



import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


A_		:     Tuple        =     threading.Lock()
A_		:     Optional[logging.Handler]        =     None

A_		:     List[Any]        =     {
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

A_		:     Optional[Any]        =     logging.WARNING

A_		:     List[Any]        =     True


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		os.getenv("TRANSFORMERS_VERBOSITY" ,					UpperCAmelCase__					)
 if env_level_str:
  if env_level_str in log_levels:
   return log_levels[env_level_str]
  else:
   logging.getLogger().warning(
       F"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
       F"""has to be one of: { ", ".join(log_levels.keys()					) }"""					)
 return _default_log_level


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 return __name__.split("."					)[0]


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 return logging.getLogger(_get_library_name()					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 global _default_handler

 with _lock:
  if _default_handler:
   # This library has already configured the library root logger.
   return
  a						=		logging.StreamHandler()  # Set sys.stderr as stream.
  a						=		sys.stderr.flush

  # Apply our default configuration to the library root logger.
  a						=		_get_library_root_logger()
  library_root_logger.addHandler(_default_handler					)
  library_root_logger.setLevel(_get_default_logging_level()					)
  a						=		False


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 global _default_handler

 with _lock:
  if not _default_handler:
   return

  a						=		_get_library_root_logger()
  library_root_logger.removeHandler(_default_handler					)
  library_root_logger.setLevel(logging.NOTSET					)
  a						=		None


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 return log_levels


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[str] = None					):






 '''simple docstring'''

 if name is None:
  a						=		_get_library_name()

 _configure_library_root_logger()
 return logging.getLogger(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 _configure_library_root_logger()
 return _get_library_root_logger().getEffectiveLevel()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int					):






 '''simple docstring'''

 _configure_library_root_logger()
 _get_library_root_logger().setLevel(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 return set_verbosity(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 return set_verbosity(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 return set_verbosity(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 return set_verbosity(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 _configure_library_root_logger()

 assert _default_handler is not None
 _get_library_root_logger().removeHandler(_default_handler					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 _configure_library_root_logger()

 assert _default_handler is not None
 _get_library_root_logger().addHandler(_default_handler					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :logging.Handler					):






 '''simple docstring'''

 _configure_library_root_logger()

 assert handler is not None
 _get_library_root_logger().addHandler(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :logging.Handler					):






 '''simple docstring'''

 _configure_library_root_logger()

 assert handler is not None and handler not in _get_library_root_logger().handlers
 _get_library_root_logger().removeHandler(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 _configure_library_root_logger()
 a						=		False


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 _configure_library_root_logger()
 a						=		True


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		_get_library_root_logger().handlers

 for handler in handlers:
  a						=		logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"					)
  handler.setFormatter(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		_get_library_root_logger().handlers

 for handler in handlers:
  handler.setFormatter(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      self :Any ,					*UpperCAmelCase__ :List[str] ,					**UpperCAmelCase__ :List[str]					):






 '''simple docstring'''

 a						=		os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS" ,					UpperCAmelCase__					)
 if no_advisory_warnings:
  return
 self.warning(*UpperCAmelCase__ ,					**UpperCAmelCase__					)


A_		:     List[str]        =     warning_advice


@functools.lru_cache(UpperCAmelCase__					)
def 	UpperCAmelCase__      (      self :Dict ,					*UpperCAmelCase__ :int ,					**UpperCAmelCase__ :int					):






 '''simple docstring'''

 self.warning(*UpperCAmelCase__ ,					**UpperCAmelCase__					)


A_		:     List[Any]        =     warning_once



class 		_lowercase		:

   def __init__(      self			:       str							,   *__lowerCAmelCase			:       Union[str, Any]							,   **__lowerCAmelCase			:       str							)       ->				Dict:  # pylint: disable=unused-argument


    """simple docstring"""






    a						=		args[0] if args else None

   def __iter__(      self			:       List[Any]							)       ->				Tuple:


    """simple docstring"""






    return iter(self._iterator							)

   def __getattr__(      self			:       Dict							,   __lowerCAmelCase			:       str							)       ->				Optional[Any]:


    """simple docstring"""






    def empty_fn(*__lowerCAmelCase			:       Optional[int]							,   **__lowerCAmelCase			:       Optional[int]							):  # pylint: disable=unused-argument
     return

    return empty_fn

   def __enter__(      self			:       Tuple							)       ->				Optional[int]:


    """simple docstring"""






    return self




   def __exit__(      self			:       Any							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       List[Any]							)       ->				List[str]:


    """simple docstring"""






    return



class 		_lowercase		:

   def __call__(      self			:       List[Any]							,   *__lowerCAmelCase			:       List[Any]							,   **__lowerCAmelCase			:       Any							)       ->				Optional[int]:


    """simple docstring"""






    if _tqdm_active:
     return tqdm_lib.tqdm(*__lowerCAmelCase							,   **__lowerCAmelCase							)
    else:
     return EmptyTqdm(*__lowerCAmelCase							,   **__lowerCAmelCase							)

   def 		A							(      self			:       Dict							,   *__lowerCAmelCase			:       Any							,   **__lowerCAmelCase			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    a						=		None
    if _tqdm_active:
     return tqdm_lib.tqdm.set_lock(*__lowerCAmelCase							,   **__lowerCAmelCase							)




   def 		A							(      self			:       List[Any]							)       ->				Tuple:


    """simple docstring"""






    if _tqdm_active:
     return tqdm_lib.tqdm.get_lock()

A_		:     List[str]        =     _tqdm_cls()


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 global _tqdm_active
 return bool(_tqdm_active					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 global _tqdm_active
 a						=		True
 hf_hub_utils.enable_progress_bars()




def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 global _tqdm_active
 a						=		False
 hf_hub_utils.disable_progress_bars()






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       UNetaDModel							,   __lowerCAmelCase			:       ScoreSdeVeScheduler							)       ->				str:


    """simple docstring"""






    super().__init__()
    self.register_modules(unet=__lowerCAmelCase							,   scheduler=__lowerCAmelCase							)




   @torch.no_grad()
   def __call__(      self			:       int							,   __lowerCAmelCase			:       int = 1							,   __lowerCAmelCase			:       int = 2000							,   __lowerCAmelCase			:       Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   __lowerCAmelCase			:       Optional[str] = "pil"							,   __lowerCAmelCase			:       bool = True							,   **__lowerCAmelCase			:       Any							,   )       ->				Union[ImagePipelineOutput, Tuple]:


    """simple docstring"""






    a						=		self.unet.config.sample_size
    a						=		(batch_size, 3, img_size, img_size)

    a						=		self.unet

    a						=		randn_tensor(__lowerCAmelCase							,   generator=__lowerCAmelCase							) * self.scheduler.init_noise_sigma
    a						=		sample.to(self.device							)

    self.scheduler.set_timesteps(__lowerCAmelCase							)
    self.scheduler.set_sigmas(__lowerCAmelCase							)

    for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
     a						=		self.scheduler.sigmas[i] * torch.ones(shape[0]							,   device=self.device							)

     # correction step
     for _ in range(self.scheduler.config.correct_steps							):
      a						=		self.unet(__lowerCAmelCase							,   __lowerCAmelCase							).sample
      a						=		self.scheduler.step_correct(__lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							).prev_sample

     # prediction step
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							).sample
     a						=		self.scheduler.step_pred(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							)

     a		,  a						=		output.prev_sample, output.prev_sample_mean

    a						=		sample_mean.clamp(0							,   1							)
    a						=		sample.cpu().permute(0							,   2							,   3							,   1							).numpy()
    if output_type == "pil":
     a						=		self.numpy_to_pil(__lowerCAmelCase							)

    if not return_dict:
     return (sample,)

    return ImagePipelineOutput(images=__lowerCAmelCase							)

