



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__							(       __lowercase				,  __lowercase=False				,  __lowercase=False				,  __lowercase=False							)		->		List[Any]:







						"""simple docstring"""

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

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

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

						return image_inputs
def 						snake_case__							(       __lowercase				,  __lowercase=1_0				,  __lowercase=1_0				,  __lowercase=False				,  __lowercase=False							)		->		List[Any]:







						"""simple docstring"""

						A__						:				Any         =			[]
						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
												A__						:				List[Any]         =			[Image.fromarray(np.moveaxis(__lowercase				,  0				,  -1							)							) for frame in video]

						if torchify:
												A__						:				Tuple         =			[torch.from_numpy(__lowercase							) for frame in video]

						return video
def 						snake_case__							(       __lowercase				,  __lowercase=False				,  __lowercase=False				,  __lowercase=False							)		->		Union[str, Any]:







						"""simple docstring"""

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

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

						return video_inputs




class 				lowerCAmelCase__	:
					__A  :     Optional[Any]     =    None







					def 			_lowercase      (							self   :  List[str]):
											A__						:				Optional[Any]         =			self.image_processing_class(**self.image_processor_dict)
											A__						:				Dict         =			json.loads(image_processor.to_json_string())
											for key, value in self.image_processor_dict.items():
																	self.assertEqual(obj[key]      ,		lowercase_)







					def 			_lowercase      (							self   :  int):
											A__						:				Union[str, Any]         =			self.image_processing_class(**self.image_processor_dict)

											with tempfile.TemporaryDirectory() as tmpdirname:
																	A__						:				List[Any]         =			os.path.join(lowercase_      ,		"image_processor.json")
																	image_processor_first.to_json_file(lowercase_)
																	A__						:				Dict         =			self.image_processing_class.from_json_file(lowercase_)

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







					def 			_lowercase      (							self   :  Dict):
											A__						:				int         =			self.image_processing_class(**self.image_processor_dict)

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

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







					def 			_lowercase      (							self   :  int):
											A__						:				int         =			self.image_processing_class()
											self.assertIsNotNone(lowercase_)







					@require_torch
					@require_vision
					def 			_lowercase      (							self   :  int):
											if self.test_cast_dtype is not None:
																	# Initialize image_processor
																	A__						:				str         =			self.image_processing_class(**self.image_processor_dict)

																	# create random PyTorch tensors
																	A__						:				List[Any]         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=lowercase_      ,		torchify=lowercase_)

																	A__						:				List[Any]         =			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)

																	A__						:				int         =			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)

																	A__						:				Optional[int]         =			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_):
																							A__						:				Optional[int]         =			image_processor(lowercase_      ,		return_tensors="pt").to(torch.bfloataa      ,		"cpu")

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


'''simple docstring'''

from .balanced_parentheses import balanced_parentheses
from .stack import Stack

def      __magic_name__							(			A				)      ->		int:
	return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(A       , -1				)

def      __magic_name__							(			A				)      ->		str:
	if not balanced_parentheses(A				):
		raise ValueError('Mismatched parentheses'				)
	snake_case							=					Stack()
	snake_case							=					[]
	for char in expression_str:
		if char.isalpha() or char.isdigit():
			postfix.append(A				)
		elif char == "(":
			stack.push(A				)
		elif char == ")":
			while not stack.is_empty() and stack.peek() != "(":
				postfix.append(stack.pop()				)
			stack.pop()
		else:
			while not stack.is_empty() and precedence(A				) <= precedence(stack.peek()				):
				postfix.append(stack.pop()				)
			stack.push(A				)
	while not stack.is_empty():
		postfix.append(stack.pop()				)
	return " ".join(A				)


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

						testmod()
						lowerCAmelCase_     =      "a+b*(c^d-e)^(f+g*h)-i"

						print("Infix to Postfix Notation demonstration:\n")
						print("Infix notation: " + expression)
						print("Postfix notation: " + infix_to_postfix(expression))





