


from dataclasses import dataclass

_snake_case   = "0.1"
_snake_case   = "Lucia Harcekova"




@dataclass
class    A_   :







	"""simple docstring"""





	_UpperCamelCase				: Any      =					42
	_UpperCamelCase				: str      =					42
	_UpperCamelCase				: Union[str, Any]      =					42
	def __repr__(			self					: Optional[Any]				)    ->			Union[str, Any]:
						return f'({self.offset}, {self.length}, {self.indicator})'




class    A_   :







	"""simple docstring"""





	def __init__(			self					: Optional[Any]       ,    _a					: Tuple = 1_3       ,    _a					: int = 6				)    ->			Tuple:
						UpperCAmelCase						:				Optional[int]		  =			window_size
						UpperCAmelCase						:				Optional[Any]		  =			lookahead_buffer_size
						UpperCAmelCase						:				Tuple		  =			self.window_size - self.lookahead_buffer_size
	def   _UpperCamelCase    (			self					: List[str]       ,    _a					: Dict				)    ->			Any:
						UpperCAmelCase						:				int		  =			[]
						UpperCAmelCase						:				Union[str, Any]		  =			""""""

						# 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						:				Union[str, Any]		  =			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						:				Union[str, Any]		  =			search_buffer[-self.search_buffer_size :]

											# update the text
											UpperCAmelCase						:				Union[str, Any]		  =			text[token.length + 1 :]

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

						return output
	def   _UpperCamelCase    (			self					: str       ,    _a					: Optional[Any]				)    ->			Any:
						UpperCAmelCase						:				Tuple		  =			""""""

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

						return output
	def   _UpperCamelCase    (			self					: Any       ,    _a					: Optional[Any]       ,    _a					: Dict				)    ->			str:

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

						# Initialise result parameters to default values
						UpperCAmelCase						:				str		  =			0, 0

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

						for i, character in enumerate(_lowerCamelCase				):
											UpperCAmelCase						:				List[Any]		  =			len(_lowerCamelCase				) - i
											if character == text[0]:
																UpperCAmelCase						:				Tuple		  =			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						:				Union[str, Any]		  =			found_offset, found_length

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

	def   _UpperCamelCase    (			self					: str       ,    _a					: Optional[Any]       ,    _a					: Tuple       ,    _a					: List[str]       ,    _a					: Union[str, Any]				)    ->			Any:

						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
						_snake_case   = LZaaCompressor(window_size=1_3, lookahead_buffer_size=6)

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







# 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    A_   :







 """simple docstring"""





 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 @classmethod
 def   _UpperCamelCase    (			cls					: Union[str, Any]				)    ->			int:
      return cls()




@dataclass
class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: ScoreSdeVeSchedulerState
 _UpperCamelCase				: jnp.ndarray
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None




class    A_   ( a_      ,						a_     ):







 """simple docstring"""





 @property
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			List[str]:
      return True
 @register_to_config
 def __init__(			self					: List[str]       ,    _a					: int = 2_0_0_0       ,    _a					: float = 0.1_5       ,    _a					: float = 0.0_1       ,    _a					: float = 1_3_4_8.0       ,    _a					: float = 1E-5       ,    _a					: int = 1       ,    )    ->			List[Any]:
      pass
 def   _UpperCamelCase    (			self					: int				)    ->			List[Any]:
      UpperCAmelCase						:				str		  =			ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          _a       ,    self.config.num_train_timesteps       ,    self.config.sigma_min       ,    self.config.sigma_max       ,    self.config.sampling_eps       ,    )
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: Tuple = ()       ,    _a					: float = None				)    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				Tuple		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps

      UpperCAmelCase						:				Dict		  =			jnp.linspace(1       ,    _a       ,    _a				)
      return state.replace(timesteps=_a				)
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: float = None       ,    _a					: float = None       ,    _a					: float = None       ,    )    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				str		  =			sigma_min if sigma_min is not None else self.config.sigma_min
      UpperCAmelCase						:				Any		  =			sigma_max if sigma_max is not None else self.config.sigma_max
      UpperCAmelCase						:				Any		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
           UpperCAmelCase						:				List[Any]		  =			self.set_timesteps(_a       ,    _a       ,    _a				)

      UpperCAmelCase						:				List[Any]		  =			jnp.exp(jnp.linspace(jnp.log(_a				)       ,    jnp.log(_a				)       ,    _a				)				)
      UpperCAmelCase						:				List[str]		  =			jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]				)

      return state.replace(discrete_sigmas=_a       ,    sigmas=_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: Optional[int]       ,    _a					: Optional[int]       ,    _a					: Union[str, Any]				)    ->			List[str]:
      return jnp.where(timesteps == 0       ,    jnp.zeros_like(_a				)       ,    state.discrete_sigmas[timesteps - 1]				)
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: int       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = 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						:				Union[str, Any]		  =			timestep * jnp.ones(
          sample.shape[0]       ,    )
      UpperCAmelCase						:				int		  =			(timestep * (len(state.timesteps				) - 1)).long()

      UpperCAmelCase						:				List[str]		  =			state.discrete_sigmas[timesteps]
      UpperCAmelCase						:				Optional[int]		  =			self.get_adjacent_sigma(_a       ,    _a       ,    _a				)
      UpperCAmelCase						:				Tuple		  =			jnp.zeros_like(_a				)
      UpperCAmelCase						:				List[Any]		  =			(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						:				Union[str, Any]		  =			diffusion.flatten()
      UpperCAmelCase						:				Union[str, Any]		  =			broadcast_to_shape_from_left(_a       ,    sample.shape				)
      UpperCAmelCase						:				Tuple		  =			drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      UpperCAmelCase						:				List[str]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)
      UpperCAmelCase						:				Any		  =			sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      UpperCAmelCase						:				str		  =			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=_a       ,    prev_sample_mean=_a       ,    state=_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = 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						:				Optional[int]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)

      # compute step size from the model_output, the noise, and the snr
      UpperCAmelCase						:				str		  =			jnp.linalg.norm(_a				)
      UpperCAmelCase						:				Optional[int]		  =			jnp.linalg.norm(_a				)
      UpperCAmelCase						:				int		  =			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
      UpperCAmelCase						:				List[Any]		  =			step_size * jnp.ones(sample.shape[0]				)

      # compute corrected sample: model_output term and noise term
      UpperCAmelCase						:				Optional[Any]		  =			step_size.flatten()
      UpperCAmelCase						:				Union[str, Any]		  =			broadcast_to_shape_from_left(_a       ,    sample.shape				)
      UpperCAmelCase						:				int		  =			sample + step_size * model_output
      UpperCAmelCase						:				int		  =			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
           return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=_a       ,    state=_a				)

 def __len__(			self					: Any				)    ->			Optional[int]:
      return self.config.num_train_timesteps



