


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 			lowercase_				(lowercase__      ):
				snake_case  						=CONFIG_NAME





				@register_to_config
				def __init__(							self					,     lowercase_ = True					,     lowercase_ = 8					,     lowercase_ = "lanczos"					,     lowercase_ = True					,     lowercase_ = False					,     )     ->   Any:
											super().__init__()





				@staticmethod
				def 	__UpperCamelCase						(							lowercase_)     ->   PIL.Image.Image:
											if images.ndim == 3:
																		a__     						=images[None, ...]
											a__     						=(images * 255).round().astype('uint8')
											if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		a__     						=[Image.fromarray(image.squeeze()					,     mode='L') for image in images]
											else:
																		a__     						=[Image.fromarray(lowercase_) for image in images]

											return pil_images





				@staticmethod
				def 	__UpperCamelCase						(							lowercase_)     ->   np.ndarray:
											if not isinstance(lowercase_					,     lowercase_):
																		a__     						=[images]
											a__     						=[np.array(lowercase_).astype(np.floataa) / 2_55.0 for image in images]
											a__     						=np.stack(lowercase_					,     axis=0)

											return images





				@staticmethod
				def 	__UpperCamelCase						(							lowercase_)     ->   torch.FloatTensor:
											if images.ndim == 3:
																		a__     						=images[..., None]

											a__     						=torch.from_numpy(images.transpose(0					,     3					,     1					,     2))
											return images





				@staticmethod
				def 	__UpperCamelCase						(							lowercase_)     ->   np.ndarray:
											a__     						=images.cpu().permute(0					,     2					,     3					,     1).float().numpy()
											return images





				@staticmethod
				def 	__UpperCamelCase						(							lowercase_)     ->   Union[str, Any]:
											return 2.0 * images - 1.0





				@staticmethod
				def 	__UpperCamelCase						(							lowercase_)     ->   Union[str, Any]:
											return (images / 2 + 0.5).clamp(0					,     1)





				@staticmethod
				def 	__UpperCamelCase						(							lowercase_)     ->   PIL.Image.Image:
											a__     						=image.convert('RGB')
											return image





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = None					,     lowercase_ = None					,     )     ->   PIL.Image.Image:
											if height is None:
																		a__     						=image.height
											if width is None:
																		a__     						=image.width

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





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = None					,     lowercase_ = None					,     )     ->   torch.Tensor:
											a__     						=(PIL.Image.Image, np.ndarray, torch.Tensor)
											if isinstance(lowercase_					,     lowercase_):
																		a__     						=[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:
																									a__     						=[self.convert_to_rgb(lowercase_) for i in image]
																		if self.config.do_resize:
																									a__     						=[self.resize(lowercase_					,     lowercase_					,     lowercase_) for i in image]
																		a__     						=self.pil_to_numpy(lowercase_)  # to np
																		a__     						=self.numpy_to_pt(lowercase_)  # to pt

											elif isinstance(image[0]					,     np.ndarray):
																		a__     						=np.concatenate(lowercase_					,     axis=0) if image[0].ndim == 4 else np.stack(lowercase_					,     axis=0)
																		a__     						=self.numpy_to_pt(lowercase_)
																		a__						,    a__						,    a__						,    a__     						=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):
																		a__     						=torch.cat(lowercase_					,     axis=0) if image[0].ndim == 4 else torch.stack(lowercase_					,     axis=0)
																		a__						,    a__						,    a__						,    a__     						=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]
											a__     						=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_					,     )
																		a__     						=False

											if do_normalize:
																		a__     						=self.normalize(lowercase_)

											return image





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = "pil"					,     lowercase_ = None					,     )     ->   Any:
											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"]:
																		a__     						=(
																		    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_)
																		a__     						='np'

											if output_type == "latent":
																		return image

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

											a__     						=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

											a__     						=self.pt_to_numpy(lowercase_)

											if output_type == "np":
																		return image

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







class 			lowercase_				(lowercase__      ):
				snake_case  						=CONFIG_NAME





				@register_to_config
				def __init__(							self					,     lowercase_ = True					,     lowercase_ = 8					,     lowercase_ = "lanczos"					,     lowercase_ = True					,     )     ->   Tuple:
											super().__init__()





