
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
							import numpy as np
							import torch

if is_vision_available():
							from PIL import Image



def 		UpperCamelCase    (					_UpperCAmelCase     :      Dict   , _UpperCAmelCase     :      Tuple=False   , _UpperCAmelCase     :      List[Any]=False   , _UpperCAmelCase     :      List[Any]=False     )      -> int:






						'''simple docstring'''

						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						_lowercase					:  Optional[int]    			=      []
						for i in range(image_processor_tester.batch_size     ):
												if equal_resolution:
																		_lowercase					:  Tuple    			=      image_processor_tester.max_resolution
												else:
																		# To avoid getting image width/height 0
																		_lowercase					:  int    			=      image_processor_tester.min_resolution
																		if getattr(_UpperCAmelCase   , "size_divisor"   , _UpperCAmelCase     ):
																								# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
																								_lowercase					:  Any    			=      max(image_processor_tester.size_divisor   , _UpperCAmelCase     )
																		_lowercase			,						_lowercase					:  Union[str, Any]    			=      np.random.choice(np.arange(_UpperCAmelCase   , image_processor_tester.max_resolution     )   , 2     )
												image_inputs.append(
												    np.random.randint(255   , size=(image_processor_tester.num_channels, width, height)   , dtype=np.uinta     )     )

						if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												_lowercase					:  Optional[Any]    			=      [Image.fromarray(np.moveaxis(_UpperCAmelCase   , 0   , -1     )     ) for image in image_inputs]

						if torchify:
												_lowercase					:  Any    			=      [torch.from_numpy(_UpperCAmelCase     ) for image in image_inputs]

						return image_inputs



def 		UpperCamelCase    (					_UpperCAmelCase     :      Optional[int]   , _UpperCAmelCase     :      str=10   , _UpperCAmelCase     :      List[Any]=10   , _UpperCAmelCase     :      Any=False   , _UpperCAmelCase     :      List[Any]=False     )      -> Dict:






						'''simple docstring'''

						_lowercase					:  List[Any]    			=      []
						for i in range(image_processor_tester.num_frames     ):
												video.append(np.random.randint(255   , size=(image_processor_tester.num_channels, width, height)   , dtype=np.uinta     )     )

						if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												_lowercase					:  List[str]    			=      [Image.fromarray(np.moveaxis(_UpperCAmelCase   , 0   , -1     )     ) for frame in video]

						if torchify:
												_lowercase					:  Dict    			=      [torch.from_numpy(_UpperCAmelCase     ) for frame in video]

						return video



def 		UpperCamelCase    (					_UpperCAmelCase     :      int   , _UpperCAmelCase     :      List[Any]=False   , _UpperCAmelCase     :      int=False   , _UpperCAmelCase     :      Optional[int]=False     )      -> Any:






						'''simple docstring'''

						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						_lowercase					:  str    			=      []
						for i in range(image_processor_tester.batch_size     ):
												if equal_resolution:
																		_lowercase					:  List[str]    			=      image_processor_tester.max_resolution
												else:
																		_lowercase			,						_lowercase					:  Tuple    			=      np.random.choice(
																		    np.arange(image_processor_tester.min_resolution   , image_processor_tester.max_resolution     )   , 2     )
																		_lowercase					:  List[Any]    			=      prepare_video(
																		    image_processor_tester=_UpperCAmelCase   , width=_UpperCAmelCase   , height=_UpperCAmelCase   , numpify=_UpperCAmelCase   , torchify=_UpperCAmelCase   , )
												video_inputs.append(_UpperCAmelCase     )

						return video_inputs




class      __lowercase	:
	_A   			=      None






	def 			_a(self							:   Tuple						)   -> Dict:
							_lowercase					:  Union[str, Any]    			=      self.image_processing_class(**self.image_processor_dict						)
							_lowercase					:  Optional[int]    			=      json.loads(image_processor.to_json_string()						)
							for key, value in self.image_processor_dict.items():
													self.assertEqual(obj[key]			,  snake_case						)






