




'''simple docstring'''







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


lowerCAmelCase_    	=							logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   _snake_case( UpperCAmelCase			):
 __snake_case:       torch.FloatTensor
 __snake_case:       Optional[torch.FloatTensor]									=				None







def 	_A			(   UpperCAmelCase			,UpperCAmelCase=0.9_99			,UpperCAmelCase="cosine"			,):






   '''simple docstring'''







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(UpperCAmelCase	):
         return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(UpperCAmelCase	):
         return math.exp(t * -12.0	)

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""	)

   A__         =    []
   for i in range(UpperCAmelCase	):
      A__         =    i / num_diffusion_timesteps
      A__         =    (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(UpperCAmelCase	) / alpha_bar_fn(UpperCAmelCase	)			,UpperCAmelCase	)	)
   return torch.tensor(UpperCAmelCase			,dtype=torch.floataa	)







class   _snake_case( UpperCAmelCase					,   UpperCAmelCase			):
 __snake_case:       Tuple									=				[e.name for e in KarrasDiffusionSchedulers]
 __snake_case:       List[str]									=				1






 @register_to_config
 def __init__(self					:     Union[str, Any]						,		a					:     int = 10_00						,		a					:     float = 0.0001						,		a					:     float = 0.02						,		a					:     str = "linear"						,		a					:     Optional[Union[np.ndarray, List[float]]] = None						,		a					:     str = "epsilon"						,		a					:     str = "linspace"						,		a					:     int = 0						,		)					->      Tuple:






    """simple docstring"""


    if trained_betas is not None:
       A__         =    torch.tensor(a						,		dtype=torch.floataa						)
    elif beta_schedule == "linear":
       A__         =    torch.linspace(a						,		a						,		a						,		dtype=torch.floataa						)
    elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       A__         =    (
           torch.linspace(beta_start**0.5						,		beta_end**0.5						,		a						,		dtype=torch.floataa						) ** 2
       )
    elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       A__         =    betas_for_alpha_bar(a						)
    else:
       raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""						)

    A__         =    1.0 - self.betas
    A__         =    torch.cumprod(self.alphas						,		dim=0						)

    A__         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
    A__         =    np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
    A__         =    torch.from_numpy(a						)

    # setable values
    A__         =    None
    A__         =    np.linspace(0						,		num_train_timesteps - 1						,		a						,		dtype=a						)[::-1].copy()
    A__         =    torch.from_numpy(a						)
    A__         =    False






 @property
 def 	_UpperCamelCase   (self					:     int						)					->      List[Any]:






    """simple docstring"""


    if self.config.timestep_spacing in ["linspace", "trailing"]:
       return self.sigmas.max()

    return (self.sigmas.max() ** 2 + 1) ** 0.5






 def 	_UpperCamelCase   (self					:     Any						,		a					:     torch.FloatTensor						,		a					:     Union[float, torch.FloatTensor]						)					->      torch.FloatTensor:






    """simple docstring"""


    if isinstance(a						,		torch.Tensor						):
       A__         =    timestep.to(self.timesteps.device						)
    A__         =    (self.timesteps == timestep).nonzero().item()
    A__         =    self.sigmas[step_index]
    A__         =    sample / ((sigma**2 + 1) ** 0.5)
    A__         =    True
    return sample






 def 	_UpperCamelCase   (self					:     Any						,		a					:     int						,		a					:     Union[str, torch.device] = None						)					->      Dict:






    """simple docstring"""


    A__         =    num_inference_steps

    # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
    if self.config.timestep_spacing == "linspace":
       A__         =    np.linspace(0						,		self.config.num_train_timesteps - 1						,		a						,		dtype=a						)[
           ::-1
       ].copy()
    elif self.config.timestep_spacing == "leading":
       A__         =    self.config.num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       A__         =    (np.arange(0						,		a						) * step_ratio).round()[::-1].copy().astype(a						)
       timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
       A__         =    self.config.num_train_timesteps / self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       A__         =    (np.arange(self.config.num_train_timesteps						,		0						,		-step_ratio						)).round().copy().astype(a						)
       timesteps -= 1
    else:
       raise ValueError(
           f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""						)

    A__         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
    A__         =    np.interp(a						,		np.arange(0						,		len(a						)						)						,		a						)
    A__         =    np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
    A__         =    torch.from_numpy(a						).to(device=a						)
    if str(a						).startswith('mps'						):
       # mps does not support float64
       A__         =    torch.from_numpy(a						).to(a						,		dtype=torch.floataa						)
    else:
       A__         =    torch.from_numpy(a						).to(device=a						)






 def 	_UpperCamelCase   (self					:     Dict						,		a					:     torch.FloatTensor						,		a					:     Union[float, torch.FloatTensor]						,		a					:     torch.FloatTensor						,		a					:     Optional[torch.Generator] = None						,		a					:     bool = True						,		)					->      Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:






    """simple docstring"""


    if (
        isinstance(a						,		a						)
        or isinstance(a						,		torch.IntTensor						)
        or isinstance(a						,		torch.LongTensor						)
    ):
       raise ValueError(
           (
               'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
               ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
               ' one of the `scheduler.timesteps` as a timestep.'
           )						,		)

    if not self.is_scale_input_called:
       logger.warning(
           'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
           'See `StableDiffusionPipeline` for a usage example.'						)

    if isinstance(a						,		torch.Tensor						):
       A__         =    timestep.to(self.timesteps.device						)

    A__         =    (self.timesteps == timestep).nonzero().item()
    A__         =    self.sigmas[step_index]

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
       A__         =    sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       A__         =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
    elif self.config.prediction_type == "sample":
       raise NotImplementedError('prediction_type not implemented yet: sample'						)
    else:
       raise ValueError(
           f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""						)

    A__         =    self.sigmas[step_index]
    A__         =    self.sigmas[step_index + 1]
    A__         =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
    A__         =    (sigma_to**2 - sigma_up**2) ** 0.5

    # 2. Convert to an ODE derivative
    A__         =    (sample - pred_original_sample) / sigma

    A__         =    sigma_down - sigma

    A__         =    sample + derivative * dt

    A__         =    model_output.device
    A__         =    randn_tensor(model_output.shape						,		dtype=model_output.dtype						,		device=a						,		generator=a						)

    A__         =    prev_sample + noise * sigma_up

    if not return_dict:
       return (prev_sample,)

    return EulerAncestralDiscreteSchedulerOutput(
        prev_sample=a						,		pred_original_sample=a						)






 def 	_UpperCamelCase   (self					:     int						,		a					:     torch.FloatTensor						,		a					:     torch.FloatTensor						,		a					:     torch.FloatTensor						,		)					->      torch.FloatTensor:






    """simple docstring"""


    A__         =    self.sigmas.to(device=original_samples.device						,		dtype=original_samples.dtype						)
    if original_samples.device.type == "mps" and torch.is_floating_point(a						):
       # mps does not support float64
       A__         =    self.timesteps.to(original_samples.device						,		dtype=torch.floataa						)
       A__         =    timesteps.to(original_samples.device						,		dtype=torch.floataa						)
    else:
       A__         =    self.timesteps.to(original_samples.device						)
       A__         =    timesteps.to(original_samples.device						)

    A__         =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

    A__         =    sigmas[step_indices].flatten()
    while len(sigma.shape						) < len(original_samples.shape						):
       A__         =    sigma.unsqueeze(-1						)

    A__         =    original_samples + noise * sigma
    return noisy_samples






 def __len__(self					:     Dict						)					->      int:






    """simple docstring"""


    return self.config.num_train_timesteps








'''simple docstring'''







from dataclasses import dataclass

lowerCAmelCase_    	=							'''0.1'''
lowerCAmelCase_    	=							'''Lucia Harcekova'''

@dataclass
class   _snake_case:
 __snake_case:       int
 __snake_case:       int
 __snake_case:       str






 def __repr__(self					:     Optional[int]						)					->      str:






    """simple docstring"""


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




class   _snake_case:






 def __init__(self					:     Optional[Any]						,		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 	_UpperCamelCase   (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 	_UpperCamelCase   (self					:     Optional[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 	_UpperCamelCase   (self					:     Any						,		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 	_UpperCamelCase   (self					:     int						,		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
    lowerCAmelCase_    	=							LZaaCompressor(window_size=1_3, 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."


