





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 A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = None





class A_					(			_lowerCamelCase ,      _lowerCamelCase   ):
   lowerCAmelCase__        = 2




   @register_to_config
   def __init__(self	:int    ,   _UpperCamelCase	:Any = 0.0_2    ,   _UpperCamelCase	:List[str] = 100    ,   _UpperCamelCase	:Optional[Any] = 1.0_0_7    ,   _UpperCamelCase	:Tuple = 80    ,   _UpperCamelCase	:Optional[int] = 0.0_5    ,   _UpperCamelCase	:List[str] = 50    ,   )->					Tuple:
         __A          =				sigma_max

         # setable values
         __A          =				None
         __A          =				None
         __A          =				None  # sigma(t_i)




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:Any = None )->					int:
         return sample




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int = None )->					int:
         __A          =				num_inference_steps
         __A          =				np.arange(0    ,   self.num_inference_steps )[::-1].copy()
         __A          =				torch.from_numpy(__a ).to(__a )
         __A          =				[
             (
                 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
         ]
         __A          =				torch.tensor(__a    ,   dtype=torch.floataa    ,   device=__a )




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:Optional[int] = None )->					Any:

         if self.config.s_min <= sigma <= self.config.s_max:
               __A          =				min(self.config.s_churn / self.num_inference_steps    ,   2**0.5 - 1 )
         else:
               __A          =				0

         # sample eps ~ N(0, S_noise^2 * I)
         __A          =				self.config.s_noise * randn_tensor(sample.shape    ,   generator=__a ).to(sample.device )
         __A          =				sigma + gamma * sigma
         __A          =				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Dict = True    ,   )->					str:
         __A          =				sample_hat + sigma_hat * model_output
         __A          =				(sample_hat - pred_original_sample) / sigma_hat
         __A          =				sample_hat + (sigma_prev - sigma_hat) * derivative

         if not return_dict:
               return (sample_prev, derivative)

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




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[Any] = True    ,   )->					str:
         __A          =				sample_prev + sigma_prev * model_output
         __A          =				(sample_prev - pred_original_sample) / sigma_prev
         __A          =				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=__a    ,   derivative=__a    ,   pred_original_sample=__a )






   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Dict )->					Union[str, Any]:
         raise NotImplementedError()










import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


_snake_case		=  {
    "<": operator.lt,
    "<=": operator.le,
    "==": operator.eq,
    "!=": operator.ne,
    ">=": operator.ge,
    ">": operator.gt,
}






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if got_ver is None or want_ver is None:
														raise ValueError(
														    F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
														    F" reinstalling {pkg}."  )
							if not ops[op](version.parse(_lowerCamelCase  )				, version.parse(_lowerCamelCase  )  ):
														raise ImportError(
														    F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase = None  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    F"\n{hint}" if hint is not None else ""

							# non-versioned check
							if re.match(r"^[\w_\-\d]+$"				, _lowerCamelCase  ):
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    requirement, None, None
							else:
														_lowerCAmelCase     :							Optional[int]    =    re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)"				, _lowerCamelCase  )
														if not match:
																					raise ValueError(
																					    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
																					    F" got {requirement}"  )
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    match[0]
														_lowerCAmelCase     :							Any    =    want_full.split(","  )  # there could be multiple requirements
														_lowerCAmelCase     :							Optional[int]    =    {}
														for w in want_range:
																					_lowerCAmelCase     :							Any    =    re.findall(r"^([\s!=<>]{1,2})(.+)"				, _lowerCamelCase  )
																					if not match:
																												raise ValueError(
																												    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
																												    F" but got {requirement}"  )
																					_lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    match[0]
																					_lowerCAmelCase     :							Union[str, Any]    =    want_ver
																					if op not in ops:
																												raise ValueError(F"{requirement}: need one of {list(ops.keys()  )}, but got {op}"  )

    # special case
							if pkg == "python":
														_lowerCAmelCase     :							Tuple    =    ".".join([str(_lowerCamelCase  ) for x in sys.version_info[:3]]  )
														for op, want_ver in wanted.items():
																					_compare_versions(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )
														return

							# check if any version is installed
							try:
														_lowerCAmelCase     :							Any    =    importlib.metadata.version(_lowerCamelCase  )
							except importlib.metadata.PackageNotFoundError:
														raise importlib.metadata.PackageNotFoundError(
														    F"The '{requirement}' distribution was not found and is required by this application. {hint}"  )

							# check that the right version is installed if version number or a range was provided
							if want_ver is not None:
														for op, want_ver in wanted.items():
																					_compare_versions(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
							return require_version(_lowerCamelCase				, _lowerCamelCase  )
