


'''simple docstring'''


import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate






class 						lowerCAmelCase__      (      _snake_case						):
			lowerCAmelCase			:						Optional[int]							 =  CONFIG_NAME




			@register_to_config
			def __init__(		self	:  Optional[Any]						,	lowerCamelCase__	:  bool = True						,	lowerCamelCase__	:  int = 8						,	lowerCamelCase__	:  str = "lanczos"						,	lowerCamelCase__	:  bool = True						,	lowerCamelCase__	:  bool = False						,	) ->Dict:



					'''simple docstring'''







					super().__init__()




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  np.ndarray			) ->PIL.Image.Image:



					'''simple docstring'''







					if images.ndim == 3:
							_UpperCAmelCase		:							Optional[int]             =   images[None, ...]
					_UpperCAmelCase		:							Optional[int]             =   (images * 2_55).round().astype("uint8"			)
					if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							_UpperCAmelCase		:							Dict             =   [Image.fromarray(image.squeeze()						,	mode="L"			) for image in images]
					else:
							_UpperCAmelCase		:							List[Any]             =   [Image.fromarray(lowercase__			) for image in images]

					return pil_images




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Union[List[PIL.Image.Image], PIL.Image.Image]			) ->np.ndarray:



					'''simple docstring'''







					if not isinstance(lowercase__						,	lowercase__			):
							_UpperCAmelCase		:							Tuple             =   [images]
					_UpperCAmelCase		:							Tuple             =   [np.array(lowercase__			).astype(np.floataa			) / 2_5_5.0 for image in images]
					_UpperCAmelCase		:							int             =   np.stack(lowercase__						,	axis=0			)

					return images




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  np.ndarray			) ->torch.FloatTensor:



					'''simple docstring'''







					if images.ndim == 3:
							_UpperCAmelCase		:							str             =   images[..., None]

					_UpperCAmelCase		:							Any             =   torch.from_numpy(images.transpose(0						,	3						,	1						,	2			)			)
					return images




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  torch.FloatTensor			) ->np.ndarray:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   images.cpu().permute(0						,	2						,	3						,	1			).float().numpy()
					return images




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[Any]			) ->Optional[int]:



					'''simple docstring'''







					return 2.0 * images - 1.0




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  List[Any]			) ->Optional[int]:



					'''simple docstring'''







					return (images / 2 + 0.5).clamp(0						,	1			)




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  PIL.Image.Image			) ->PIL.Image.Image:



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   image.convert("RGB"			)
					return image




			def 				lowerCAmelCase__     (		self	:  Tuple						,	lowerCamelCase__	:  PIL.Image.Image						,	lowerCamelCase__	:  Optional[int] = None						,	lowerCamelCase__	:  Optional[int] = None						,	) ->PIL.Image.Image:



					'''simple docstring'''







					if height is None:
							_UpperCAmelCase		:							Tuple             =   image.height
					if width is None:
							_UpperCAmelCase		:							List[Any]             =   image.width

					_UpperCAmelCase       ,     _UpperCAmelCase		:							List[str]             =   (
					    x - x % self.config.vae_scale_factor for x in (width, height)
					)  # resize to integer multiple of vae_scale_factor
					_UpperCAmelCase		:							str             =   image.resize((width, height)						,	resample=PIL_INTERPOLATION[self.config.resample]			)
					return image




			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]						,	lowerCamelCase__	:  Optional[int] = None						,	lowerCamelCase__	:  Optional[int] = None						,	) ->torch.Tensor:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   (PIL.Image.Image, np.ndarray, torch.Tensor)
					if isinstance(lowercase__						,	lowercase__			):
							_UpperCAmelCase		:							List[str]             =   [image]
					elif not (isinstance(lowercase__						,	lowercase__			) and all(isinstance(lowercase__						,	lowercase__			) for i in image			)):
							raise ValueError(
							    F"""Input is in incorrect format: {[type(lowercase__			) for i in image]}. Currently, we only support {', '.join(lowercase__			)}"""			)

					if isinstance(image[0]						,	PIL.Image.Image			):
							if self.config.do_convert_rgb:
									_UpperCAmelCase		:							List[Any]             =   [self.convert_to_rgb(lowercase__			) for i in image]
							if self.config.do_resize:
									_UpperCAmelCase		:							str             =   [self.resize(lowercase__						,	lowercase__						,	lowercase__			) for i in image]
							_UpperCAmelCase		:							Optional[Any]             =   self.pil_to_numpy(lowercase__			)  # to np
							_UpperCAmelCase		:							Dict             =   self.numpy_to_pt(lowercase__			)  # to pt

					elif isinstance(image[0]						,	np.ndarray			):
							_UpperCAmelCase		:							Optional[int]             =   np.concatenate(lowercase__						,	axis=0			) if image[0].ndim == 4 else np.stack(lowercase__						,	axis=0			)
							_UpperCAmelCase		:							str             =   self.numpy_to_pt(lowercase__			)
							_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							Any             =   image.shape
							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
									raise ValueError(
									    F"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
									    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""			)

					elif isinstance(image[0]						,	torch.Tensor			):
							_UpperCAmelCase		:							Tuple             =   torch.cat(lowercase__						,	axis=0			) if image[0].ndim == 4 else torch.stack(lowercase__						,	axis=0			)
							_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							int             =   image.shape

							# don't need any preprocess if the image is latents
							if channel == 4:
									return image

							if self.config.do_resize and (
							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
							):
									raise ValueError(
									    F"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
									    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""			)

