
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 			lowercase_		(      unittest.TestCase       ):


       def __init__(    self						,       __A						,       __A=7						,       __A=3						,       __A=18						,       __A=30						,       __A=400						,       __A=True						,       __A=None						,       __A=True						,       __A=None						,       __A=True						,       __A=[0.5, 0.5, 0.5]						,       __A=[0.5, 0.5, 0.5]						,       __A=False						,       )						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=size if size is not None else {'''height''': 20, '''width''': 20}
              SCREAMING_SNAKE_CASE_							:				Tuple		=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=parent
              SCREAMING_SNAKE_CASE_							:				Any		=batch_size
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=num_channels
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=image_size
              SCREAMING_SNAKE_CASE_							:				List[Any]		=min_resolution
              SCREAMING_SNAKE_CASE_							:				List[Any]		=max_resolution
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=do_resize
              SCREAMING_SNAKE_CASE_							:				str		=size
              SCREAMING_SNAKE_CASE_							:				str		=do_center_crop
              SCREAMING_SNAKE_CASE_							:				List[Any]		=crop_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=do_normalize
              SCREAMING_SNAKE_CASE_							:				List[str]		=image_mean
              SCREAMING_SNAKE_CASE_							:				Any		=image_std
              SCREAMING_SNAKE_CASE_							:				List[Any]		=do_reduce_labels





       def 	_snake_case				(    self				)						->    List[Any]:
              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 							SCREAMING_SNAKE_CASE_	(					)			->						Any:
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=load_dataset('''hf-internal-testing/fixtures_ade20k'''			,							split='''test'''				)

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=Image.open(dataset[0]['''file''']				)
       SCREAMING_SNAKE_CASE_							:				str		=Image.open(dataset[1]['''file''']				)

       return image, map
def 							SCREAMING_SNAKE_CASE_	(					)			->						Optional[Any]:
       SCREAMING_SNAKE_CASE_							:				int		=load_dataset('''hf-internal-testing/fixtures_ade20k'''			,							split='''test'''				)

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

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


@require_torch
@require_vision
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						BeitImageProcessor if is_vision_available() else None


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Tuple		=BeitImageProcessingTester(self				)


       @property
       def 	_snake_case				(    self				)						->    Optional[int]:
              return self.image_processor_tester.prepare_image_processor_dict()


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(**self.image_processor_dict				)
              self.assertTrue(hasattr(__A						,       '''do_resize'''				)				)
              self.assertTrue(hasattr(__A						,       '''size'''				)				)
              self.assertTrue(hasattr(__A						,       '''do_center_crop'''				)				)
              self.assertTrue(hasattr(__A						,       '''center_crop'''				)				)
              self.assertTrue(hasattr(__A						,       '''do_normalize'''				)				)
              self.assertTrue(hasattr(__A						,       '''image_mean'''				)				)
              self.assertTrue(hasattr(__A						,       '''image_std'''				)				)


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Dict		=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						,       __A				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.image_processing_class.from_dict(
                  self.image_processor_dict						,       size=42						,       crop_size=84						,       reduce_labels=__A				)
              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						,       __A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              pass


       def 	_snake_case				(    self				)						->    int:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.image_processing_class(**self.image_processor_dict				)
              # create random PIL images
              SCREAMING_SNAKE_CASE_							:				List[Any]		=prepare_image_inputs(self.image_processor_tester						,       equal_resolution=__A				)
              for image in image_inputs:
                     self.assertIsInstance(__A						,       Image.Image				)

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

              # Test batched
              SCREAMING_SNAKE_CASE_							:				Optional[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'''],
                  )						,       )


       def 	_snake_case				(    self				)						->    Any:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.image_processing_class(**self.image_processor_dict				)
              # create random numpy tensors
              SCREAMING_SNAKE_CASE_							:				Tuple		=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
              SCREAMING_SNAKE_CASE_							:				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
              SCREAMING_SNAKE_CASE_							:				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 	_snake_case				(    self				)						->    Optional[int]:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.image_processing_class(**self.image_processor_dict				)
              # create random PyTorch tensors
              SCREAMING_SNAKE_CASE_							:				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
              SCREAMING_SNAKE_CASE_							:				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
              SCREAMING_SNAKE_CASE_							:				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 	_snake_case				(    self				)						->    Optional[int]:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(**self.image_processor_dict				)
              # create random PyTorch tensors
              SCREAMING_SNAKE_CASE_							:				str		=prepare_image_inputs(self.image_processor_tester						,       equal_resolution=__A						,       torchify=__A				)
              SCREAMING_SNAKE_CASE_							:				int		=[]
              for image in image_inputs:
                     self.assertIsInstance(__A						,       torch.Tensor				)
                     maps.append(torch.zeros(image.shape[-2:]				).long()				)

              # Test not batched input
              SCREAMING_SNAKE_CASE_							:				Tuple		=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
              SCREAMING_SNAKE_CASE_							:				Dict		=image_processing(__A						,       __A						,       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)
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=prepare_semantic_single_inputs()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=image_processing(__A						,       __A						,       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)
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				int		=prepare_semantic_batch_inputs()

              SCREAMING_SNAKE_CASE_							:				Any		=image_processing(__A						,       __A						,       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 	_snake_case				(    self				)						->    Tuple:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				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
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=prepare_semantic_single_inputs()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=image_processing(__A						,       __A						,       return_tensors='''pt'''				)
              self.assertTrue(encoding['''labels'''].min().item() >= 0				)
              self.assertTrue(encoding['''labels'''].max().item() <= 150				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=True
              SCREAMING_SNAKE_CASE_							:				List[str]		=image_processing(__A						,       __A						,       return_tensors='''pt'''				)
              self.assertTrue(encoding['''labels'''].min().item() >= 0				)
              self.assertTrue(encoding['''labels'''].max().item() <= 255				)


def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int | float | str				)			->						tuple[int, int]:
       try:
              SCREAMING_SNAKE_CASE_							:				int		=float(UpperCAmelCase_				)
       except ValueError:
              raise ValueError('''Please enter a valid number'''				)
       SCREAMING_SNAKE_CASE_							:				Any		=decimal - int(UpperCAmelCase_				)
       if fractional_part == 0:
              return int(UpperCAmelCase_				), 1
       else:
              SCREAMING_SNAKE_CASE_							:				Any		=len(str(UpperCAmelCase_				).split('''.'''				)[1]				)
              SCREAMING_SNAKE_CASE_							:				str		=int(decimal * (1_0**number_of_frac_digits)				)
              SCREAMING_SNAKE_CASE_							:				Any		=1_0**number_of_frac_digits
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=denominator, numerator
              while True:
                     SCREAMING_SNAKE_CASE_							:				Any		=dividend % divisor
                     if remainder == 0:
                            break
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=divisor, remainder
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=numerator / divisor, denominator / divisor
              return int(UpperCAmelCase_				), int(UpperCAmelCase_				)


if __name__ == "__main__":
 print(F"{decimal_to_fraction(2) = }")
 print(F"{decimal_to_fraction(89.0) = }")
 print(F"{decimal_to_fraction('67') = }")
 print(F"{decimal_to_fraction('45.0') = }")
 print(F"{decimal_to_fraction(1.5) = }")
 print(F"{decimal_to_fraction('6.25') = }")
 print(F"{decimal_to_fraction('78td') = }")
