
import argparse
import os
import re


lowerCamelCase__    :		Union[str, Any]          =   """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCamelCase__    :		Tuple          =   re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
lowerCamelCase__    :		Dict          =   re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")

def 		UpperCamelCase	(      lowercase_ , lowercase_ = False   )		->	Any:


  '''simple docstring'''






  with open(lowercase_ , """r""" , encoding="""utf-8"""   ) as f:
    lowercase__  :   Any    		=      f.read()

  lowercase__  :   Optional[int]    		=      content.split("""\n"""   )
  lowercase__  :   Dict    		=      []
  lowercase__  :   Optional[int]    		=      0
  while line_idx < len(lowercase_   ):
    if _re_intro_mapping.search(lines[line_idx]   ) is not None:
      lowercase__  :   Tuple    		=      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

      lowercase__  :   int    		=      []
      while lines[line_idx].strip() != "]":
        # Blocks either fit in one line or not
        if lines[line_idx].strip() == "(":
          lowercase__  :   str    		=      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
      lowercase__  :   Optional[int]    		=      sorted(lowercase_ , key=lambda lowercase_   : _re_identifier.search(lowercase_   ).groups()[0]   )
      new_lines += blocks
    else:
      new_lines.append(lines[line_idx]   )
      line_idx += 1

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

def 		UpperCamelCase	(      lowercase_ = False   )		->	int:


  '''simple docstring'''






  lowercase__  :   Union[str, Any]    		=      [os.path.join(lowercase_ , lowercase_   ) for f in os.listdir(lowercase_   ) if f.endswith(""".py"""   )]
  lowercase__  :   int    		=      [sort_auto_mapping(lowercase_ , overwrite=lowercase_   ) for fname in fnames]

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


if __name__ == "__main__":
     lowerCamelCase__    :		Tuple          =   argparse.ArgumentParser()
     parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
     lowerCamelCase__    :		Optional[int]          =   parser.parse_args()

     sort_all_auto_mappings(not args.check_only)








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       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     Optional[torch.FloatTensor]        =			None



class       _snake_case   (	UpperCAmelCase_  ,						UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, 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					,  ):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      sigma_max

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


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   return sample


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__  :   Dict    		=      num_inference_steps
   lowercase__  :   List[Any]    		=      np.arange(0					,  self.num_inference_steps)[::-1].copy()
   lowercase__  :   Optional[Any]    		=      torch.from_numpy(SCREAMING_SNAKE_CASE_).to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   str    		=      [
       (
           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
   ]
   lowercase__  :   int    		=      torch.tensor(SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa					,  device=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




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

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

   return sample_hat, sigma_hat


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_hat + sigma_hat * model_output
   lowercase__  :   str    		=      (sample_hat - pred_original_sample) / sigma_hat
   lowercase__  :   Optional[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      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_prev + sigma_prev * model_output
   lowercase__  :   Dict    		=      (sample_prev - pred_original_sample) / sigma_prev
   lowercase__  :   Optional[int]    		=      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      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   raise NotImplementedError()






