


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

			UpperCamelCase__   =    []
			for i in range(image_processor_tester.batch_size					):
						if equal_resolution:
									UpperCamelCase__   =    UpperCamelCase__   =    image_processor_tester.max_resolution
						else:
									# To avoid getting image width/height 0
									UpperCamelCase__   =    image_processor_tester.min_resolution
									if getattr(_lowerCamelCase			,       "size_divisor"			,       _lowerCamelCase					):
												# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
												UpperCamelCase__   =    max(image_processor_tester.size_divisor			,       _lowerCamelCase					)
									UpperCamelCase__, UpperCamelCase__   =    np.random.choice(np.arange(_lowerCamelCase			,       image_processor_tester.max_resolution					)			,       2					)
						image_inputs.append(
						    np.random.randint(2_55			,       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
						UpperCamelCase__   =    [Image.fromarray(np.moveaxis(_lowerCamelCase			,       0			,       -1					)					) for image in image_inputs]

			if torchify:
						UpperCamelCase__   =    [torch.from_numpy(_lowerCamelCase					) for image in image_inputs]

			return image_inputs
def     UpperCamelCase_(			_A				:List[Any]			,       _A				:Tuple=10			,       _A				:List[str]=10			,       _A				:Optional[Any]=False			,       _A				:str=False					)->						Dict:
			UpperCamelCase__   =    []
			for i in range(image_processor_tester.num_frames					):
						video.append(np.random.randint(2_55			,       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
						UpperCamelCase__   =    [Image.fromarray(np.moveaxis(_lowerCamelCase			,       0			,       -1					)					) for frame in video]

			if torchify:
						UpperCamelCase__   =    [torch.from_numpy(_lowerCamelCase					) for frame in video]

			return video
def     UpperCamelCase_(			_A				:List[str]			,       _A				:Any=False			,       _A				:int=False			,       _A				:Optional[int]=False					)->						Union[str, Any]:
			assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

			return video_inputs







class     lowerCamelCase__		:






			"""simple docstring"""






			_UpperCamelCase						:   Tuple					=						None




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.image_processing_class(**self.image_processor_dict       )
						UpperCamelCase__   =    json.loads(image_processor.to_json_string()       )
						for key, value in self.image_processor_dict.items():
									self.assertEqual(obj[key] ,  __SCREAMING_SNAKE_CASE       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.image_processing_class(**self.image_processor_dict       )

						with tempfile.TemporaryDirectory() as tmpdirname:
									UpperCamelCase__   =    os.path.join(__SCREAMING_SNAKE_CASE ,  "image_processor.json"       )
									image_processor_first.to_json_file(__SCREAMING_SNAKE_CASE       )
									UpperCamelCase__   =    self.image_processing_class.from_json_file(__SCREAMING_SNAKE_CASE       )

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




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.image_processing_class(**self.image_processor_dict       )

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

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




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.image_processing_class()
						self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )







			@require_torch
			@require_vision
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						if self.test_cast_dtype is not None:
									# Initialize image_processor
									UpperCamelCase__   =    self.image_processing_class(**self.image_processor_dict       )

									# create random PyTorch tensors
									UpperCamelCase__   =    prepare_image_inputs(self.image_processor_tester ,  equal_resolution=__SCREAMING_SNAKE_CASE ,  torchify=__SCREAMING_SNAKE_CASE       )

									UpperCamelCase__   =    image_processor(__SCREAMING_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       )

									UpperCamelCase__   =    image_processor(__SCREAMING_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       )

									UpperCamelCase__   =    image_processor(__SCREAMING_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(__SCREAMING_SNAKE_CASE       ):
												UpperCamelCase__   =    image_processor(__SCREAMING_SNAKE_CASE ,  return_tensors="pt"       ).to(torch.bfloataa ,  "cpu"       )

									# Try with text + image feature
									UpperCamelCase__   =    image_processor(__SCREAMING_SNAKE_CASE ,  return_tensors="pt"       )
									encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]       )}       )
									UpperCamelCase__   =    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_(			_A				:list					)->						int | float:
			UpperCamelCase__   =    sorted(_A					)
			UpperCamelCase__   =    len(_A					)
			UpperCamelCase__   =    length >> 1
			return (
			    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
			    if length % 2 == 0
			    else sorted_list[mid_index]
			)
def     UpperCamelCase_(			)->						List[Any]:
			import doctest

			doctest.testmod()


if __name__ == "__main__":
						main()


