




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 				__snake_case     (_a						):
				lowerCAmelCase__			=   CONFIG_NAME



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





								'''simple docstring'''




								super().__init__()



				@staticmethod
				def 					SCREAMING_SNAKE_CASE				(					_UpperCAmelCase					:						np.ndarray					)     ->			PIL.Image.Image:





								'''simple docstring'''




								if images.ndim == 3:
												_lowerCAmelCase			:			int        =					images[None, ...]
								_lowerCAmelCase			:			Dict        =					(images * 255).round().astype("""uint8"""					)
								if images.shape[-1] == 1:
												# special case for grayscale (single channel) images
												_lowerCAmelCase			:			Tuple        =					[Image.fromarray(image.squeeze()						,		mode="""L"""					) for image in images]
								else:
												_lowerCAmelCase			:			Any        =					[Image.fromarray(_UpperCAmelCase					) for image in images]

								return pil_images



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





								'''simple docstring'''




								if not isinstance(_UpperCAmelCase						,		_UpperCAmelCase					):
												_lowerCAmelCase			:			Tuple        =					[images]
								_lowerCAmelCase			:			List[str]        =					[np.array(_UpperCAmelCase					).astype(np.floataa					) / 255.0 for image in images]
								_lowerCAmelCase			:			str        =					np.stack(_UpperCAmelCase						,		axis=0					)

								return images



				@staticmethod
				def 					SCREAMING_SNAKE_CASE				(					_UpperCAmelCase					:						np.ndarray					)     ->			torch.FloatTensor:





								'''simple docstring'''




								if images.ndim == 3:
												_lowerCAmelCase			:			List[Any]        =					images[..., None]

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



				@staticmethod
				def 					SCREAMING_SNAKE_CASE				(					_UpperCAmelCase					:						torch.FloatTensor					)     ->			np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					images.cpu().permute(0						,		2						,		3						,		1					).float().numpy()
								return images



				@staticmethod
				def 					SCREAMING_SNAKE_CASE				(					_UpperCAmelCase					:						List[Any]					)     ->			List[Any]:





								'''simple docstring'''




								return 2.0 * images - 1.0



				@staticmethod
				def 					SCREAMING_SNAKE_CASE				(					_UpperCAmelCase					:						Any					)     ->			List[str]:





								'''simple docstring'''




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



				@staticmethod
				def 					SCREAMING_SNAKE_CASE				(					_UpperCAmelCase					:						PIL.Image.Image					)     ->			PIL.Image.Image:





								'''simple docstring'''




								_lowerCAmelCase			:			Tuple        =					image.convert("""RGB"""					)
								return image



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





								'''simple docstring'''




								if height is None:
												_lowerCAmelCase			:			int        =					image.height
								if width is None:
												_lowerCAmelCase			:			int        =					image.width

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



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





								'''simple docstring'''




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

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

								elif isinstance(image[0]						,		np.ndarray					):
												_lowerCAmelCase			:			int        =					np.concatenate(_UpperCAmelCase						,		axis=0					) if image[0].ndim == 4 else np.stack(_UpperCAmelCase						,		axis=0					)
												_lowerCAmelCase			:			Any        =					self.numpy_to_pt(_UpperCAmelCase					)
												_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Dict        =					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					):
												_lowerCAmelCase			:			Union[str, Any]        =					torch.cat(_UpperCAmelCase						,		axis=0					) if image[0].ndim == 4 else torch.stack(_UpperCAmelCase						,		axis=0					)
												_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Any        =					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]
								_lowerCAmelCase			:			Dict        =					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()}]"						,		_UpperCAmelCase						,		)
												_lowerCAmelCase			:			Union[str, Any]        =					False

								if do_normalize:
												_lowerCAmelCase			:			Optional[int]        =					self.normalize(_UpperCAmelCase					)

								return image



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





								'''simple docstring'''




								if not isinstance(_UpperCAmelCase						,		torch.Tensor					):
												raise ValueError(
												    f"Input for postprocessing is in incorrect format: {type(_UpperCAmelCase					)}. We only support pytorch tensor"					)
								if output_type not in ["latent", "pt", "np", "pil"]:
												_lowerCAmelCase			:			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"""						,		_UpperCAmelCase						,		standard_warn=_UpperCAmelCase					)
												_lowerCAmelCase			:			List[str]        =					"""np"""

								if output_type == "latent":
												return image

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

								_lowerCAmelCase			:			Tuple        =					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

								_lowerCAmelCase			:			Union[str, Any]        =					self.pt_to_numpy(_UpperCAmelCase					)

								if output_type == "np":
												return image

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






class 				__snake_case     (_a						):
				lowerCAmelCase__			=   CONFIG_NAME



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





								'''simple docstring'''




								super().__init__()