	def 			_a(self							:   int						)   -> str:
							_lowercase					:  Any    			=      self.image_processing_class(**self.image_processor_dict						)

							with tempfile.TemporaryDirectory() as tmpdirname:
													_lowercase					:  int    			=      os.path.join(snake_case			,  "image_processor.json"						)
													image_processor_first.to_json_file(snake_case						)
													_lowercase					:  Dict    			=      self.image_processing_class.from_json_file(snake_case						)

							self.assertEqual(image_processor_second.to_dict()			,  image_processor_first.to_dict()						)






	def 			_a(self							:   List[Any]						)   -> Optional[Any]:
							_lowercase					:  str    			=      self.image_processing_class(**self.image_processor_dict						)

							with tempfile.TemporaryDirectory() as tmpdirname:
													_lowercase					:  Tuple    			=      image_processor_first.save_pretrained(snake_case						)[0]
													check_json_file_has_correct_format(snake_case						)
													_lowercase					:  Dict    			=      self.image_processing_class.from_pretrained(snake_case						)

							self.assertEqual(image_processor_second.to_dict()			,  image_processor_first.to_dict()						)






	def 			_a(self							:   List[str]						)   -> Any:
							_lowercase					:  List[Any]    			=      self.image_processing_class()
							self.assertIsNotNone(snake_case						)






	@require_torch
	@require_vision
	def 			_a(self							:   Optional[Any]						)   -> Optional[int]:
							if self.test_cast_dtype is not None:
													# Initialize image_processor
													_lowercase					:  Optional[Any]    			=      self.image_processing_class(**self.image_processor_dict						)

													# create random PyTorch tensors
													_lowercase					:  Optional[int]    			=      prepare_image_inputs(self.image_processor_tester			,  equal_resolution=snake_case			,  torchify=snake_case						)

													_lowercase					:  List[str]    			=      image_processor(snake_case			,  return_tensors="pt"						)
													# for layoutLM compatiblity
													self.assertEqual(encoding.pixel_values.device			,  torch.device("cpu"						)						)
													self.assertEqual(encoding.pixel_values.dtype			,  torch.floataa						)

													_lowercase					:  Union[str, Any]    			=      image_processor(snake_case			,  return_tensors="pt"						).to(torch.floataa						)
													self.assertEqual(encoding.pixel_values.device			,  torch.device("cpu"						)						)
													self.assertEqual(encoding.pixel_values.dtype			,  torch.floataa						)

													_lowercase					:  Any    			=      image_processor(snake_case			,  return_tensors="pt"						).to("cpu"			,  torch.bfloataa						)
													self.assertEqual(encoding.pixel_values.device			,  torch.device("cpu"						)						)
													self.assertEqual(encoding.pixel_values.dtype			,  torch.bfloataa						)

													with self.assertRaises(snake_case						):
																			_lowercase					:  str    			=      image_processor(snake_case			,  return_tensors="pt"						).to(torch.bfloataa			,  "cpu"						)

													# Try with text + image feature
													_lowercase					:  Optional[int]    			=      image_processor(snake_case			,  return_tensors="pt"						)
													encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]						)}						)
													_lowercase					:  List[Any]    			=      encoding.to(torch.floataa						)

													self.assertEqual(encoding.pixel_values.device			,  torch.device("cpu"						)						)
													self.assertEqual(encoding.pixel_values.dtype			,  torch.floataa						)
													self.assertEqual(encoding.input_ids.dtype			,  torch.long						)






from __future__ import annotations



def 		UpperCamelCase    (					_UpperCAmelCase     :      list[int]   , _UpperCAmelCase     :      int     )      -> int:






						'''simple docstring'''

						_lowercase			,						_lowercase					:  Any    			=      0, len(_UpperCAmelCase     ) - 1
						while start_ind <= end_ind:
												if array[start_ind] == search_item:
																		return start_ind
												elif array[end_ind] == search_item:
																		return end_ind
												else:
																		start_ind += 1
																		end_ind -= 1
    # returns -1 if search_item is not found in array
						return -1


if __name__ == "__main__":
							print(double_linear_search(list(range(100)), 40))




