


'''simple docstring'''


import collections
import importlib.util
import os
import re
from pathlib import Path


lowerCamelCase__					=   'src/transformers'


# Matches is_xxx_available()
lowerCamelCase__					=   re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
lowerCamelCase__					=   re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCamelCase__					=   re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
lowerCamelCase__					=   re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
lowerCamelCase__					=   re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCamelCase__					=   re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
lowerCamelCase__					=   re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowerCamelCase__					=   re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
lowerCamelCase__					=   re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
lowerCamelCase__					=   re.compile(r'^\s*try:')
# Catches a line with else:
lowerCamelCase__					=   re.compile(r'^\s*else:')






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		if _re_test_backend.search(_lowerCAmelCase      ) is None:
				return None
		_UpperCAmelCase						:				Union[str, Any]	    =					[b[0] for b in _re_backend.findall(_lowerCAmelCase      )]
		backends.sort()
		return "_and_".join(_lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		with open(_lowerCAmelCase					,       "r"					,       encoding="utf-8"					,       newline="\n"      ) as f:
				_UpperCAmelCase						:				List[str]	    =					f.readlines()

		_UpperCAmelCase						:				Optional[Any]	    =					0
		while line_index < len(_lowerCAmelCase      ) and not lines[line_index].startswith("_import_structure = {"      ):
				line_index += 1

		# If this is a traditional init, just return.
		if line_index >= len(_lowerCAmelCase      ):
				return None

		# First grab the objects without a specific backend in _import_structure
		_UpperCAmelCase						:				Any	    =					[]
		while not lines[line_index].startswith("if TYPE_CHECKING"      ) and find_backend(lines[line_index]      ) is None:
				_UpperCAmelCase						:				str	    =					lines[line_index]
				# If we have everything on a single line, let's deal with it.
				if _re_one_line_import_struct.search(_lowerCAmelCase      ):
						_UpperCAmelCase						:				Any	    =					_re_one_line_import_struct.search(_lowerCAmelCase      ).groups()[0]
						_UpperCAmelCase						:				Optional[Any]	    =					re.findall("\[([^\]]+)\]"					,       _lowerCAmelCase      )
						for imp in imports:
								objects.extend([obj[1:-1] for obj in imp.split(", "      )]      )
						line_index += 1
						continue
				_UpperCAmelCase						:				List[str]	    =					_re_import_struct_key_value.search(_lowerCAmelCase      )
				if single_line_import_search is not None:
						_UpperCAmelCase						:				int	    =					[obj[1:-1] for obj in single_line_import_search.groups()[0].split(", "      ) if len(_lowerCAmelCase      ) > 0]
						objects.extend(_lowerCAmelCase      )
				elif line.startswith(" " * 8 + "\""      ):
						objects.append(line[9:-3]      )
				line_index += 1

		_UpperCAmelCase						:				Any	    =					{'none': objects}
		# Let's continue with backend-specific objects in _import_structure
		while not lines[line_index].startswith("if TYPE_CHECKING"      ):
				# If the line is an if not is_backend_available, we grab all objects associated.
				_UpperCAmelCase						:				Union[str, Any]	    =					find_backend(lines[line_index]      )
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]      ) is None:
						_UpperCAmelCase						:				str	    =					None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]      ) is None:
								line_index += 1

						line_index += 1

						_UpperCAmelCase						:				int	    =					[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(" " * 4      ):
								_UpperCAmelCase						:				Dict	    =					lines[line_index]
								if _re_import_struct_add_one.search(_lowerCAmelCase      ) is not None:
										objects.append(_re_import_struct_add_one.search(_lowerCAmelCase      ).groups()[0]      )
								elif _re_import_struct_add_many.search(_lowerCAmelCase      ) is not None:
										_UpperCAmelCase						:				List[Any]	    =					_re_import_struct_add_many.search(_lowerCAmelCase      ).groups()[0].split(", "      )
										_UpperCAmelCase						:				Tuple	    =					[obj[1:-1] for obj in imports if len(_lowerCAmelCase      ) > 0]
										objects.extend(_lowerCAmelCase      )
								elif _re_between_brackets.search(_lowerCAmelCase      ) is not None:
										_UpperCAmelCase						:				Union[str, Any]	    =					_re_between_brackets.search(_lowerCAmelCase      ).groups()[0].split(", "      )
										_UpperCAmelCase						:				Tuple	    =					[obj[1:-1] for obj in imports if len(_lowerCAmelCase      ) > 0]
										objects.extend(_lowerCAmelCase      )
								elif _re_quote_object.search(_lowerCAmelCase      ) is not None:
										objects.append(_re_quote_object.search(_lowerCAmelCase      ).groups()[0]      )
								elif line.startswith(" " * 8 + "\""      ):
										objects.append(line[9:-3]      )
								elif line.startswith(" " * 12 + "\""      ):
										objects.append(line[13:-3]      )
								line_index += 1

