

'''simple docstring'''





import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor


class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = 32   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = 1 / 255   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = [0.48_145_466, 0.4_578_275, 0.40_821_073]   ,			SCREAMING_SNAKE_CASE_ = [0.26_862_954, 0.26_130_258, 0.27_577_711]   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_=7   ,			SCREAMING_SNAKE_CASE_=30   ,			SCREAMING_SNAKE_CASE_=400   ,			SCREAMING_SNAKE_CASE_=3   ,			)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      parent
							lowerCamelCase_  				=      do_resize
							lowerCamelCase_  				=      size if size is not None else {'shortest_edge': 288}
							lowerCamelCase_  				=      size_divisor
							lowerCamelCase_  				=      do_rescale
							lowerCamelCase_  				=      rescale_factor
							lowerCamelCase_  				=      do_normalize
							lowerCamelCase_  				=      do_center_crop
							lowerCamelCase_  				=      image_mean
							lowerCamelCase_  				=      image_std
							lowerCamelCase_  				=      do_pad
							lowerCamelCase_  				=      batch_size
							lowerCamelCase_  				=      num_channels
							lowerCamelCase_  				=      min_resolution
							lowerCamelCase_  				=      max_resolution
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''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,
							    "size_divisor": self.size_divisor,
							}






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=False		)  ->     List[str]:






							'''simple docstring'''



							if not batched:
												lowerCamelCase_  				=      self.size['shortest_edge']
												lowerCamelCase_  				=      image_inputs[0]
												if isinstance(SCREAMING_SNAKE_CASE_   ,			Image.Image		):
																	lowerCamelCase_				,lowerCamelCase_  				=      image.size
												else:
																	lowerCamelCase_				,lowerCamelCase_  				=      image.shape[1], image.shape[2]
												lowerCamelCase_  				=      size / min(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
												if h < w:
																	lowerCamelCase_				,lowerCamelCase_  				=      size, scale * w
												else:
																	lowerCamelCase_				,lowerCamelCase_  				=      scale * h, size

												lowerCamelCase_  				=      int((1333 / 800) * size		)
												if max(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		) > max_size:
																	lowerCamelCase_  				=      max_size / max(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_  				=      newh * scale
																	lowerCamelCase_  				=      neww * scale

												lowerCamelCase_				,lowerCamelCase_  				=      int(newh + 0.5		), int(neww + 0.5		)
												lowerCamelCase_				,lowerCamelCase_  				=      (
												    newh // self.size_divisor * self.size_divisor,
												    neww // self.size_divisor * self.size_divisor,
												)

							else:
												lowerCamelCase_  				=      []
												for image in image_inputs:
																	lowerCamelCase_				,lowerCamelCase_  				=      self.get_expected_values([image]		)
																	expected_values.append((expected_height, expected_width)		)
												lowerCamelCase_  				=      max(SCREAMING_SNAKE_CASE_   ,			key=lambda SCREAMING_SNAKE_CASE_		: item[0]		)[0]
												lowerCamelCase_  				=      max(SCREAMING_SNAKE_CASE_   ,			key=lambda SCREAMING_SNAKE_CASE_		: item[1]		)[1]

							return expected_height, expected_width


@require_torch
@require_vision
class  UpperCAmelCase (							UpperCAmelCase__  , unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					BridgeTowerImageProcessor if is_vision_available() else None
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      BridgeTowerImageProcessingTester(self		)
		@property
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'image_mean'		)		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'image_std'		)		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'do_normalize'		)		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'do_resize'		)		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'size'		)		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'size_divisor'		)		)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							pass
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



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

							# Test not batched input
							lowerCamelCase_  				=      image_processing(image_inputs[0]   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,			)

							# Test batched
							lowerCamelCase_  				=      image_processing(SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   ,			batched=SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   ,			)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



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

							# Test not batched input
							lowerCamelCase_  				=      image_processing(image_inputs[0]   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,			)

							# Test batched
							lowerCamelCase_  				=      image_processing(SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   ,			batched=SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   ,			)






		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



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

							# Test not batched input
							lowerCamelCase_  				=      image_processing(image_inputs[0]   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,			)

							# Test batched
							lowerCamelCase_  				=      image_processing(SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   ,			batched=SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   ,			)






"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 if digit_amount > 0:
  return round(number - int(lowercase__   )					,					lowercase__   )
 return number - int(lowercase__   )


if __name__ == "__main__":
    print(decimal_isolate(1.53, 0))
    print(decimal_isolate(35.345, 1))
    print(decimal_isolate(35.345, 2))
    print(decimal_isolate(35.345, 3))
    print(decimal_isolate(-14.789, 3))
    print(decimal_isolate(0, 2))
    print(decimal_isolate(-14.123, 1))
    print(decimal_isolate(-14.123, 2))
    print(decimal_isolate(-14.123, 3))






