors='''pt'''       )
							self.assertEqual(
							    encoding['''pixel_values'''].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'''],
							    )   ,				)
							self.assertEqual(
							    encoding['''labels'''].shape   ,				(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )   ,				)
							self.assertEqual(encoding['''labels'''].dtype   ,				torch.long       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 255       )

							# Test not batched input (PIL images)
							snake_case_     ,				snake_case_		:       Optional[int]           =    prepare_semantic_single_inputs()

							snake_case_		:       int           =    image_processing(__magic_name__   ,				__magic_name__   ,				return_tensors='''pt'''       )
							self.assertEqual(
							    encoding['''pixel_values'''].shape   ,				(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )   ,				)
							self.assertEqual(
							    encoding['''labels'''].shape   ,				(
							        1,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )   ,				)
							self.assertEqual(encoding['''labels'''].dtype   ,				torch.long       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 255       )

							# Test batched input (PIL images)
							snake_case_     ,				snake_case_		:       Dict           =    prepare_semantic_batch_inputs()

							snake_case_		:       Optional[int]           =    image_processing(__magic_name__   ,				__magic_name__   ,				return_tensors='''pt'''       )
							self.assertEqual(
							    encoding['''pixel_values'''].shape   ,				(
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )   ,				)
							self.assertEqual(
							    encoding['''labels'''].shape   ,				(
							        2,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )   ,				)
							self.assertEqual(encoding['''labels'''].dtype   ,				torch.long       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 255       )






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    self.image_processing_class(**self.image_processor_dict       )

							# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
							snake_case_     ,				snake_case_		:       Tuple           =    prepare_semantic_single_inputs()
							snake_case_		:       Optional[int]           =    image_processing(__magic_name__   ,				__magic_name__   ,				return_tensors='''pt'''       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 150       )

							snake_case_		:       List[Any]           =    True
							snake_case_		:       int           =    image_processing(__magic_name__   ,				__magic_name__   ,				return_tensors='''pt'''       )
							self.assertTrue(encoding['''labels'''].min().item() >= 0       )
							self.assertTrue(encoding['''labels'''].max().item() <= 255       )


'''simple docstring'''





SCREAMING_SNAKE_CASE     =       {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
SCREAMING_SNAKE_CASE     =       {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 1_2,
    """Pm""": 1_5,
    """Em""": 1_8,
    """Zm""": 2_1,
    """Ym""": 2_4,
}







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					str				       =							from_type.lower().strip("s"		)
  UpperCAmelCase__      :					Any				       =							to_type.lower().strip("s"		)

  UpperCAmelCase__      :					Union[str, Any]				       =							UNIT_SYMBOL.get(lowercase__   ,       lowercase__		)
  UpperCAmelCase__      :					Any				       =							UNIT_SYMBOL.get(lowercase__   ,       lowercase__		)

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

  if from_exponent > to_exponent:
    UpperCAmelCase__      :					Tuple				       =							from_exponent - to_exponent
  else:
    UpperCAmelCase__      :					Optional[int]				       =							-(to_exponent - from_exponent)

  return value * pow(1_0   ,       lowercase__		)


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

      testmod()



