

'''simple docstring'''

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      __magic_name__							(			A       , A=False       , A=False       , A=False				)      ->		List[Any]:
	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

	snake_case							=					[]
	for i in range(image_processor_tester.batch_size				):
		if equal_resolution:
			snake_case							=					snake_case							=					image_processor_tester.max_resolution
		else:
			# To avoid getting image width/height 0
			snake_case							=					image_processor_tester.min_resolution
			if getattr(A       , 'size_divisor'       , A				):
				# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
				snake_case							=					max(image_processor_tester.size_divisor       , A				)
			snake_case			,			snake_case							=					np.random.choice(np.arange(A       , image_processor_tester.max_resolution				)       , 2				)
		image_inputs.append(
		    np.random.randint(2_5_5       , 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
		snake_case							=					[Image.fromarray(np.moveaxis(A       , 0       , -1				)				) for image in image_inputs]

	if torchify:
		snake_case							=					[torch.from_numpy(A				) for image in image_inputs]

	return image_inputs

def      __magic_name__							(			A       , A=1_0       , A=1_0       , A=False       , A=False				)      ->		List[Any]:
	snake_case							=					[]
	for i in range(image_processor_tester.num_frames				):
		video.append(np.random.randint(2_5_5       , 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
		snake_case							=					[Image.fromarray(np.moveaxis(A       , 0       , -1				)				) for frame in video]

	if torchify:
		snake_case							=					[torch.from_numpy(A				) for frame in video]

	return video

def      __magic_name__							(			A       , A=False       , A=False       , A=False				)      ->		Union[str, Any]:
	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

	snake_case							=					[]
	for i in range(image_processor_tester.batch_size				):
		if equal_resolution:
			snake_case							=					snake_case							=					image_processor_tester.max_resolution
		else:
			snake_case			,			snake_case							=					np.random.choice(
			    np.arange(image_processor_tester.min_resolution       , image_processor_tester.max_resolution				)       , 2				)
			snake_case							=					prepare_video(
			    image_processor_tester=A       , width=A       , height=A       , numpify=A       , torchify=A       , )
		video_inputs.append(A				)

	return video_inputs







class lowerCamelCase   :
				snake_case_							     = None

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					snake_case							=					json.loads(image_processor.to_json_string() )
					for key, value in self.image_processor_dict.items():
						self.assertEqual(obj[key],						lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					self.image_processing_class(**self.image_processor_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
						snake_case							=					os.path.join(lowercase_,						'image_processor.json' )
						image_processor_first.to_json_file(lowercase_ )
						snake_case							=					self.image_processing_class.from_json_file(lowercase_ )

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

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					self.image_processing_class(**self.image_processor_dict )

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

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

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					self.image_processing_class()
					self.assertIsNotNone(lowercase_ )






				@require_torch
				@require_vision
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					if self.test_cast_dtype is not None:
						# Initialize image_processor
						snake_case							=					self.image_processing_class(**self.image_processor_dict )

						# create random PyTorch tensors
						snake_case							=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowercase_,						torchify=lowercase_ )

						snake_case							=					image_processor(lowercase_,						return_tensors='pt' )
						# for layoutLM compatiblity
						self.assertEqual(encoding.pixel_values.device,						torch.device('cpu' ) )
						self.assertEqual(encoding.pixel_values.dtype,						torch.floataa )

						snake_case							=					image_processor(lowercase_,						return_tensors='pt' ).to(torch.floataa )
						self.assertEqual(encoding.pixel_values.device,						torch.device('cpu' ) )
						self.assertEqual(encoding.pixel_values.dtype,						torch.floataa )

						snake_case							=					image_processor(lowercase_,						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(lowercase_ ):
							snake_case							=					image_processor(lowercase_,						return_tensors='pt' ).to(torch.bfloataa,						'cpu' )

						# Try with text + image feature
						snake_case							=					image_processor(lowercase_,						return_tensors='pt' )
						encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]] )} )
						snake_case							=					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 )








'''simple docstring'''

def      __magic_name__							(			A				)      ->		tuple[int, int]:
	# base case
	if len(A				) == 1:
		return a[0], a[0]
	else:
		# split A into half.
		snake_case							=					a[: len(A				) // 2]
		snake_case							=					a[len(A				) // 2 :]

		# 2 sub problems, 1/2 of original size.
		snake_case			,			snake_case							=					max_difference(A				)
		snake_case			,			snake_case							=					max_difference(A				)

		# get min of first and max of second
		# linear time
		snake_case							=					min(A				)
		snake_case							=					max(A				)

		# 3 cases, either (small1, big1),
		# (min_first, max_second), (small2, big2)
		# constant comparisons
		if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
			return smalla, biga
		elif biga - smalla > max_second - min_first:
			return smalla, biga
		else:
			return min_first, max_second


if __name__ == "__main__":
						import doctest

						doctest.testmod()





