code: 
  
  
  
  
  
  # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  from jax import random
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
  
  
  
  
  @flax.struct.dataclass
  class   _UpperCAmelCase	:
  					# setable values
  					__A          =   None
  					__A          =   None
  					__A          =   None
  
  
  
  
  					@classmethod
  					def 	snake_case_		(						cls       )						->  Union[str, Any]:
  							return cls()
  
  
  
  
  @dataclass
  class   _UpperCAmelCase	(   _lowercase					):
  					__A          =   42
  					__A          =   42
  					__A          =   None
  
  
  
  
  class   _UpperCAmelCase	(   _lowercase ,       _lowercase					):
  
  
  
  
  					@property
  					def 	snake_case_		(						self       )						->  List[str]:
  							return True
  
  
  
  
  					@register_to_config
  					def __init__(						self    ,      __lowerCAmelCase = 2_000    ,      __lowerCAmelCase = 0.1_5    ,      __lowerCAmelCase = 0.0_1    ,      __lowerCAmelCase = 1_348.0    ,      __lowerCAmelCase = 1E-5    ,      __lowerCAmelCase = 1    ,      )						->  List[Any]:
  							pass
  
  
  
  
  					def 	snake_case_		(						self       )						->  List[str]:
  							UpperCamelCase__         =	ScoreSdeVeSchedulerState.create()
  							return self.set_sigmas(
  							    __lowerCAmelCase    ,      self.config.num_train_timesteps    ,      self.config.sigma_min    ,      self.config.sigma_max    ,      self.config.sampling_eps    ,      )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = ()    ,      __lowerCAmelCase = None       )						->  ScoreSdeVeSchedulerState:
  							UpperCamelCase__         =	sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  							UpperCamelCase__         =	jnp.linspace(1    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
  							return state.replace(timesteps=__lowerCAmelCase       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      )						->  ScoreSdeVeSchedulerState:
  							UpperCamelCase__         =	sigma_min if sigma_min is not None else self.config.sigma_min
  							UpperCamelCase__         =	sigma_max if sigma_max is not None else self.config.sigma_max
  							UpperCamelCase__         =	sampling_eps if sampling_eps is not None else self.config.sampling_eps
  							if state.timesteps is None:
  									UpperCamelCase__         =	self.set_timesteps(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
  
  							UpperCamelCase__         =	jnp.exp(jnp.linspace(jnp.log(__lowerCAmelCase       )    ,      jnp.log(__lowerCAmelCase       )    ,      __lowerCAmelCase       )       )
  							UpperCamelCase__         =	jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]       )
  
  							return state.replace(discrete_sigmas=__lowerCAmelCase    ,      sigmas=__lowerCAmelCase       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  str:
  							return jnp.where(timesteps == 0    ,      jnp.zeros_like(__lowerCAmelCase       )    ,      state.discrete_sigmas[timesteps - 1]       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = True    ,      )						->  Union[FlaxSdeVeOutput, Tuple]:
  							if state.timesteps is None:
  									raise ValueError(
  									    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )
  
  							UpperCamelCase__         =	timestep * jnp.ones(
  							    sample.shape[0]    ,      )
  							UpperCamelCase__         =	(timestep * (len(state.timesteps       ) - 1)).long()
  
  							UpperCamelCase__         =	state.discrete_sigmas[timesteps]
  							UpperCamelCase__         =	self.get_adjacent_sigma(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
  							UpperCamelCase__         =	jnp.zeros_like(__lowerCAmelCase       )
  							UpperCamelCase__         =	(sigma**2 - adjacent_sigma**2) ** 0.5
  
  							# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
  							# also equation 47 shows the analog from SDE models to ancestral sampling methods
  							UpperCamelCase__         =	diffusion.flatten()
  							UpperCamelCase__         =	broadcast_to_shape_from_left(__lowerCAmelCase    ,      sample.shape       )
  							UpperCamelCase__         =	drift - diffusion**2 * model_output
  
  							#  equation 6: sample noise for the diffusion term of
  							UpperCamelCase__         =	random.split(__lowerCAmelCase    ,      num=1       )
  							UpperCamelCase__         =	random.normal(key=__lowerCAmelCase    ,      shape=sample.shape       )
  							UpperCamelCase__         =	sample - drift  # subtract because `dt` is a small negative timestep
  							# TODO is the variable diffusion the correct scaling term for the noise?
  							UpperCamelCase__         =	prev_sample_mean + diffusion * noise  # add impact of diffusion field g
  
  							if not return_dict:
  									return (prev_sample, prev_sample_mean, state)
  
  							return FlaxSdeVeOutput(prev_sample=__lowerCAmelCase    ,      prev_sample_mean=__lowerCAmelCase    ,      state=__lowerCAmelCase       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = True    ,      )						->  Union[FlaxSdeVeOutput, Tuple]:
  							if state.timesteps is None:
  									raise ValueError(
  									    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )
  
