





'''simple docstring'''




import argparse
import os
import re

import packaging.version


_SCREAMING_SNAKE_CASE							     =			"examples/"
_SCREAMING_SNAKE_CASE							     =			{
    "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","),
    "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
_SCREAMING_SNAKE_CASE							     =			{
    "init": "src/diffusers/__init__.py",
    "setup": "setup.py",
}
_SCREAMING_SNAKE_CASE							     =			"README.md"





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							Dict		)		->  Dict:
   with open(__lowerCAmelCase      ,    """r"""      ,    encoding="""utf-8"""      ,    newline="""\n"""		) as f:
      snake_case 						=   f.read()
   snake_case						,    snake_case 						=   REPLACE_PATTERNS[pattern]
   snake_case 						=   replace.replace("""VERSION"""      ,    __lowerCAmelCase		)
   snake_case 						=   re_pattern.sub(__lowerCAmelCase      ,    __lowerCAmelCase		)
   with open(__lowerCAmelCase      ,    """w"""      ,    encoding="""utf-8"""      ,    newline="""\n"""		) as f:
      f.write(__lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[Any]		)		->  int:
   for folder, directories, fnames in os.walk(__lowerCAmelCase		):
      # Removing some of the folders with non-actively maintained examples from the walk
      if "research_projects" in directories:
         directories.remove("""research_projects"""		)
      if "legacy" in directories:
         directories.remove("""legacy"""		)
      for fname in fnames:
         if fname.endswith(""".py"""		):
            update_version_in_file(os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    __lowerCAmelCase      ,    pattern="""examples"""		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							Union[str, Any]=False		)		->  List[Any]:
   for pattern, fname in REPLACE_FILES.items():
      update_version_in_file(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   if not patch:
      update_version_in_examples(__lowerCAmelCase		)





def 	__lowerCamelCase			(      )		->  Optional[Any]:
   snake_case 						=   """🤗 Transformers currently provides the following architectures"""
   snake_case 						=   """1. Want to contribute a new model?"""
   with open(__lowerCAmelCase      ,    """r"""      ,    encoding="""utf-8"""      ,    newline="""\n"""		) as f:
      snake_case 						=   f.readlines()

   # Find the start of the list.
   snake_case 						=   0
   while not lines[start_index].startswith(_start_prompt		):
      start_index += 1
   start_index += 1

   snake_case 						=   start_index
   # Update the lines in the model list.
   while not lines[index].startswith(_end_prompt		):
      if lines[index].startswith("""1."""		):
         snake_case 						=   lines[index].replace(
             """https://huggingface.co/docs/diffusers/main/model_doc"""      ,    """https://huggingface.co/docs/diffusers/model_doc"""      ,    )
      index += 1

   with open(__lowerCAmelCase      ,    """w"""      ,    encoding="""utf-8"""      ,    newline="""\n"""		) as f:
      f.writelines(__lowerCAmelCase		)





def 	__lowerCamelCase			(      )		->  List[Any]:
   with open(REPLACE_FILES["""init"""]      ,    """r"""		) as f:
      snake_case 						=   f.read()
   snake_case 						=   REPLACE_PATTERNS["""init"""][0].search(__lowerCAmelCase		).groups()[0]
   return packaging.version.parse(__lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]=False		)		->  Union[str, Any]:
   snake_case 						=   get_version()
   if patch and default_version.is_devrelease:
      raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""		)
   if default_version.is_devrelease:
      snake_case 						=   default_version.base_version
   elif patch:
      snake_case 						=   F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
   else:
      snake_case 						=   F'''{default_version.major}.{default_version.minor + 1}.0'''

   # Now let's ask nicely if that's the right one.
   snake_case 						=   input(F'''Which version are you releasing? [{default_version}]'''		)
   if len(__lowerCAmelCase		) == 0:
      snake_case 						=   default_version

   print(F'''Updating version to {version}.'''		)
   global_version_update(__lowerCAmelCase      ,    patch=__lowerCAmelCase		)





def 	__lowerCamelCase			(      )		->  List[str]:
   snake_case 						=   get_version()
   snake_case 						=   F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
   snake_case 						=   current_version.base_version

   # Check with the user we got that right.
   snake_case 						=   input(F'''Which version are we developing now? [{dev_version}]'''		)
   if len(__lowerCAmelCase		) == 0:
      snake_case 						=   dev_version

   print(F'''Updating version to {version}.'''		)
   global_version_update(__lowerCAmelCase		)


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()
 parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
 parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 if not args.post_release:
  pre_release_work(patch=args.patch)
 elif args.patch:
  print("Nothing to do after a patch :-)")
 else:
  post_release_work()








'''simple docstring'''




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

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   42
     snake_case_						      =   42




class 			_lowerCAmelCase		(			A__							,     A__	):