						_UpperCAmelCase						:				Tuple	    =					objects
				else:
						line_index += 1

				# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
		_UpperCAmelCase						:				Any	    =					[]
		while (
		    line_index < len(_lowerCAmelCase      )
		    and find_backend(lines[line_index]      ) is None
		    and not lines[line_index].startswith("else"      )
		):
				_UpperCAmelCase						:				Any	    =					lines[line_index]
				_UpperCAmelCase						:				Dict	    =					_re_import.search(_lowerCAmelCase      )
				if single_line_import_search is not None:
						objects.extend(single_line_import_search.groups()[0].split(", "      )      )
				elif line.startswith(" " * 8      ):
						objects.append(line[8:-2]      )
				line_index += 1

		_UpperCAmelCase						:				Dict	    =					{'none': objects}
		# Let's continue with backend-specific objects
		while line_index < len(_lowerCAmelCase      ):
				# If the line is an if is_backend_available, we grab all objects associated.
				_UpperCAmelCase						:				Dict	    =					find_backend(lines[line_index]      )
				# Check if the backend declaration is inside a try block:
				if _re_try.search(lines[line_index - 1]      ) is None:
						_UpperCAmelCase						:				List[str]	    =					None

				if backend is not None:
						line_index += 1

						# Scroll until we hit the else block of try-except-else
						while _re_else.search(lines[line_index]      ) is None:
								line_index += 1

						line_index += 1

						_UpperCAmelCase						:				int	    =					[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(" " * 8      ):
								_UpperCAmelCase						:				Tuple	    =					lines[line_index]
								_UpperCAmelCase						:				Dict	    =					_re_import.search(_lowerCAmelCase      )
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "      )      )
								elif line.startswith(" " * 12      ):
										objects.append(line[12:-2]      )
								line_index += 1

						_UpperCAmelCase						:				Optional[Any]	    =					objects
				else:
						line_index += 1

		return import_dict_objects, type_hint_objects






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):

		def find_duplicates(__lowerCAmelCase      ):
				return [k for k, v in collections.Counter(_lowerCAmelCase      ).items() if v > 1]

		if list(import_dict_objects.keys()      ) != list(type_hint_objects.keys()      ):
				return ["Both sides of the init do not have the same backends!"]

		_UpperCAmelCase						:				Any	    =					[]
		for key in import_dict_objects.keys():
				_UpperCAmelCase						:				Any	    =					find_duplicates(import_dict_objects[key]      )
				if duplicate_imports:
						errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}"""      )
				_UpperCAmelCase						:				Union[str, Any]	    =					find_duplicates(type_hint_objects[key]      )
				if duplicate_type_hints:
						errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"""      )

				if sorted(set(import_dict_objects[key]      )      ) != sorted(set(type_hint_objects[key]      )      ):
						_UpperCAmelCase						:				Dict	    =					'base imports' if key == 'none' else F"""{key} backend"""
						errors.append(F"""Differences for {name}:"""      )
						for a in type_hint_objects[key]:
								if a not in import_dict_objects[key]:
										errors.append(F"""  {a} in TYPE_HINT but not in _import_structure."""      )
						for a in import_dict_objects[key]:
								if a not in type_hint_objects[key]:
										errors.append(F"""  {a} in _import_structure but not in TYPE_HINT."""      )
		return errors






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Union[str, Any]	    =					[]
		for root, _, files in os.walk(_lowerCAmelCase      ):
				if "__init__.py" in files:
						_UpperCAmelCase						:				Tuple	    =					os.path.join(_lowerCAmelCase					,       "__init__.py"      )
						_UpperCAmelCase						:				Tuple	    =					parse_init(_lowerCAmelCase      )
						if objects is not None:
								_UpperCAmelCase						:				List[str]	    =					analyze_results(*_lowerCAmelCase      )
								if len(_lowerCAmelCase      ) > 0:
										_UpperCAmelCase						:				Any	    =					F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
										failures.append("\n".join(_lowerCAmelCase      )      )
		if len(_lowerCAmelCase      ) > 0:
				raise ValueError("\n\n".join(_lowerCAmelCase      )      )