  							# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
  							# sample noise for correction
  							UpperCamelCase__         =	random.split(__lowerCAmelCase    ,      num=1       )
  							UpperCamelCase__         =	random.normal(key=__lowerCAmelCase    ,      shape=sample.shape       )
  
  							# compute step size from the model_output, the noise, and the snr
  							UpperCamelCase__         =	jnp.linalg.norm(__lowerCAmelCase       )
  							UpperCamelCase__         =	jnp.linalg.norm(__lowerCAmelCase       )
  							UpperCamelCase__         =	(self.config.snr * noise_norm / grad_norm) ** 2 * 2
  							UpperCamelCase__         =	step_size * jnp.ones(sample.shape[0]       )
  
  							# compute corrected sample: model_output term and noise term
  							UpperCamelCase__         =	step_size.flatten()
  							UpperCamelCase__         =	broadcast_to_shape_from_left(__lowerCAmelCase    ,      sample.shape       )
  							UpperCamelCase__         =	sample + step_size * model_output
  							UpperCamelCase__         =	prev_sample_mean + ((step_size * 2) ** 0.5) * noise
  
  							if not return_dict:
  									return (prev_sample, state)
  
  							return FlaxSdeVeOutput(prev_sample=__lowerCAmelCase    ,      state=__lowerCAmelCase       )
  
  
  
  
  					def __len__(						self       )						->  Union[str, Any]:
  							return self.config.num_train_timesteps
  
  
  
  
style_context: 
  
  
  
  
  
  from dataclasses import dataclass
  
  __lowerCAmelCase     =			"0.1"
  __lowerCAmelCase     =			"Lucia Harcekova"
  
  
  
  
  @dataclass
  class   _UpperCAmelCase	:
  					__A          =   42
  					__A          =   42
  					__A          =   42
  
  
  
  
  					def __repr__(						self       )						->  str:
  							return F"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class   _UpperCAmelCase	:
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase = 13    ,      __lowerCAmelCase = 6       )						->  None:
  							UpperCamelCase__         =	window_size
  							UpperCamelCase__         =	lookahead_buffer_size
  							UpperCamelCase__         =	self.window_size - self.lookahead_buffer_size
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  list[Token]:
  							UpperCamelCase__         =	[]
  							UpperCamelCase__         =	""""""
  
  							# 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)
  									UpperCamelCase__         =	self._find_encoding_token(__lowerCAmelCase    ,      __lowerCAmelCase       )
  
  									# 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(__lowerCAmelCase       ) > self.search_buffer_size:
  											UpperCamelCase__         =	search_buffer[-self.search_buffer_size :]
  
  									# update the text
  									UpperCamelCase__         =	text[token.length + 1 :]
  
  									# append the token to output
  									output.append(__lowerCAmelCase       )
  
  							return output
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  str:
  							UpperCamelCase__         =	""""""
  
  							for token in tokens:
  									for _ in range(token.length       ):
  											output += output[-token.offset]
  									output += token.indicator
  
  							return output
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Token:
  
  							if not text:
  									raise ValueError("""We need some text to work with."""       )
  
  							# Initialise result parameters to default values
  							UpperCamelCase__  ,			UpperCamelCase__         =	0, 0
  
  							if not search_buffer:
  									return Token(__lowerCAmelCase    ,      __lowerCAmelCase    ,      text[length]       )
  
  							for i, character in enumerate(__lowerCAmelCase       ):
  									UpperCamelCase__         =	len(__lowerCAmelCase       ) - i
  									if character == text[0]:
  											UpperCamelCase__         =	self._match_length_from_index(__lowerCAmelCase    ,      __lowerCAmelCase    ,      0    ,      __lowerCAmelCase       )
  											# 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:
  													UpperCamelCase__  ,			UpperCamelCase__         =	found_offset, found_length
  
  							return Token(__lowerCAmelCase    ,      __lowerCAmelCase    ,      text[length]       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  int:
  							if not text or text[text_index] != window[window_index]:
  									return 0
  							return 1 + self._match_length_from_index(
  							    __lowerCAmelCase    ,      window + text[text_index]    ,      text_index + 1    ,      window_index + 1       )
  
  
  if __name__ == "__main__":
  				from doctest import testmod
  
  				testmod()
  				# Initialize compressor class
  				__lowerCAmelCase     =			LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  				# Example
  				__lowerCAmelCase     =			"cabracadabrarrarrad"
  				__lowerCAmelCase     =			lzaa_compressor.compress(TEXT)
  				print(lzaa_compressor.compress("ababcbababaa"))
  				__lowerCAmelCase     =			lzaa_compressor.decompress(compressed_text)
  				assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
  
  