        # expected range [0,1], normalize to [-1,1]
					_UpperCAmelCase		:							Any             =   self.config.do_normalize
					if image.min() < 0:
							warnings.warn(
							    "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
							    F"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""						,	lowercase__						,	)
							_UpperCAmelCase		:							Tuple             =   False

					if do_normalize:
							_UpperCAmelCase		:							List[str]             =   self.normalize(lowercase__			)

					return image






			def 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  torch.FloatTensor						,	lowerCamelCase__	:  str = "pil"						,	lowerCamelCase__	:  Optional[List[bool]] = None						,	) ->List[Any]:



					'''simple docstring'''







					if not isinstance(lowercase__						,	torch.Tensor			):
							raise ValueError(
							    F"""Input for postprocessing is in incorrect format: {type(lowercase__			)}. We only support pytorch tensor"""			)
					if output_type not in ["latent", "pt", "np", "pil"]:
							_UpperCAmelCase		:							Optional[int]             =   (
							    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
							    "`pil`, `np`, `pt`, `latent`"
							)
							deprecate("Unsupported output_type"						,	"1.0.0"						,	lowercase__						,	standard_warn=lowercase__			)
							_UpperCAmelCase		:							Tuple             =   "np"

					if output_type == "latent":
							return image

					if do_denormalize is None:
							_UpperCAmelCase		:							Optional[int]             =   [self.config.do_normalize] * image.shape[0]

					_UpperCAmelCase		:							List[Any]             =   torch.stack(
					    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

					if output_type == "pt":
							return image

					_UpperCAmelCase		:							Dict             =   self.pt_to_numpy(lowercase__			)

					if output_type == "np":
							return image

					if output_type == "pil":
							return self.numpy_to_pil(lowercase__			)






class 						lowerCAmelCase__      (      _snake_case						):
			lowerCAmelCase			:						int							 =  CONFIG_NAME




			@register_to_config
			def __init__(		self	:  Any						,	lowerCamelCase__	:  bool = True						,	lowerCamelCase__	:  int = 8						,	lowerCamelCase__	:  str = "lanczos"						,	lowerCamelCase__	:  bool = True						,	) ->Any:



					'''simple docstring'''







					super().__init__()




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  List[str]			) ->List[Any]:



					'''simple docstring'''







					if images.ndim == 3:
							_UpperCAmelCase		:							Dict             =   images[None, ...]
					_UpperCAmelCase		:							Dict             =   (images * 2_55).round().astype("uint8"			)
					if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							_UpperCAmelCase		:							str             =   [Image.fromarray(image.squeeze()						,	mode="L"			) for image in images]
					else:
							_UpperCAmelCase		:							List[str]             =   [Image.fromarray(image[:, :, :3]			) for image in images]

					return pil_images




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Any			) ->List[str]:



					'''simple docstring'''







					return image[:, :, 1] * 2**8 + image[:, :, 2]




			def 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  List[str]			) ->int:



					'''simple docstring'''







					if images.ndim == 3:
							_UpperCAmelCase		:							int             =   images[None, ...]
					_UpperCAmelCase		:							str             =   images[:, :, :, 3:]
					if images.shape[-1] == 6:
							_UpperCAmelCase		:							Tuple             =   (images_depth * 2_55).round().astype("uint8"			)
							_UpperCAmelCase		:							Optional[int]             =   [
							    Image.fromarray(self.rgblike_to_depthmap(lowercase__			)						,	mode="I;16"			) for image_depth in images_depth
							]
					elif images.shape[-1] == 4:
							_UpperCAmelCase		:							Tuple             =   (images_depth * 6_5_5_3_5.0).astype(np.uintaa			)
							_UpperCAmelCase		:							Optional[int]             =   [Image.fromarray(lowercase__						,	mode="I;16"			) for image_depth in images_depth]
					else:
							raise Exception("Not supported"			)

					return pil_images






			def 				lowerCAmelCase__     (		self	:  Optional[Any]						,	lowerCamelCase__	:  torch.FloatTensor						,	lowerCamelCase__	:  str = "pil"						,	lowerCamelCase__	:  Optional[List[bool]] = None						,	) ->List[Any]:



					'''simple docstring'''







					if not isinstance(lowercase__						,	torch.Tensor			):
							raise ValueError(
							    F"""Input for postprocessing is in incorrect format: {type(lowercase__			)}. We only support pytorch tensor"""			)
					if output_type not in ["latent", "pt", "np", "pil"]:
							_UpperCAmelCase		:							List[str]             =   (
							    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
							    "`pil`, `np`, `pt`, `latent`"
							)
							deprecate("Unsupported output_type"						,	"1.0.0"						,	lowercase__						,	standard_warn=lowercase__			)
							_UpperCAmelCase		:							Union[str, Any]             =   "np"

					if do_denormalize is None:
							_UpperCAmelCase		:							str             =   [self.config.do_normalize] * image.shape[0]

					_UpperCAmelCase		:							Tuple             =   torch.stack(
					    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

					_UpperCAmelCase		:							Dict             =   self.pt_to_numpy(lowercase__			)

					if output_type == "np":
							if image.shape[-1] == 6:
									_UpperCAmelCase		:							Any             =   np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]						,	axis=0			)
							else:
									_UpperCAmelCase		:							Optional[Any]             =   image[:, :, :, 3:]
							return image[:, :, :, :3], image_depth

					if output_type == "pil":
							return self.numpy_to_pil(lowercase__			), self.numpy_to_depth(lowercase__			)
					else:
							raise Exception(F"""This type {output_type} is not supported"""			)






'''simple docstring'''







from .balanced_parentheses import balanced_parentheses
from .stack import Stack






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(snake_case_							,       -1	)






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


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))




