code: 
  
  
  
  
  
  
  """simple docstring"""
  
  import argparse
  import glob
  import inspect
  import os
  import re
  import unittest
  
  from get_test_info import get_test_classes
  
  from tests.test_pipeline_mixin import pipeline_test_mapping
  
  
  lowercase__      =				{}
  for task, _ in pipeline_test_mapping.items():
  					lowercase__      =				{"""pt""": None, """tf""": None}
  
  
  # DO **NOT** add item to this set (unless the reason is approved)
  lowercase__      =				{
      """tests/models/esm/test_modeling_esmfold.py""",  # The pipeline test mapping is added to `test_modeling_esm.py`
  }
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					str:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
  				return "pt"
  		elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
  				return "tf"
  		elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
  				return "flax"
  		else:
  				return None
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					Dict:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if PIPELINE_TEST_MAPPING[task].get(lowerCAmelCase__   , lowerCAmelCase__ ) is not None:
  				return PIPELINE_TEST_MAPPING[task][framework]
  
  		lowerCAmelCase_     :							Tuple             =    pipeline_test_mapping[task]['test']
  		lowerCAmelCase_     :							Optional[int]             =    None
  
  		if framework == "pt":
  				lowerCAmelCase_     :							Tuple             =    getattr(lowerCAmelCase__   , "model_mapping"   , lowerCAmelCase__ )
  		elif framework == "tf":
  				lowerCAmelCase_     :							List[Any]             =    getattr(lowerCAmelCase__   , "tf_model_mapping"   , lowerCAmelCase__ )
  
  		if mapping is not None:
  				lowerCAmelCase_     :							Any             =    dict(mapping.items() )
  
  		# cache the results
  		lowerCAmelCase_     :							Tuple             =    mapping
  		return mapping
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					Tuple:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							str             =    get_framework(lowerCAmelCase__ )
  		if framework is None:
  				return None
  		lowerCAmelCase_     :							str             =    get_mapping_for_task(lowerCAmelCase__   , lowerCAmelCase__ )
  		if mapping is None:
  				return None
  
  		lowerCAmelCase_     :							int             =    list({model_class.config_class for model_class in test_class.all_model_classes} )
  		if len(lowerCAmelCase__ ) != 1:
  				raise ValueError("There should be exactly one configuration class from `test_class.all_model_classes`." )
  
  		# This could be a list/tuple of model classes, but it's rare.
  		lowerCAmelCase_     :							int             =    mapping.get(config_classes[0]   , lowerCAmelCase__ )
  		if isinstance(lowerCAmelCase__   , (tuple, list) ):
  				lowerCAmelCase_     :							Tuple             =    sorted(lowerCAmelCase__   , key=lambda __UpperCamelCase : x.__name__ )
  
  		return model_class
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					Tuple:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Tuple             =    [(task, get_model_for_pipeline_test(lowerCAmelCase__   , lowerCAmelCase__ )) for task in pipeline_test_mapping]
  		lowerCAmelCase_     :							int             =    sorted([(task, model) for task, model in mapping if model is not None]   , key=lambda __UpperCamelCase : x[0] )
  
  		return dict(lowerCAmelCase__ )
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					Union[str, Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							str             =    get_framework(lowerCAmelCase__ )
  		if framework == "pt":
  				lowerCAmelCase_     :							str             =    'torch'
  		lowerCAmelCase_     :							Any             =    '{}'
  
  		lowerCAmelCase_     :							Optional[int]             =    get_pipeline_model_mapping(lowerCAmelCase__ )
  		if len(lowerCAmelCase__ ) == 0:
  				return ""
  
  		lowerCAmelCase_     :							Optional[int]             =    []
  		for task, model_classes in mapping.items():
  				if isinstance(lowerCAmelCase__   , (tuple, list) ):
  						# A list/tuple of model classes
  						lowerCAmelCase_     :							Tuple             =    '(' + ', '.join([x.__name__ for x in model_classes] ) + ')'
  				else:
  						# A single model class
  						lowerCAmelCase_     :							List[str]             =    model_classes.__name__
  				texts.append(f'''\"{task}\": {value}''' )
  		lowerCAmelCase_     :							str             =    '{' + ', '.join(lowerCAmelCase__ ) + '}'
  		lowerCAmelCase_     :							Union[str, Any]             =    f'''pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}'''
  
  		return text
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					Optional[Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Optional[int]             =    {'ModelTesterMixin', 'TFModelTesterMixin', 'FlaxModelTesterMixin'}
  		if not issubclass(lowerCAmelCase__   , unittest.TestCase ):
  				return False
  		return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__] ) ) > 0
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					Any:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Tuple             =    [x for x in get_test_classes(lowerCAmelCase__ ) if is_valid_test_class(lowerCAmelCase__ )]
  
