


"""simple docstring"""


from dataclasses import dataclass

a_											=			"0.1"
a_											=			"Lucia Harcekova"
@dataclass
class snake_case					:
		__UpperCamelCase							=	42
		__UpperCamelCase							=	42
		__UpperCamelCase							=	42







		def __repr__(    self					:				Union[str, Any]						)				->  str:





						'''simple docstring'''







						return F"""({self.offset}, {self.length}, {self.indicator})"""





class snake_case					:







		def __init__(    self					:				str			,      a__					:				int = 13			,      a__					:				int = 6						)				->  None:





						'''simple docstring'''







						_A         =	window_size
						_A         =	lookahead_buffer_size
						_A         =	self.window_size - self.lookahead_buffer_size







		def        a_       (    self					:				Optional[Any]			,      a__					:				str						)				->  list[Token]:





						'''simple docstring'''







						_A         =	[]
						_A         =	""

						# while there are still characters in text to compress
						while text:
										# find the next encoding phrase
										# - triplet with offset, length, indicator (the next encoding character)
										_A         =	self._find_encoding_token(a__			,      a__						)

										# update the search buffer:
										# - add new characters from text into it
										# - check if size exceed the max search buffer size, if so, drop the
										#   oldest elements
										search_buffer += text[: token.length + 1]
										if len(a__						) > self.search_buffer_size:
														_A         =	search_buffer[-self.search_buffer_size :]

										# update the text
										_A         =	text[token.length + 1 :]

										# append the token to output
										output.append(a__						)

						return output







		def        a_       (    self					:				int			,      a__					:				list[Token]						)				->  str:





						'''simple docstring'''







						_A         =	""

						for token in tokens:
										for _ in range(token.length						):
														output += output[-token.offset]
										output += token.indicator

						return output







		def        a_       (    self					:				Dict			,      a__					:				str			,      a__					:				str						)				->  Token:





						'''simple docstring'''







						if not text:
										raise ValueError("We need some text to work with."						)

						# Initialise result parameters to default values
						_A     ,						_A         =	0, 0

						if not search_buffer:
										return Token(a__			,      a__			,      text[length]						)

						for i, character in enumerate(a__						):
										_A         =	len(a__						) - i
										if character == text[0]:
														_A         =	self._match_length_from_index(a__			,      a__			,      0			,      a__						)
														# if the found length is bigger than the current or if it's equal,
														# which means it's offset is smaller: update offset and length
														if found_length >= length:
																		_A     ,						_A         =	found_offset, found_length

						return Token(a__			,      a__			,      text[length]						)







		def        a_       (    self					:				Optional[Any]			,      a__					:				str			,      a__					:				str			,      a__					:				int			,      a__					:				int						)				->  int:





						'''simple docstring'''







						if not text or text[text_index] != window[window_index]:
										return 0
						return 1 + self._match_length_from_index(
						    a__			,      window + text[text_index]			,      text_index + 1			,      window_index + 1						)


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

		testmod()
		# Initialize compressor class
		a_											=			LZaaCompressor(window_size=13, lookahead_buffer_size=6)

		# Example
		a_											=			"cabracadabrarrarrad"
		a_											=			lzaa_compressor.compress(TEXT)
		print(lzaa_compressor.compress("ababcbababaa"))
		a_											=			lzaa_compressor.decompress(compressed_text)
		assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
"""simple docstring"""


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class    __UpperCAmelCase						(	__lowerCAmelCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, ):
										super().__init__()

										self.register_modules(vae=_lowerCamelCase	, text_encoder=_lowerCamelCase	, tokenizer=_lowerCamelCase	, unet=_lowerCamelCase	, scheduler=_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase      ):
										with torch.no_grad():
																lowerCamelCase__					=self.vae.encode(tfms.ToTensor()(_lowerCamelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
																lowerCamelCase__					=0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=(1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
																lowerCamelCase__					=self.vae.decode(_lowerCamelCase      ).sample
										lowerCamelCase__					=(img / 2 + 0.5).clamp(0	, 1      )
										lowerCamelCase__					=img.detach().cpu().permute(0	, 2	, 3	, 1      ).numpy()
										lowerCamelCase__					=(img * 255).round().astype("uint8"      )
										return Image.fromarray(img[0]      )

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=self.tokenizer(
										    _lowerCamelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

										lowerCamelCase__					=self.tokenizer(
										    ""	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

										return torch.cat([uncond_embedding, text_embedding]      )






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = 0.3	, _lowerCamelCase = 0.6	, _lowerCamelCase = 0.5	, _lowerCamelCase = 42	, _lowerCamelCase = 50	, _lowerCamelCase = 7.5	, ):
										lowerCamelCase__					=steps - int(kmin * steps      )
										lowerCamelCase__					=steps - int(kmax * steps      )

										lowerCamelCase__					=self.prep_text(_lowerCamelCase      )

										self.scheduler.set_timesteps(_lowerCamelCase      )

										lowerCamelCase__					,      lowerCamelCase__					=img.size
										lowerCamelCase__					=self.encode(_lowerCamelCase      )

										torch.manual_seed(_lowerCamelCase      )
										lowerCamelCase__					=torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)	, ).to(self.device      )

										lowerCamelCase__					=self.scheduler.add_noise(
										    _lowerCamelCase	, _lowerCamelCase	, timesteps=self.scheduler.timesteps[tmax]	, )

										lowerCamelCase__					=torch.cat([latents] * 2      )

										lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, self.scheduler.timesteps[tmax]      )

										with torch.no_grad():
																lowerCamelCase__					=self.unet(
																    _lowerCamelCase	, self.scheduler.timesteps[tmax]	, encoder_hidden_states=_lowerCamelCase	, ).sample

										lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
										lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

										lowerCamelCase__					=self.scheduler.step(_lowerCamelCase	, self.scheduler.timesteps[tmax]	, _lowerCamelCase      ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
																if i > tmax:
																						if i < tmin:  # layout generation phase
																												lowerCamelCase__					=self.scheduler.add_noise(
																												    _lowerCamelCase	, _lowerCamelCase	, timesteps=_lowerCamelCase	, )

																												lowerCamelCase__					=(mix_factor * latents) + (
																												    1 - mix_factor
																												) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																												lowerCamelCase__					=torch.cat([input] * 2      )

																						else:  # content generation phase
																												lowerCamelCase__					=torch.cat([latents] * 2      )

																						lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, _lowerCamelCase      )

																						with torch.no_grad():
																												lowerCamelCase__					=self.unet(
																												    _lowerCamelCase	, _lowerCamelCase	, encoder_hidden_states=_lowerCamelCase	, ).sample

																						lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
																						lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

																						lowerCamelCase__					=self.scheduler.step(_lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ).prev_sample

										return self.decode(_lowerCamelCase      )