				@staticmethod
				def 					SCREAMING_SNAKE_CASE				(					_UpperCAmelCase					:						Any					)     ->			Any:





								'''simple docstring'''




								if images.ndim == 3:
												_lowerCAmelCase			:			Dict        =					images[None, ...]
								_lowerCAmelCase			:			Any        =					(images * 255).round().astype("""uint8"""					)
								if images.shape[-1] == 1:
												# special case for grayscale (single channel) images
												_lowerCAmelCase			:			List[str]        =					[Image.fromarray(image.squeeze()						,		mode="""L"""					) for image in images]
								else:
												_lowerCAmelCase			:			Dict        =					[Image.fromarray(image[:, :, :3]					) for image in images]

								return pil_images



				@staticmethod
				def 					SCREAMING_SNAKE_CASE				(					_UpperCAmelCase					:						Dict					)     ->			Any:





								'''simple docstring'''




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



				def 					SCREAMING_SNAKE_CASE				(					self					:						int						,		_UpperCAmelCase					:						Optional[int]					)     ->			Any:





								'''simple docstring'''




								if images.ndim == 3:
												_lowerCAmelCase			:			Union[str, Any]        =					images[None, ...]
								_lowerCAmelCase			:			Dict        =					images[:, :, :, 3:]
								if images.shape[-1] == 6:
												_lowerCAmelCase			:			Any        =					(images_depth * 255).round().astype("""uint8"""					)
												_lowerCAmelCase			:			str        =					[
												    Image.fromarray(self.rgblike_to_depthmap(_UpperCAmelCase					)						,		mode="""I;16"""					) for image_depth in images_depth
												]
								elif images.shape[-1] == 4:
												_lowerCAmelCase			:			Optional[int]        =					(images_depth * 65535.0).astype(np.uintaa					)
												_lowerCAmelCase			:			int        =					[Image.fromarray(_UpperCAmelCase						,		mode="""I;16"""					) for image_depth in images_depth]
								else:
												raise Exception("""Not supported"""					)

								return pil_images



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						torch.FloatTensor						,		_UpperCAmelCase					:						str = "pil"						,		_UpperCAmelCase					:						Optional[List[bool]] = None						,		)     ->			str:





								'''simple docstring'''




								if not isinstance(_UpperCAmelCase						,		torch.Tensor					):
												raise ValueError(
												    f"Input for postprocessing is in incorrect format: {type(_UpperCAmelCase					)}. We only support pytorch tensor"					)
								if output_type not in ["latent", "pt", "np", "pil"]:
												_lowerCAmelCase			:			List[Any]        =					(
												    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"""						,		_UpperCAmelCase						,		standard_warn=_UpperCAmelCase					)
												_lowerCAmelCase			:			Any        =					"""np"""

								if do_denormalize is None:
												_lowerCAmelCase			:			List[Any]        =					[self.config.do_normalize] * image.shape[0]

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

								_lowerCAmelCase			:			Optional[Any]        =					self.pt_to_numpy(_UpperCAmelCase					)

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

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







def 							_UpperCAmelCase     (UpperCamelCase_       :       int					,			UpperCamelCase_       :       Optional[int]				):
				'''simple docstring'''
				_lowerCAmelCase			:			Dict        =					len(UpperCamelCase_				)
				# Create a stack and push index of fist element to it
				_lowerCAmelCase			:			Tuple        =					[]
				st.append(0				)

				# Span value of first element is always 1
				_lowerCAmelCase			:			List[Any]        =					1

				# Calculate span values for rest of the elements
				for i in range(1					,			UpperCamelCase_				):
								# Pop elements from stack while stack is not
								# empty and top of stack is smaller than price[i]
								while len(UpperCamelCase_				) > 0 and price[st[0]] <= price[i]:
												st.pop()

								# If stack becomes empty, then price[i] is greater
								# than all elements on left of it, i.e. price[0],
								# price[1], ..price[i-1]. Else the price[i]  is
								# greater than elements after top of stack
								_lowerCAmelCase			:			Union[str, Any]        =					i + 1 if len(UpperCamelCase_				) <= 0 else (i - st[0])

								# Push this element to stack
								st.append(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       Union[str, Any]				):
				'''simple docstring'''
				for i in range(0					,			UpperCamelCase_				):
								print(arr[i]					,			end=""" """				)


# Driver program to test above function
_lowerCamelCase					:     Union[str, Any]					  =  [1_0, 4, 5, 9_0, 1_2_0, 8_0]
_lowerCamelCase					:     List[Any]					  =  [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)





print_array(S, len(price))