     """simple docstring"""






     snake_case_						      =   1



     @register_to_config
     def __init__(   self							:			str   ,       __snake_case							:			int = 20_00   ,       __snake_case							:			float = 0.15   ,       __snake_case							:			float = 0.01   ,       __snake_case							:			float = 13_48.0   ,       __snake_case							:			float = 1e-5   ,       __snake_case							:			int = 1   ,       )->	str:
        # standard deviation of the initial noise distribution
        snake_case 						=   sigma_max

        # setable values
        snake_case 						=   None

        self.set_sigmas(__snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[int] = None						)->	torch.FloatTensor:
        return sample



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			int   ,       __snake_case							:			float = None   ,       __snake_case							:			Union[str, torch.device] = None						)->	Optional[Any]:
        snake_case 						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

        snake_case 						=   torch.linspace(1   ,       __snake_case   ,       __snake_case   ,       device=__snake_case						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]   ,       __snake_case							:			int   ,       __snake_case							:			float = None   ,       __snake_case							:			float = None   ,       __snake_case							:			float = None						)->	str:
        snake_case 						=   sigma_min if sigma_min is not None else self.config.sigma_min
        snake_case 						=   sigma_max if sigma_max is not None else self.config.sigma_max
        snake_case 						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
           self.set_timesteps(__snake_case   ,       __snake_case						)

        snake_case 						=   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        snake_case 						=   torch.exp(torch.linspace(math.log(__snake_case						)   ,       math.log(__snake_case						)   ,       __snake_case						)						)
        snake_case 						=   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			List[str]   ,       __snake_case							:			str						)->	Optional[int]:
        return torch.where(
            timesteps == 0   ,       torch.zeros_like(t.to(timesteps.device						)						)   ,       self.discrete_sigmas[timesteps - 1].to(timesteps.device						)   ,       )



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			int   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[torch.Generator] = None   ,       __snake_case							:			bool = True   ,       )->	Union[SdeVeOutput, Tuple]:
        if self.timesteps is None:
           raise ValueError(
               """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""						)

        snake_case 						=   timestep * torch.ones(
            sample.shape[0]   ,       device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
        snake_case 						=   (timestep * (len(self.timesteps						) - 1)).long()

        # mps requires indices to be in the same device, so we use cpu as is the default with cuda
        snake_case 						=   timesteps.to(self.discrete_sigmas.device						)

        snake_case 						=   self.discrete_sigmas[timesteps].to(sample.device						)
        snake_case 						=   self.get_adjacent_sigma(__snake_case   ,       __snake_case						).to(sample.device						)
        snake_case 						=   torch.zeros_like(__snake_case						)
        snake_case 						=   (sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        snake_case 						=   diffusion.flatten()
        while len(diffusion.shape						) < len(sample.shape						):
           snake_case 						=   diffusion.unsqueeze(-1						)
        snake_case 						=   drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        snake_case 						=   randn_tensor(
            sample.shape   ,       layout=sample.layout   ,       generator=__snake_case   ,       device=sample.device   ,       dtype=sample.dtype						)
        snake_case 						=   sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        snake_case 						=   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
           return (prev_sample, prev_sample_mean)

        return SdeVeOutput(prev_sample=__snake_case   ,       prev_sample_mean=__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[torch.Generator] = None   ,       __snake_case							:			bool = True   ,       )->	Union[SchedulerOutput, Tuple]:
        if self.timesteps is None:
           raise ValueError(
               """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""						)

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        snake_case 						=   randn_tensor(sample.shape   ,       layout=sample.layout   ,       generator=__snake_case						).to(sample.device						)

        # compute step size from the model_output, the noise, and the snr
        snake_case 						=   torch.norm(model_output.reshape(model_output.shape[0]   ,       -1						)   ,       dim=-1						).mean()
        snake_case 						=   torch.norm(noise.reshape(noise.shape[0]   ,       -1						)   ,       dim=-1						).mean()
        snake_case 						=   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        snake_case 						=   step_size * torch.ones(sample.shape[0]						).to(sample.device						)
        # self.repeat_scalar(step_size, sample.shape[0])

        # compute corrected sample: model_output term and noise term
        snake_case 						=   step_size.flatten()
        while len(step_size.shape						) < len(sample.shape						):
           snake_case 						=   step_size.unsqueeze(-1						)
        snake_case 						=   sample + step_size * model_output
        snake_case 						=   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
           return (prev_sample,)

        return SchedulerOutput(prev_sample=__snake_case						)



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       )->	torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        snake_case 						=   timesteps.to(original_samples.device						)
        snake_case 						=   self.discrete_sigmas.to(original_samples.device						)[timesteps]
        snake_case 						=   (
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(__snake_case						) * sigmas[:, None, None, None]
        )
        snake_case 						=   noise + original_samples
        return noisy_samples



     def __len__(   self							:			str						)->	Any:
        return self.config.num_train_timesteps

