



"""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):

	def __init__(  self	: Any						,  lowercase_	: Optional[Any]						,  lowercase_	: bool = True						,  lowercase_	: Dict[str, int] = None						,  lowercase_	: int = 32						,  lowercase_	: bool = True						,  lowercase_	: Union[int, float] = 1 / 255						,  lowercase_	: bool = True						,  lowercase_	: bool = True						,  lowercase_	: Optional[Union[float, List[float]]] = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]						,  lowercase_	: Optional[Union[float, List[float]]] = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]						,  lowercase_	: bool = True						,  lowercase_	: Optional[Any]=7						,  lowercase_	: List[str]=30						,  lowercase_	: Any=400						,  lowercase_	: Union[str, Any]=3						,  ):
					snake_case_				:  int       =			parent
					snake_case_				:  List[str]       =			do_resize
					snake_case_				:  str       =			size if size is not None else {'''shortest_edge''': 288}
					snake_case_				:  Any       =			size_divisor
					snake_case_				:  List[Any]       =			do_rescale
					snake_case_				:  Optional[int]       =			rescale_factor
					snake_case_				:  Any       =			do_normalize
					snake_case_				:  str       =			do_center_crop
					snake_case_				:  Any       =			image_mean
					snake_case_				:  Dict       =			image_std
					snake_case_				:  List[str]       =			do_pad
					snake_case_				:  Tuple       =			batch_size
					snake_case_				:  List[str]       =			num_channels
					snake_case_				:  Dict       =			min_resolution
					snake_case_				:  Union[str, Any]       =			max_resolution

	def 		_snake_case      (  self	: Dict       ):
					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 		_snake_case      (  self	: Dict						,  lowercase_	: Dict						,  lowercase_	: List[str]=False       ):
					if not batched:
									snake_case_				:  Dict       =			self.size['''shortest_edge''']
									snake_case_				:  Optional[Any]       =			image_inputs[0]
									if isinstance(lowercase_						,  Image.Image       ):
													snake_case_				:  Optional[int]       =			image.size
									else:
													snake_case_				:  List[str]       =			image.shape[1], image.shape[2]
									snake_case_				:  Any       =			size / min(lowercase_						,  lowercase_       )
									if h < w:
													snake_case_				:  Dict       =			size, scale * w
									else:
													snake_case_				:  Union[str, Any]       =			scale * h, size

									snake_case_				:  str       =			int((1333 / 800) * size       )
									if max(lowercase_						,  lowercase_       ) > max_size:
													snake_case_				:  List[str]       =			max_size / max(lowercase_						,  lowercase_       )
													snake_case_				:  Tuple       =			newh * scale
													snake_case_				:  Tuple       =			neww * scale

									snake_case_				:  str       =			int(newh + 0.5       ), int(neww + 0.5       )
									snake_case_				:  List[str]       =			(
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

					else:
									snake_case_				:  Union[str, Any]       =			[]
									for image in image_inputs:
													snake_case_				:  Optional[Any]       =			self.get_expected_values([image]       )
													expected_values.append((expected_height, expected_width)       )
									snake_case_				:  Optional[int]       =			max(lowercase_						,  key=lambda lowercase_       : item[0]       )[0]
									snake_case_				:  Union[str, Any]       =			max(lowercase_						,  key=lambda lowercase_       : item[1]       )[1]

					return expected_height, expected_width





@require_torch
@require_vision
class  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):
	_lowerCAmelCase     :    Optional[int]	      =	BridgeTowerImageProcessor if is_vision_available() else None

	def 		_snake_case      (  self	: List[Any]       ):
					snake_case_				:  Tuple       =			BridgeTowerImageProcessingTester(self       )

	@property
	def 		_snake_case      (  self	: Union[str, Any]       ):
					return self.image_processor_tester.prepare_image_processor_dict()

	def 		_snake_case      (  self	: Optional[Any]       ):
					snake_case_				:  str       =			self.image_processing_class(**self.image_processor_dict       )
					self.assertTrue(hasattr(lowercase_						,  '''image_mean'''       )       )
					self.assertTrue(hasattr(lowercase_						,  '''image_std'''       )       )
					self.assertTrue(hasattr(lowercase_						,  '''do_normalize'''       )       )
					self.assertTrue(hasattr(lowercase_						,  '''do_resize'''       )       )
					self.assertTrue(hasattr(lowercase_						,  '''size'''       )       )
					self.assertTrue(hasattr(lowercase_						,  '''size_divisor'''       )       )

	def 		_snake_case      (  self	: Tuple       ):
					pass

	def 		_snake_case      (  self	: Optional[Any]       ):
					# Initialize image processor
					snake_case_				:  List[Any]       =			self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					snake_case_				:  str       =			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowercase_       )
					for image in image_inputs:
									self.assertIsInstance(lowercase_						,  Image.Image       )

					# Test not batched input
					snake_case_				:  Union[str, Any]       =			image_processing(image_inputs[0]						,  return_tensors='''pt'''       ).pixel_values

					snake_case_				:  Dict       =			self.image_processor_tester.get_expected_values(lowercase_       )
					self.assertEqual(
					    encoded_images.shape						,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,  )

					# Test batched
					snake_case_				:  str       =			image_processing(lowercase_						,  return_tensors='''pt'''       ).pixel_values

					snake_case_				:  int       =			self.image_processor_tester.get_expected_values(lowercase_						,  batched=lowercase_       )
					self.assertEqual(
					    encoded_images.shape						,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )						,  )

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

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

					snake_case_				:  Union[str, Any]       =			self.image_processor_tester.get_expected_values(lowercase_       )
					self.assertEqual(
					    encoded_images.shape						,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,  )

					# Test batched
					snake_case_				:  List[Any]       =			image_processing(lowercase_						,  return_tensors='''pt'''       ).pixel_values

					snake_case_				:  int       =			self.image_processor_tester.get_expected_values(lowercase_						,  batched=lowercase_       )
					self.assertEqual(
					    encoded_images.shape						,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )						,  )






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

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

					snake_case_				:  Dict       =			self.image_processor_tester.get_expected_values(lowercase_       )
					self.assertEqual(
					    encoded_images.shape						,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,  )

					# Test batched
					snake_case_				:  Dict       =			image_processing(lowercase_						,  return_tensors='''pt'''       ).pixel_values

					snake_case_				:  Any       =			self.image_processor_tester.get_expected_values(lowercase_						,  batched=lowercase_       )
					self.assertEqual(
					    encoded_images.shape						,  (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )						,  )










"""simple docstring"""

def __lowercase    (							_a					):
    snake_case_				:  Optional[Any]       =			int(_a					)
    if decimal in (0, 1):  # Exit cases for the recursion
        return str(_a					)
    snake_case_,			snake_case_				:  Optional[int]       =			divmod(_a	,    2					)
    return binary_recursive(_a					) + str(_a					)




def __lowercase    (							_a					):
    snake_case_				:  Dict       =			str(_a					).strip()
    if not number:
        raise ValueError('''No input value was provided'''					)
    snake_case_				:  List[str]       =			'''-''' if number.startswith('''-'''					) else ''''''
    snake_case_				:  Optional[Any]       =			number.lstrip('''-'''					)
    if not number.isnumeric():
        raise ValueError('''Input value is not an integer'''					)
    return f"{negative}0b{binary_recursive(int(_a					)					)}"


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

     testmod()





