
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


__magic_name__            =  threading.Lock()
__magic_name__            =  None

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

__magic_name__            =  logging.WARNING

__magic_name__            =  True





def 	SCREAMING_SNAKE_CASE__    (      ):
 snake_case__						=				os.getenv("TRANSFORMERS_VERBOSITY"    ,		a_							)
 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 	SCREAMING_SNAKE_CASE__    (      ):
 return __name__.split("."							)[0]





def 	SCREAMING_SNAKE_CASE__    (      ):
 return logging.getLogger(_get_library_name()							)





def 	SCREAMING_SNAKE_CASE__    (      ):
 global _default_handler

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

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





def 	SCREAMING_SNAKE_CASE__    (      ):
 global _default_handler

 with _lock:
  if not _default_handler:
   return

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





def 	SCREAMING_SNAKE_CASE__    (      ):
 return log_levels





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase = None							):

 if name is None:
  snake_case__						=				_get_library_name()

 _configure_library_root_logger()
 return logging.getLogger(a_							)





def 	SCREAMING_SNAKE_CASE__    (      ):
 _configure_library_root_logger()
 return _get_library_root_logger().getEffectiveLevel()





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
 _configure_library_root_logger()
 _get_library_root_logger().setLevel(a_							)





def 	SCREAMING_SNAKE_CASE__    (      ):
 return set_verbosity(a_							)





def 	SCREAMING_SNAKE_CASE__    (      ):
 return set_verbosity(a_							)





def 	SCREAMING_SNAKE_CASE__    (      ):
 return set_verbosity(a_							)





def 	SCREAMING_SNAKE_CASE__    (      ):
 return set_verbosity(a_							)





def 	SCREAMING_SNAKE_CASE__    (      ):
 _configure_library_root_logger()

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





def 	SCREAMING_SNAKE_CASE__    (      ):
 _configure_library_root_logger()

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
 _configure_library_root_logger()

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
 _configure_library_root_logger()

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





def 	SCREAMING_SNAKE_CASE__    (      ):
 _configure_library_root_logger()
 snake_case__						=				False





def 	SCREAMING_SNAKE_CASE__    (      ):
 _configure_library_root_logger()
 snake_case__						=				True





def 	SCREAMING_SNAKE_CASE__    (      ):
 snake_case__						=				_get_library_root_logger().handlers

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





def 	SCREAMING_SNAKE_CASE__    (      ):
 snake_case__						=				_get_library_root_logger().handlers

 for handler in handlers:
  handler.setFormatter(a_							)





def 	SCREAMING_SNAKE_CASE__    (      self    ,		*__lowerCAmelCase    ,		**__lowerCAmelCase							):
 snake_case__						=				os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS"    ,		a_							)
 if no_advisory_warnings:
  return
 self.warning(*a_    ,		**a_							)


__magic_name__            =  warning_advice





@functools.lru_cache(a_							)
def 	SCREAMING_SNAKE_CASE__    (      self    ,		*__lowerCAmelCase    ,		**__lowerCAmelCase							):
 self.warning(*a_    ,		**a_							)


__magic_name__            =  warning_once




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	*lowerCamelCase		,	**lowerCamelCase ):  # pylint: disable=unused-argument
       snake_case__						=				args[0] if args else None
      def __iter__(			self ):
       return iter(self._iterator )
      def __getattr__(			self		,	lowerCamelCase ):

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

       return empty_fn
      def __enter__(			self ):
       return self



      def __exit__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
       return





class 					_SCREAMING_SNAKE_CASE  :
      def __call__(			self		,	*lowerCamelCase		,	**lowerCamelCase ):
       if _tqdm_active:
        return tqdm_lib.tqdm(*_UpperCAmelCase		,	**_UpperCAmelCase )
       else:
        return EmptyTqdm(*_UpperCAmelCase		,	**_UpperCAmelCase )
      def A_			(			self		,	*lowerCamelCase		,	**lowerCamelCase ):
       snake_case__						=				None
       if _tqdm_active:
        return tqdm_lib.tqdm.set_lock(*_UpperCAmelCase		,	**_UpperCAmelCase )



      def A_			(			self ):
       if _tqdm_active:
        return tqdm_lib.tqdm.get_lock()





__magic_name__            =  _tqdm_cls()





def 	SCREAMING_SNAKE_CASE__    (      ):
 global _tqdm_active
 return bool(_tqdm_active							)





def 	SCREAMING_SNAKE_CASE__    (      ):
 global _tqdm_active
 snake_case__						=				True
 hf_hub_utils.enable_progress_bars()





def 	SCREAMING_SNAKE_CASE__    (      ):
 global _tqdm_active
 snake_case__						=				False
 hf_hub_utils.disable_progress_bars()





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__magic_name__            =  8





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase=BITS							):
    snake_case__						=				x.device

    snake_case__						=				(x * 255).int().clamp(0    ,		255							)

    snake_case__						=				2 ** torch.arange(bits - 1    ,		-1    ,		-1    ,		device=__lowerCAmelCase							)
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"d -> d 1 1"							)
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"b c h w -> b c 1 h w"							)

    snake_case__						=				((x & mask) != 0).float()
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"b c d h w -> b (c d) h w"							)
    snake_case__						=				bits * 2 - 1
    return bits





