



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

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

class   snake_case__							(_UpperCamelCase   ,      _UpperCamelCase						):







						"""simple docstring"""




						__lowerCAmelCase	:Tuple         =	1

						@register_to_config
						def __init__(  self   ,  __lowercase=2_0_0_0   ,  __lowercase=0.1   ,  __lowercase=2_0   ,  __lowercase=1E-3    )   -> str:
									"""simple docstring"""




									a__  :		Dict					=    None
									a__  :		Any					=    None
									a__  :		Dict					=    None

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase = None    )   -> Any:
									"""simple docstring"""




									a__  :		Union[str, Any]					=    torch.linspace(1   ,  self.config.sampling_eps   ,  __lowercase   ,  device=__lowercase    )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase=None    )   -> Optional[Any]:
									"""simple docstring"""




									if self.timesteps is None:
												raise ValueError(
												    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""    )

									# TODO(Patrick) better comments + non-PyTorch
									# postprocess model score
									a__  :		int					=    (
									    -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
									)
									a__  :		Optional[Any]					=    torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff    )    )
									a__  :		List[str]					=    std.flatten()
									while len(std.shape    ) < len(score.shape    ):
												a__  :		int					=    std.unsqueeze(-1    )
									a__  :		Tuple					=    -score / std

									# compute
									a__  :		str					=    -1.0 / len(self.timesteps    )

									a__  :		Tuple					=    self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
									a__  :		List[str]					=    beta_t.flatten()
									while len(beta_t.shape    ) < len(x.shape    ):
												a__  :		int					=    beta_t.unsqueeze(-1    )
									a__  :		Optional[Any]					=    -0.5 * beta_t * x

									a__  :		int					=    torch.sqrt(__lowercase    )
									a__  :		Optional[int]					=    drift - diffusion**2 * score
									a__  :		Optional[Any]					=    x + drift * dt

									# add noise
									a__  :		Dict					=    randn_tensor(x.shape   ,  layout=x.layout   ,  generator=__lowercase   ,  device=x.device   ,  dtype=x.dtype    )
									a__  :		int					=    x_mean + diffusion * math.sqrt(-dt    ) * noise

									return x, x_mean

						def __len__(  self    )   -> List[str]:
									"""simple docstring"""




									return self.config.num_train_timesteps











import argparse
import os
import re


SCREAMING_SNAKE_CASE__							:      Any         =    """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")






def    _A	(   lowerCamelCase				,			lowerCamelCase = False ):
   with open(lowerCamelCase				,			"r"				,			encoding="utf-8" ) as f:
      a__						:    Optional[int]   		=	f.read()

   a__						:    Optional[Any]   		=	content.split("\n" )
   a__						:    Optional[int]   		=	[]
   a__						:    Optional[Any]   		=	0
   while line_idx < len(lowerCamelCase ):
      if _re_intro_mapping.search(lines[line_idx] ) is not None:
         a__						:    str   		=	len(re.search(r"^(\s*)\S"				,			lines[line_idx] ).groups()[0] ) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "(" ):
            new_lines.append(lines[line_idx] )
            line_idx += 1

         a__						:    Union[str, Any]   		=	[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               a__						:    int   		=	line_idx
               while not lines[line_idx].startswith(" " * indent + ")" ):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1] ) )
            else:
               blocks.append(lines[line_idx] )
            line_idx += 1

         # Sort blocks by their identifiers
         a__						:    Optional[Any]   		=	sorted(lowerCamelCase				,			key=lambda lowerCamelCase : _re_identifier.search(lowerCamelCase ).groups()[0] )
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx] )
         line_idx += 1

   if overwrite:
      with open(lowerCamelCase				,			"w"				,			encoding="utf-8" ) as f:
         f.write("\n".join(lowerCamelCase ) )
   elif "\n".join(lowerCamelCase ) != content:
      return True






def    _A	(   lowerCamelCase = False ):
   a__						:    List[str]   		=	[os.path.join(lowerCamelCase				,			lowerCamelCase ) for f in os.listdir(lowerCamelCase ) if f.endswith(".py" )]
   a__						:    Any   		=	[sort_auto_mapping(lowerCamelCase				,			overwrite=lowerCamelCase ) for fname in fnames]

   if not overwrite and any(lowerCamelCase ):
      a__						:    Dict   		=	[f for f, d in zip(lowerCamelCase				,			lowerCamelCase ) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(lowerCamelCase )}. Run `make style` to fix"""
          " this." )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Dict         =    argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    parser.parse_args()

  sort_all_auto_mappings(not args.check_only)

