'''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     snake_case__							(      _A: int				,       _A: Any=False				,       _A: str=False				,       _A: Optional[int]=False			)   ->     Dict:

		'''simple docstring'''


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

		lowerCAmelCase        =  []
		for i in range(image_processor_tester.batch_size			):
				if equal_resolution:
						lowerCAmelCase        =  lowerCAmelCase        =  image_processor_tester.max_resolution
				else:
						# To avoid getting image width/height 0
						lowerCAmelCase        =  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`
								lowerCAmelCase        =  max(image_processor_tester.size_divisor				,       _A			)
						lowerCAmelCase ,					lowerCAmelCase        =  np.random.choice(np.arange(_A				,       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
				lowerCAmelCase        =  [Image.fromarray(np.moveaxis(_A				,       0				,       -1			)			) for image in image_inputs]

		if torchify:
				lowerCAmelCase        =  [torch.from_numpy(_A			) for image in image_inputs]

		return image_inputs






def     snake_case__							(      _A: Optional[int]				,       _A: Tuple=10				,       _A: List[Any]=10				,       _A: Optional[Any]=False				,       _A: List[Any]=False			)   ->     Optional[int]:

		'''simple docstring'''


		lowerCAmelCase        =  []
		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
				lowerCAmelCase        =  [Image.fromarray(np.moveaxis(_A				,       0				,       -1			)			) for frame in video]

		if torchify:
				lowerCAmelCase        =  [torch.from_numpy(_A			) for frame in video]

		return video






def     snake_case__							(      _A: Dict				,       _A: int=False				,       _A: Any=False				,       _A: List[Any]=False			)   ->     Union[str, Any]:

		'''simple docstring'''


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

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

		return video_inputs






class     a__:


			'''simple docstring'''






			UpperCAmelCase_      :					str								=	None



			def 							a_	(       self):




					"""simple docstring"""


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



			def 							a_	(       self):




					"""simple docstring"""


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

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

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



			def 							a_	(       self):




					"""simple docstring"""


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

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

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



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.image_processing_class()
					self.assertIsNotNone(__lowerCAmelCase)







			@require_torch
			@require_vision
			def 							a_	(       self):




					"""simple docstring"""


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

							# create random PyTorch tensors
							lowerCAmelCase        =  prepare_image_inputs(self.image_processor_tester	,   equal_resolution=__lowerCAmelCase	,   torchify=__lowerCAmelCase)

							lowerCAmelCase        =  image_processor(__lowerCAmelCase	,   return_tensors="""pt""")
							# for layoutLM compatiblity
							self.assertEqual(encoding.pixel_values.device	,   torch.device("""cpu"""))
							self.assertEqual(encoding.pixel_values.dtype	,   torch.floataa)

							lowerCAmelCase        =  image_processor(__lowerCAmelCase	,   return_tensors="""pt""").to(torch.floataa)
							self.assertEqual(encoding.pixel_values.device	,   torch.device("""cpu"""))
							self.assertEqual(encoding.pixel_values.dtype	,   torch.floataa)

							lowerCAmelCase        =  image_processor(__lowerCAmelCase	,   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(__lowerCAmelCase):
									lowerCAmelCase        =  image_processor(__lowerCAmelCase	,   return_tensors="""pt""").to(torch.bfloataa	,   """cpu""")

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




from __future__ import annotations

__lowercase				   =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  -1
					lowerCAmelCase        =  -1
					lowerCAmelCase        =  [-1] * length
					lowerCAmelCase        =  None
					lowerCAmelCase        =  None







			def __repr__(       self):




					"""simple docstring"""


					return f"Node(min_value={self.minn} max_value={self.maxx})"







def     snake_case__							(      _A: list[int]			)   ->     Node | None:

		'''simple docstring'''


		lowerCAmelCase        =  Node(len(_A			)			)
		lowerCAmelCase ,					lowerCAmelCase        =  min(_A			), max(_A			)
		# Leaf node case where the node contains only one unique value
		if root.minn == root.maxx:
				return root
		lowerCAmelCase        =  (root.minn + root.maxx) // 2

		lowerCAmelCase        =  []
		lowerCAmelCase        =  []

		for index, num in enumerate(_A			):
				if num <= pivot:
						left_arr.append(_A			)
				else:
						right_arr.append(_A			)
				lowerCAmelCase        =  len(_A			)
		lowerCAmelCase        =  build_tree(_A			)
		lowerCAmelCase        =  build_tree(_A			)
		return root






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if index < 0 or node is None:
				return 0
		# Leaf node cases
		if node.minn == node.maxx:
				return index + 1 if node.minn == num else 0
		lowerCAmelCase        =  (node.minn + node.maxx) // 2
		if num <= pivot:
				# go the left subtree and map index to the left subtree
				return rank_till_index(node.left				,       _A				,       node.map_left[index] - 1			)
		else:
				# go to the right subtree and map index to the right subtree
				return rank_till_index(node.right				,       _A				,       index - node.map_left[index]			)






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if start > end:
				return 0
		lowerCAmelCase        =  rank_till_index(_A				,       _A				,       _A			)
		lowerCAmelCase        =  rank_till_index(_A				,       _A				,       start - 1			)
		return rank_till_end - rank_before_start






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if index > (end - start) or start > end or node is None:
				return -1
		# Leaf node case
		if node.minn == node.maxx:
				return node.minn
		# Number of elements in the left subtree in interval [start, end]
		lowerCAmelCase        =  node.map_left[end] - (
		    node.map_left[start - 1] if start else 0
		)
		if num_elements_in_left_tree > index:
				return quantile(
				    node.left				,       _A				,       (node.map_left[start - 1] if start else 0)				,       node.map_left[end] - 1				,       )
		else:
				return quantile(
				    node.right				,       index - num_elements_in_left_tree				,       start - (node.map_left[start - 1] if start else 0)				,       end - node.map_left[end]				,       )






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if (
		    start > end
		    or node is None
		    or start_num > end_num
		    or node.minn > end_num
		    or node.maxx < start_num
		):
				return 0
		if start_num <= node.minn and node.maxx <= end_num:
				return end - start + 1
		lowerCAmelCase        =  range_counting(
		    node.left				,       (node.map_left[start - 1] if start else 0)				,       node.map_left[end] - 1				,       _A				,       _A				,       )
		lowerCAmelCase        =  range_counting(
		    node.right				,       start - (node.map_left[start - 1] if start else 0)				,       end - node.map_left[end]				,       _A				,       _A				,       )
		return left + right


if __name__ == "__main__":
						import doctest

						doctest.testmod()