				@staticmethod
				def 	__UpperCamelCase						(							lowercase_)     ->   str:
											if images.ndim == 3:
																		a__     						=images[None, ...]
											a__     						=(images * 255).round().astype('uint8')
											if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		a__     						=[Image.fromarray(image.squeeze()					,     mode='L') for image in images]
											else:
																		a__     						=[Image.fromarray(image[:, :, :3]) for image in images]

											return pil_images





				@staticmethod
				def 	__UpperCamelCase						(							lowercase_)     ->   Union[str, Any]:
											return image[:, :, 1] * 2**8 + image[:, :, 2]





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Union[str, Any]:
											if images.ndim == 3:
																		a__     						=images[None, ...]
											a__     						=images[:, :, :, 3:]
											if images.shape[-1] == 6:
																		a__     						=(images_depth * 255).round().astype('uint8')
																		a__     						=[
																		    Image.fromarray(self.rgblike_to_depthmap(lowercase_)					,     mode='I;16') for image_depth in images_depth
																		]
											elif images.shape[-1] == 4:
																		a__     						=(images_depth * 6_55_35.0).astype(np.uintaa)
																		a__     						=[Image.fromarray(lowercase_					,     mode='I;16') for image_depth in images_depth]
											else:
																		raise Exception('Not supported')

											return pil_images





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = "pil"					,     lowercase_ = None					,     )     ->   Any:
											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"]:
																		a__     						=(
																		    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_)
																		a__     						='np'

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

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

											a__     						=self.pt_to_numpy(lowercase_)

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





_lowerCAmelCase:	List[str]      							=					[0.33, 0.44, 0.55, 0.44, 0.33]
_lowerCAmelCase:	Union[str, Any]      							=					3
_lowerCAmelCase:	Dict      							=					5

# Buffer Space (with Parameters Space)
_lowerCAmelCase:	list[float]      							=					[]
_lowerCAmelCase:	list[float]      							=					[]

# Machine Time
_lowerCAmelCase:	Optional[Any]      							=					0






def      _lowercase(				__a							:	Optional[Any]    ):
							global buffer_space, params_space, machine_time, K, m, t

							# Choosing Dynamical Systems (All)
							for key, value in enumerate(__a    ):
														# Evolution Parameter
														a__     						=float(seed / value    )

														# Control Theory: Orbit Change
														a__     						=(buffer_space[(key + 1) % m] + e) % 1

														# Control Theory: Trajectory Change
														a__     						=(params_space[key] + e) % 1 + 3

														# Modification (Transition Function) - Jumps
														a__     						=round(float(r * value * (1 - value)    )						,    10    )
														a__     						=r  # Saving to Parameters Space

							# Logistic Map
							assert max(__a    ) < 1
							assert max(__a    ) < 4

							# Machine Time
							machine_time += 1






def      _lowercase(				):
							global buffer_space, params_space, machine_time, K, m, t

							# PRNG (Xorshift by George Marsaglia)
							def xorshift(__a							:	str						,    __a							:	Dict    ):
														x ^= y >> 13
														y ^= x << 17
														x ^= y >> 5
														return x

							# Choosing Dynamical Systems (Increment)
							a__     						=machine_time % m

							# Evolution (Time Length)
							for _ in range(0						,    __a    ):
														# Variables (Position + Parameters)
														a__     						=params_space[key]
														a__     						=buffer_space[key]

														# Modification (Transition Function) - Flow
														a__     						=round(float(r * value * (1 - value)    )						,    10    )
														a__     						=(machine_time * 0.01 + r * 1.01) % 1 + 3

							# Choosing Chaotic Data
							a__     						=int(buffer_space[(key + 2) % m] * (10**10)    )
							a__     						=int(buffer_space[(key - 2) % m] * (10**10)    )

							# Machine Time
							machine_time += 1

							return xorshift(__a						,    __a    ) % 0Xffff_ffff






def      _lowercase(				):
							global buffer_space, params_space, machine_time, K, m, t

							a__     						=K
							a__     						=[0] * m
							a__     						=0


if __name__ == "__main__":
	# Initialization
	reset()

	# Pushing Data (Input)
	import random

	_lowerCAmelCase:	int      							=					random.sample(range(0xff_fff_fff), 100)
	for chunk in message:
		push(chunk)

	# for controlling
	_lowerCAmelCase:	List[Any]      							=					''

	# Pulling Data (Output)
	while inp in ("e", "E"):
		print(F"""{format(pull(), '#04x')}""")
		print(buffer_space)
		print(params_space)
		_lowerCAmelCase:	str      							=					input('(e)exit? ').strip()