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Tuple	    =					[]
		for path, directories, files in os.walk(_lowerCAmelCase      ):
				for folder in directories:
						# Ignore private modules
						if folder.startswith("_"      ):
								directories.remove(_lowerCAmelCase      )
								continue
						# Ignore leftovers from branches (empty folders apart from pycache)
						if len(list((Path(_lowerCAmelCase      ) / folder).glob("*.py"      )      )      ) == 0:
								continue
						_UpperCAmelCase						:				Union[str, Any]	    =					str((Path(_lowerCAmelCase      ) / folder).relative_to(_lowerCAmelCase      )      )
						_UpperCAmelCase						:				Optional[Any]	    =					short_path.replace(os.path.sep					,       "."      )
						submodules.append(_lowerCAmelCase      )
				for fname in files:
						if fname == "__init__.py":
								continue
						_UpperCAmelCase						:				List[str]	    =					str((Path(_lowerCAmelCase      ) / fname).relative_to(_lowerCAmelCase      )      )
						_UpperCAmelCase						:				int	    =					short_path.replace(".py"					,       ""      ).replace(os.path.sep					,       "."      )
						if len(submodule.split("."      )      ) == 1:
								submodules.append(_lowerCAmelCase      )
		return submodules


lowerCamelCase__					=   [
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]






def        __lowerCAmelCase       ():
		# This is to make sure the transformers module imported is the one in the repo.
		_UpperCAmelCase						:				Any	    =					importlib.util.spec_from_file_location(
		    "transformers"					,       os.path.join(_lowerCAmelCase					,       "__init__.py"      )					,       submodule_search_locations=[PATH_TO_TRANSFORMERS]					,       )
		_UpperCAmelCase						:				Union[str, Any]	    =					spec.loader.load_module()

		_UpperCAmelCase						:				Optional[int]	    =					[
		    module
		    for module in get_transformers_submodules()
		    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
		]
		if len(_lowerCAmelCase      ) > 0:
				_UpperCAmelCase						:				int	    =					'\n'.join(F"""- {module}""" for module in module_not_registered      )
				raise ValueError(
				    "The following submodules are not properly registered in the main init of Transformers:\n"
				    F"""{list_of_modules}\n"""
				    "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."      )


if __name__ == "__main__":
						check_all_inits()
						check_submodules()








'''simple docstring'''





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


_UpperCamelCase											=						8

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Any:
  __lowerCamelCase       :       List[str]           = x.device

  __lowerCamelCase       :       int           = (x * 255).int().clamp(0       ,255       )

  __lowerCamelCase       :       Any           = 2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=_lowerCAmelCase       )
  __lowerCamelCase       :       List[str]           = rearrange(_lowerCAmelCase       ,'d -> d 1 1'       )
  __lowerCamelCase       :       Optional[Any]           = rearrange(_lowerCAmelCase       ,'b c h w -> b c 1 h w'       )

  __lowerCamelCase       :       Optional[Any]           = ((x & mask) != 0).float()
  __lowerCamelCase       :       Tuple           = rearrange(_lowerCAmelCase       ,'b c d h w -> b (c d) h w'       )
  __lowerCamelCase       :       Union[str, Any]           = bits * 2 - 1
  return bits

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Optional[Any]:
  __lowerCamelCase       :       int           = x.device

  __lowerCamelCase       :       int           = (x > 0).int()
  __lowerCamelCase       :       List[str]           = 2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=_lowerCAmelCase       ,dtype=torch.intaa       )

  __lowerCamelCase       :       Any           = rearrange(_lowerCAmelCase       ,'d -> d 1 1'       )
  __lowerCamelCase       :       Any           = rearrange(_lowerCAmelCase       ,'b (c d) h w -> b c d h w'       ,d=8       )
  __lowerCamelCase       :       List[str]           = reduce(x * mask       ,'b c d h w -> b c h w'       ,'sum'       )
  return (dec / 255).clamp(0.0       ,1.0       )

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 0.0       ,_lowerCAmelCase = True       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDIMSchedulerOutput, Tuple]:
  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)
  __lowerCamelCase       :       Optional[Any]           = timestep - self.config.num_train_timesteps // self.num_inference_steps

  # 2. compute alphas, betas
  __lowerCamelCase       :       str           = self.alphas_cumprod[timestep]
  __lowerCamelCase       :       Optional[Any]           = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

  __lowerCamelCase       :       Union[str, Any]           = 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
  __lowerCamelCase       :       Optional[int]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  __lowerCamelCase       :       Dict           = self.bit_scale
  if self.config.clip_sample:
    __lowerCamelCase       :       Optional[Any]           = 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)
  __lowerCamelCase       :       int           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       )
  __lowerCamelCase       :       Tuple           = eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    __lowerCamelCase       :       List[str]           = (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
  __lowerCamelCase       :       Union[str, Any]           = (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
  __lowerCamelCase       :       Any           = 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
    __lowerCamelCase       :       Dict           = model_output.device if torch.is_tensor(_lowerCAmelCase       ) else 'cpu'
    __lowerCamelCase       :       Dict           = torch.randn(model_output.shape       ,dtype=model_output.dtype       ,generator=_lowerCAmelCase       ).to(_lowerCAmelCase       )
    __lowerCamelCase       :       Optional[Any]           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       ) ** 0.5 * eta * noise

    __lowerCamelCase       :       int           = prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase="epsilon"       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDPMSchedulerOutput, Tuple]:
  __lowerCamelCase       :       Dict           = timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    __lowerCamelCase       ,__lowerCamelCase       :       int           = torch.split(_lowerCAmelCase       ,sample.shape[1]       ,dim=1       )
  else:
    __lowerCamelCase       :       Optional[Any]           = None

  # 1. compute alphas, betas
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t]
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t - 1] if t > 0 else self.one
  __lowerCamelCase       :       Any           = 1 - alpha_prod_t
  __lowerCamelCase       :       Any           = 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":
    __lowerCamelCase       :       List[Any]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    __lowerCamelCase       :       Any           = model_output
  else:
    raise ValueError(F'Unsupported prediction_type {prediction_type}.'       )

  # 3. Clip "predicted x_0"
  __lowerCamelCase       :       Optional[Any]           = self.bit_scale
  if self.config.clip_sample:
    __lowerCamelCase       :       Union[str, Any]           = 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
  __lowerCamelCase       :       Union[str, Any]           = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  __lowerCamelCase       :       Dict           = 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
  __lowerCamelCase       :       Dict           = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  __lowerCamelCase       :       Optional[Any]           = 0
  if t > 0:
    __lowerCamelCase       :       Any           = torch.randn(
        model_output.size()       ,dtype=model_output.dtype       ,layout=model_output.layout       ,generator=_lowerCAmelCase       ).to(model_output.device       )
    __lowerCamelCase       :       Optional[Any]           = (self._get_variance(_lowerCAmelCase       ,predicted_variance=_lowerCAmelCase       ) ** 0.5) * noise

  __lowerCamelCase       :       str           = pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	List[str]			,	_a      :	UNetaDConditionModel			,	_a      :	Union[DDIMScheduler, DDPMScheduler]			,	_a      :	Optional[float] = 1.0			,	)					->					Union[str, Any]:
    super().__init__()
    __lowerCamelCase       :       Dict           = bit_scale
    __lowerCamelCase       :       List[str]           = (
        ddim_bit_scheduler_step if isinstance(_a			,	_a   ) else ddpm_bit_scheduler_step
    )

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




  @torch.no_grad()
  def __call__( self      :	Tuple			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 50			,	_a      :	Optional[torch.Generator] = None			,	_a      :	Optional[int] = 1			,	_a      :	Optional[str] = "pil"			,	_a      :	bool = True			,	**_a      :	Dict			,	)					->					Union[Tuple, ImagePipelineOutput]:
    __lowerCamelCase       :       str           = torch.randn(
        (batch_size, self.unet.config.in_channels, height, width)			,	generator=_a			,	)
    __lowerCamelCase       :       int           = decimal_to_bits(_a   ) * self.bit_scale
    __lowerCamelCase       :       Dict           = latents.to(self.device   )

    self.scheduler.set_timesteps(_a   )

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

      # compute the previous noisy sample x_t -> x_t-1
      __lowerCamelCase       :       List[Any]           = self.scheduler.step(_a			,	_a			,	_a   ).prev_sample

    __lowerCamelCase       :       Dict           = bits_to_decimal(_a   )

    if output_type == "pil":
      __lowerCamelCase       :       int           = self.numpy_to_pil(_a   )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a   )






