





'''simple docstring'''

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   	=    threading.Lock()
a   	=    None

a   	=    {
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

a   	=    logging.WARNING

a   	=    True

def     __magic_name__							(				)    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							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     __magic_name__							(				)    ->       str:

   '''simple docstring'''







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

def     __magic_name__							(				)    ->       logging.Logger:

   '''simple docstring'''







   return logging.getLogger(_get_library_name()   )

def     __magic_name__							(				)    ->       None:

   '''simple docstring'''







   global _default_handler

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

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

def     __magic_name__							(				)    ->       None:

   '''simple docstring'''







   global _default_handler

   with _lock:
      if not _default_handler:
         return

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

def     __magic_name__							(				)    ->       int:

   '''simple docstring'''







   return log_levels

def     __magic_name__							(				__UpperCAmelCase = None   )    ->       logging.Logger:

   '''simple docstring'''







   if name is None:
      __SCREAMING_SNAKE_CASE   				=							_get_library_name()

   _configure_library_root_logger()
   return logging.getLogger(__UpperCAmelCase   )

def     __magic_name__							(				)    ->       int:

   '''simple docstring'''







   _configure_library_root_logger()
   return _get_library_root_logger().getEffectiveLevel()

def     __magic_name__							(				__UpperCAmelCase   )    ->       None:

   '''simple docstring'''







   _configure_library_root_logger()
   _get_library_root_logger().setLevel(__UpperCAmelCase   )

def     __magic_name__							(				)    ->       str:

   '''simple docstring'''







   return set_verbosity(__UpperCAmelCase   )

def     __magic_name__							(				)    ->       Union[str, Any]:

   '''simple docstring'''







   return set_verbosity(__UpperCAmelCase   )

def     __magic_name__							(				)    ->       Optional[int]:

   '''simple docstring'''







   return set_verbosity(__UpperCAmelCase   )

def     __magic_name__							(				)    ->       Tuple:

   '''simple docstring'''







   return set_verbosity(__UpperCAmelCase   )

def     __magic_name__							(				)    ->       None:

   '''simple docstring'''







   _configure_library_root_logger()

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

def     __magic_name__							(				)    ->       None:

   '''simple docstring'''







   _configure_library_root_logger()

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

def     __magic_name__							(				__UpperCAmelCase   )    ->       None:

   '''simple docstring'''







   _configure_library_root_logger()

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

def     __magic_name__							(				__UpperCAmelCase   )    ->       None:

   '''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     __magic_name__							(				)    ->       None:

   '''simple docstring'''







   _configure_library_root_logger()
   __SCREAMING_SNAKE_CASE   				=							False

def     __magic_name__							(				)    ->       None:

   '''simple docstring'''







   _configure_library_root_logger()
   __SCREAMING_SNAKE_CASE   				=							True

def     __magic_name__							(				)    ->       None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							_get_library_root_logger().handlers

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

def     __magic_name__							(				)    ->       None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							_get_library_root_logger().handlers

   for handler in handlers:
      handler.setFormatter(__UpperCAmelCase   )

def     __magic_name__							(				self				,						*__UpperCAmelCase				,						**__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







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


a   	=    warning_advice

@functools.lru_cache(__UpperCAmelCase   )
def     __magic_name__							(				self				,						*__UpperCAmelCase				,						**__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   self.warning(*__UpperCAmelCase				,						**__UpperCAmelCase   )


a   	=    warning_once







class __a			:





   def __init__(       self					:      List[Any]					,*lowerCamelCase					:      Union[str, Any]					,**lowerCamelCase					:      Optional[int]     ):  # pylint: disable=unused-argument

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							args[0] if args else None





   def __iter__(       self					:      Optional[int]     ):

      '''simple docstring'''

      return iter(self._iterator     )





   def __getattr__(       self					:      List[str]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      def empty_fn(*lowerCamelCase					:      Any					,**lowerCamelCase					:      Tuple     ):  # pylint: disable=unused-argument
         return

      return empty_fn





   def __enter__(       self					:      List[Any]     ):

      '''simple docstring'''

      return self





   def __exit__(       self					:      List[Any]					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      return







class __a			:





   def __call__(       self					:      Optional[Any]					,*lowerCamelCase					:      Optional[Any]					,**lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

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





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							None
      if _tqdm_active:
         return tqdm_lib.tqdm.set_lock(*lowerCamelCase					,**lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      if _tqdm_active:
         return tqdm_lib.tqdm.get_lock()





a   	=    _tqdm_cls()

def     __magic_name__							(				)    ->       bool:

   '''simple docstring'''







   global _tqdm_active
   return bool(_tqdm_active   )

def     __magic_name__							(				)    ->       Union[str, Any]:

   '''simple docstring'''







   global _tqdm_active
   __SCREAMING_SNAKE_CASE   				=							True
   hf_hub_utils.enable_progress_bars()







def     __magic_name__							(				)    ->       Tuple:

   '''simple docstring'''







   global _tqdm_active
   __SCREAMING_SNAKE_CASE   				=							False
   hf_hub_utils.disable_progress_bars()













from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


A__      :     Union[str, Any]       =					transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				if isinstance(lowerCamelCase_						,  torch.Tensor							):
								return image
				elif isinstance(lowerCamelCase_						,  PIL.Image.Image							):
								lowercase__  					=     [image]

				lowercase__  					=     [trans(img.convert('''RGB'''							)							) for img in image]
				lowercase__  					=     torch.stack(lowerCamelCase_							)
				return image




class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def __init__(	self		:       Tuple,    lowerCamelCase		:       Dict,    lowerCamelCase		:       str    ):

								'''simple docstring'''
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								lowercase__  					=     DDIMScheduler.from_config(scheduler.config    )

								self.register_modules(unet=lowerCamelCase,    scheduler=lowerCamelCase    )




				def 							lowercase__  (	self		:       int,    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								if strength < 0 or strength > 1:
												raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""    )




				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								# get the original timestep using init_timestep
								lowercase__  					=     min(int(num_inference_steps * strength    ),    lowerCamelCase    )

								lowercase__  					=     max(num_inference_steps - init_timestep,    0    )
								lowercase__  					=     self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       List[Any],    lowerCamelCase		:       int,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Any,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Any=None    ):

								'''simple docstring'''
								if not isinstance(lowerCamelCase,    (torch.Tensor, PIL.Image.Image, list)    ):
												raise ValueError(
												    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase    )}"""    )

								lowercase__  					=     image.to(device=lowerCamelCase,    dtype=lowerCamelCase    )

								if isinstance(lowerCamelCase,    lowerCamelCase    ) and len(lowerCamelCase    ) != batch_size:
												raise ValueError(
												    F"""You have passed a list of generators of length {len(lowerCamelCase    )}, but requested an effective batch"""
												    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

								lowercase__  					=     init_latents.shape
								lowercase__  					=     randn_tensor(lowerCamelCase,    generator=lowerCamelCase,    device=lowerCamelCase,    dtype=lowerCamelCase    )

								# get latents
								print('''add noise to latents at timestep''',    lowerCamelCase    )
								lowercase__  					=     self.scheduler.add_noise(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    )
								lowercase__  					=     init_latents

								return latents




				@torch.no_grad()
				def __call__(	self		:       Dict,    lowerCamelCase		:       Union[torch.FloatTensor, PIL.Image.Image] = None,    lowerCamelCase		:       float = 0.8,    lowerCamelCase		:       int = 1,    lowerCamelCase		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None,    lowerCamelCase		:       float = 0.0,    lowerCamelCase		:       int = 50,    lowerCamelCase		:       Optional[bool] = None,    lowerCamelCase		:       Optional[str] = "pil",    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								self.check_inputs(lowerCamelCase    )

								# 2. Preprocess image
								lowercase__  					=     preprocess(lowerCamelCase    )

								# 3. set timesteps
								self.scheduler.set_timesteps(lowerCamelCase,    device=self.device    )
								lowercase__					,		lowercase__  					=     self.get_timesteps(lowerCamelCase,    lowerCamelCase,    self.device    )
								lowercase__  					=     timesteps[:1].repeat(lowerCamelCase    )

								# 4. Prepare latent variables
								lowercase__  					=     self.prepare_latents(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    self.unet.dtype,    self.device,    lowerCamelCase    )
								lowercase__  					=     latents

								# 5. Denoising loop
								for t in self.progress_bar(lowerCamelCase    ):
												# 1. predict noise model_output
												lowercase__  					=     self.unet(lowerCamelCase,    lowerCamelCase    ).sample

												# 2. predict previous mean of image x_t-1 and add variance depending on eta
												# eta corresponds to η in paper and should be between [0, 1]
												# do x_t -> x_t-1
												lowercase__  					=     self.scheduler.step(
												    lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    eta=lowerCamelCase,    use_clipped_model_output=lowerCamelCase,    generator=lowerCamelCase,    ).prev_sample

								lowercase__  					=     (image / 2 + 0.5).clamp(0,    1    )
								lowercase__  					=     image.cpu().permute(0,    2,    3,    1    ).numpy()
								if output_type == "pil":
												lowercase__  					=     self.numpy_to_pil(lowerCamelCase    )

								if not return_dict:
												return (image, latent_timestep.item())

								return ImagePipelineOutput(images=lowerCamelCase    )