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase=BITS							):
    snake_case__						=				x.device

    snake_case__						=				(x > 0).int()
    snake_case__						=				2 ** torch.arange(bits - 1    ,		-1    ,		-1    ,		device=__lowerCAmelCase    ,		dtype=torch.intaa							)

    snake_case__						=				rearrange(__lowerCAmelCase    ,		"d -> d 1 1"							)
    snake_case__						=				rearrange(__lowerCAmelCase    ,		"b (c d) h w -> b c d h w"    ,		d=8							)
    snake_case__						=				reduce(x * mask    ,		"b c d h w -> b c h w"    ,		"sum"							)
    return (dec / 255).clamp(0.0    ,		1.0							)





def 	SCREAMING_SNAKE_CASE__    (      self    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase = 0.0    ,		__lowerCAmelCase = True    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase = True    ,		):
    if self.num_inference_steps is None:
        raise ValueError(
            "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"							)

    # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
    # Ideally, read DDIM paper in-detail understanding

    # Notation (<variable name> -> <name in paper>
    # - pred_noise_t -> e_theta(x_t, t)
    # - pred_original_sample -> f_theta(x_t, t) or x_0
    # - std_dev_t -> sigma_t
    # - eta -> η
    # - pred_sample_direction -> "direction pointing to x_t"
    # - pred_prev_sample -> "x_t-1"

    # 1. get previous step value (=t-1)
    snake_case__						=				timestep - self.config.num_train_timesteps // self.num_inference_steps

    # 2. compute alphas, betas
    snake_case__						=				self.alphas_cumprod[timestep]
    snake_case__						=				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

    snake_case__						=				1 - alpha_prod_t

    # 3. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    snake_case__						=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

    # 4. Clip "predicted x_0"
    snake_case__						=				self.bit_scale
    if self.config.clip_sample:
        snake_case__						=				torch.clamp(__lowerCAmelCase    ,		-scale    ,		__lowerCAmelCase							)

    # 5. compute variance: "sigma_t(η)" -> see formula (16)
    # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
    snake_case__						=				self._get_variance(__lowerCAmelCase    ,		__lowerCAmelCase							)
    snake_case__						=				eta * variance ** 0.5

    if use_clipped_model_output:
        # the model_output is always re-derived from the clipped x_0 in Glide
        snake_case__						=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    snake_case__						=				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

    # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    snake_case__						=				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

    if eta > 0:
        # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
        snake_case__						=				model_output.device if torch.is_tensor(__lowerCAmelCase							) else "cpu"
        snake_case__						=				torch.randn(model_output.shape    ,		dtype=model_output.dtype    ,		generator=__lowerCAmelCase							).to(__lowerCAmelCase							)
        snake_case__						=				self._get_variance(__lowerCAmelCase    ,		__lowerCAmelCase							) ** 0.5 * eta * noise

        snake_case__						=				prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=__lowerCAmelCase    ,		pred_original_sample=__lowerCAmelCase							)





def 	SCREAMING_SNAKE_CASE__    (      self    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase="epsilon"    ,		__lowerCAmelCase=None    ,		__lowerCAmelCase = True    ,		):
    snake_case__						=				timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
        snake_case__						,					snake_case__						=				torch.split(__lowerCAmelCase    ,		sample.shape[1]    ,		dim=1							)
    else:
        snake_case__						=				None

    # 1. compute alphas, betas
    snake_case__						=				self.alphas_cumprod[t]
    snake_case__						=				self.alphas_cumprod[t - 1] if t > 0 else self.one
    snake_case__						=				1 - alpha_prod_t
    snake_case__						=				1 - alpha_prod_t_prev

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if prediction_type == "epsilon":
        snake_case__						=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif prediction_type == "sample":
        snake_case__						=				model_output
    else:
        raise ValueError(F"""Unsupported prediction_type {prediction_type}."""							)

    # 3. Clip "predicted x_0"
    snake_case__						=				self.bit_scale
    if self.config.clip_sample:
        snake_case__						=				torch.clamp(__lowerCAmelCase    ,		-scale    ,		__lowerCAmelCase							)

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    snake_case__						=				(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
    snake_case__						=				self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    snake_case__						=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    snake_case__						=				0
    if t > 0:
        snake_case__						=				torch.randn(
            model_output.size()    ,		dtype=model_output.dtype    ,		layout=model_output.layout    ,		generator=__lowerCAmelCase							).to(model_output.device							)
        snake_case__						=				(self._get_variance(__lowerCAmelCase    ,		predicted_variance=__lowerCAmelCase							) ** 0.5) * noise

    snake_case__						=				pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=__lowerCAmelCase    ,		pred_original_sample=__lowerCAmelCase							)





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase = 1.0		,	):
          super().__init__()
          snake_case__						=				bit_scale
          snake_case__						=				(
              ddim_bit_scheduler_step if isinstance(lowerCamelCase		,	lowerCamelCase ) else ddpm_bit_scheduler_step
          )

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



      @torch.no_grad()
      def __call__(			self		,	lowerCamelCase = 2_56		,	lowerCamelCase = 2_56		,	lowerCamelCase = 50		,	lowerCamelCase = None		,	lowerCamelCase = 1		,	lowerCamelCase = "pil"		,	lowerCamelCase = True		,	**lowerCamelCase		,	):
          snake_case__						=				torch.randn(
              (batch_size, self.unet.config.in_channels, height, width)		,	generator=lowerCamelCase		,	)
          snake_case__						=				decimal_to_bits(lowerCamelCase ) * self.bit_scale
          snake_case__						=				latents.to(self.device )

          self.scheduler.set_timesteps(lowerCamelCase )

          for t in self.progress_bar(self.scheduler.timesteps ):
              # predict the noise residual
              snake_case__						=				self.unet(lowerCamelCase		,	lowerCamelCase ).sample

              # compute the previous noisy sample x_t -> x_t-1
              snake_case__						=				self.scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ).prev_sample

          snake_case__						=				bits_to_decimal(lowerCamelCase )

          if output_type == "pil":
              snake_case__						=				self.numpy_to_pil(lowerCamelCase )

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowerCamelCase )



