





# Lint as: python3
import itertools
import os
import re


snake_case_				=			re.compile(R'([A-Z]+)([A-Z][a-z])')
snake_case_				=			re.compile(R'([a-z\d])([A-Z])')

snake_case_				=			re.compile(R'(?<!_)_(?!_)')
snake_case_				=			re.compile(R'(_{2,})')

snake_case_				=			R'^\w+(\.\w+)*$'

snake_case_				=			R'<>:/\|?*'






def lowerCamelCase__    (							snake_case_	:  Dict       )      ->				Optional[int]:
   __snake_case							       =   _uppercase_uppercase_re.sub(R'''\1_\2'''       ,      snake_case_       )
   __snake_case							       =   _lowercase_uppercase_re.sub(R'''\1_\2'''       ,      snake_case_       )
   return name.lower()






def lowerCamelCase__    (							snake_case_	:  Any       )      ->				Any:
   __snake_case							       =   _single_underscore_re.split(snake_case_       )
   __snake_case							       =   [_multiple_underscores_re.split(snake_case_       ) for n in name]
   return "".join(n.capitalize() for n in itertools.chain.from_iterable(snake_case_       ) if n != ''''''       )






def lowerCamelCase__    (							snake_case_	:  Optional[int]       )      ->				int:
   if os.path.basename(snake_case_       ) != name:
      raise ValueError(f"""Should be a dataset name, not a path: {name}"""       )
   return camelcase_to_snakecase(snake_case_       )






def lowerCamelCase__    (							snake_case_	:  Tuple       ,      snake_case_	:  Any       )      ->				str:
   if os.path.basename(snake_case_       ) != name:
      raise ValueError(f"""Should be a dataset name, not a path: {name}"""       )
   if not re.match(_split_re       ,      snake_case_       ):
      raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'."""       )
   return f"""{filename_prefix_for_name(snake_case_       )}-{split}"""






def lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  Dict       ,      snake_case_	:  int       ,      snake_case_	:  List[Any]=None       )      ->				Union[str, Any]:
   __snake_case							       =   filename_prefix_for_split(snake_case_       ,      snake_case_       )
   if filetype_suffix:
      prefix += f""".{filetype_suffix}"""
   __snake_case							       =   os.path.join(snake_case_       ,      snake_case_       )
   return f"""{filepath}*"""







def lowerCamelCase__    (							snake_case_	:  Any       ,      snake_case_	:  int       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  Any=None       ,      snake_case_	:  Union[str, Any]=None       )      ->				str:
   __snake_case							       =   filename_prefix_for_split(snake_case_       ,      snake_case_       )
   __snake_case							       =   os.path.join(snake_case_       ,      snake_case_       )

   if shard_lengths:
      __snake_case							       =   len(snake_case_       )
      __snake_case							       =   [f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(snake_case_       )]
      if filetype_suffix:
         __snake_case							       =   [filename + f""".{filetype_suffix}""" for filename in filenames]
      return filenames
   else:
      __snake_case							       =   prefix
      if filetype_suffix:
         filename += f""".{filetype_suffix}"""
      return [filename]








"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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






@dataclass
class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      torch.FloatTensor
       SCREAMING_SNAKE_CASE_   :      torch.FloatTensor
       SCREAMING_SNAKE_CASE_   :      Optional[torch.FloatTensor]							=  None






class 					_SCREAMING_SNAKE_CASE(   A    ,	A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  2




       @register_to_config
       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 0.0_2		,SCREAMING_SNAKE_CASE__ = 1_00		,SCREAMING_SNAKE_CASE__ = 1.0_0_7		,SCREAMING_SNAKE_CASE__ = 80		,SCREAMING_SNAKE_CASE__ = 0.0_5		,SCREAMING_SNAKE_CASE__ = 50		,)			->      Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  sigma_max

             # setable values
             __SCREAMING_SNAKE_CASE   :int						=  None
             __SCREAMING_SNAKE_CASE   :np.IntTensor						=  None
             __SCREAMING_SNAKE_CASE   :torch.FloatTensor						=  None  # sigma(t_i)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      torch.FloatTensor:





             """simple docstring"""






             return sample




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  num_inference_steps
             __SCREAMING_SNAKE_CASE   :int						=  np.arange(0		,self.num_inference_steps							)[::-1].copy()
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.from_numpy(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Any						=  [
                 (
                     self.config.sigma_max**2
                     * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                 )
                 for i in self.timesteps
             ]
             __SCREAMING_SNAKE_CASE   :List[str]						=  torch.tensor(SCREAMING_SNAKE_CASE__		,dtype=torch.floataa		,device=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      Tuple[torch.FloatTensor, float]:





             """simple docstring"""






             if self.config.s_min <= sigma <= self.config.s_max:
                   __SCREAMING_SNAKE_CASE   :List[str]						=  min(self.config.s_churn / self.num_inference_steps		,2**0.5 - 1							)
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  0

             # sample eps ~ N(0, S_noise^2 * I)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.config.s_noise * randn_tensor(sample.shape		,generator=SCREAMING_SNAKE_CASE__							).to(sample.device							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  sigma + gamma * sigma
             __SCREAMING_SNAKE_CASE   :str						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

             return sample_hat, sigma_hat




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)			->      Union[KarrasVeOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  sample_hat + sigma_hat * model_output
             __SCREAMING_SNAKE_CASE   :Tuple						=  (sample_hat - pred_original_sample) / sigma_hat
             __SCREAMING_SNAKE_CASE   :List[Any]						=  sample_hat + (sigma_prev - sigma_hat) * derivative

             if not return_dict:
                   return (sample_prev, derivative)

             return KarrasVeOutput(
                 prev_sample=SCREAMING_SNAKE_CASE__		,derivative=SCREAMING_SNAKE_CASE__		,pred_original_sample=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)			->      Union[KarrasVeOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  sample_prev + sigma_prev * model_output
             __SCREAMING_SNAKE_CASE   :List[Any]						=  (sample_prev - pred_original_sample) / sigma_prev
             __SCREAMING_SNAKE_CASE   :Dict						=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

             if not return_dict:
                   return (sample_prev, derivative)

             return KarrasVeOutput(
                 prev_sample=SCREAMING_SNAKE_CASE__		,derivative=SCREAMING_SNAKE_CASE__		,pred_original_sample=SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             raise NotImplementedError()