  		lowerCAmelCase_     :							str             =    None
  		for test_class in test_classes:
  				# If a test class has defined `pipeline_model_mapping`, let's take it
  				if getattr(lowerCAmelCase__   , "pipeline_model_mapping"   , lowerCAmelCase__ ) is not None:
  						lowerCAmelCase_     :							str             =    test_class
  						break
      # Take the test class with the shortest name (just a heuristic)
  		if target_test_class is None and len(lowerCAmelCase__ ) > 0:
  				lowerCAmelCase_     :							Dict             =    sorted(lowerCAmelCase__   , key=lambda __UpperCamelCase : (len(x.__name__ ), x.__name__) )[0]
  
  		return target_test_class
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )		->					Optional[Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							List[Any]             =    start_idx
  		for idx, line in enumerate(lines[start_idx:] ):
  				lowerCAmelCase_     :							Tuple             =    len(lowerCAmelCase__ ) - len(line.lstrip() )
  				if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
  						lowerCAmelCase_     :							Optional[int]             =    start_idx + idx
  				elif idx > 0 and indent <= indent_level:
  						# Outside the definition block of `pipeline_model_mapping`
  						break
  
  		return end_idx
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase=False )		->					Dict:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if getattr(lowerCAmelCase__   , "pipeline_model_mapping"   , lowerCAmelCase__ ) is not None:
  				if not overwrite:
  						return "", -1
  
  		lowerCAmelCase_     :							List[str]             =    get_pipeline_model_mapping_string(lowerCAmelCase__ )
  		if len(lowerCAmelCase__ ) == 0:
  				return "", -1
  		lowerCAmelCase_     :							List[str]             =    line_to_add + '\n'
  
  		# The code defined the class `test_class`
  		lowerCAmelCase_     :							List[str]             =    inspect.getsourcelines(lowerCAmelCase__ )
  		# `inspect` gives the code for an object, including decorator(s) if any.
  		# We (only) need the exact line of the class definition.
  		for idx, line in enumerate(lowerCAmelCase__ ):
  				if line.lstrip().startswith("class " ):
  						lowerCAmelCase_     :							Optional[Any]             =    class_lines[idx:]
  						class_start_line_no += idx
  						break
  		lowerCAmelCase_     :							Any             =    class_start_line_no + len(lowerCAmelCase__ ) - 1
  
  		# The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
  		# `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
  		# block that appears in a `test_class`.
  		lowerCAmelCase_     :							str             =    None
  		# The indent level of the line at `class_lines[start_idx]` (if defined)
  		lowerCAmelCase_     :							Tuple             =    0
  		# To record if `pipeline_model_mapping` is found in `test_class`.
  		lowerCAmelCase_     :							Optional[Any]             =    None
  		for idx, line in enumerate(lowerCAmelCase__ ):
  				if line.strip().startswith("all_model_classes = " ):
  						lowerCAmelCase_     :							Optional[int]             =    len(lowerCAmelCase__ ) - len(line.lstrip() )
  						lowerCAmelCase_     :							Tuple             =    idx
  				elif line.strip().startswith("all_generative_model_classes = " ):
  						lowerCAmelCase_     :							Tuple             =    len(lowerCAmelCase__ ) - len(line.lstrip() )
  						lowerCAmelCase_     :							Dict             =    idx
  				elif line.strip().startswith("pipeline_model_mapping = " ):
  						lowerCAmelCase_     :							Optional[Any]             =    len(lowerCAmelCase__ ) - len(line.lstrip() )
  						lowerCAmelCase_     :							Union[str, Any]             =    idx
  						lowerCAmelCase_     :							List[str]             =    line
  						break
  
  		if start_idx is None:
  				return "", -1
  		# Find the ending index (inclusive) of the above found block.
  		lowerCAmelCase_     :							Optional[int]             =    find_block_ending(lowerCAmelCase__   , lowerCAmelCase__   , lowerCAmelCase__ )
  
  		# Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
  		# `is_timm_available()` instead of `is_torch_available()`.
  		# Keep leading and trailing whitespaces
  		lowerCAmelCase_     :							Any             =    re.compile(r"\s(is_\S+?_available\(\))\s" )
  		for line in class_lines[start_idx : end_idx + 1]:
  				lowerCAmelCase_     :							List[str]             =    r.search(lowerCAmelCase__ )
  				if backend_condition is not None:
  						# replace the leading and trailing whitespaces to the space character " ".
  						lowerCAmelCase_     :							str             =    ' ' + backend_condition[0][1:-1] + ' '
  						lowerCAmelCase_     :							int             =    r.sub(lowerCAmelCase__   , lowerCAmelCase__ )
  						break
  
  		if def_line is None:
  				# `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
  				# `all_model_classes` or `all_generative_model_classes`.
  				lowerCAmelCase_     :							List[str]             =    end_idx
  		else:
  				# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
  				lowerCAmelCase_     :							Tuple             =    start_idx - 1
  				# mark the lines of the currently existing `pipeline_model_mapping` to be removed.
  				for idx in range(lowerCAmelCase__   , end_idx + 1 ):
  						# These lines are going to be removed before writing to the test file.
  						lowerCAmelCase_     :							List[Any]             =    None  # noqa
  
      # Make sure the test class is a subclass of `PipelineTesterMixin`.
  		lowerCAmelCase_     :							List[Any]             =    [x.__name__ for x in test_class.__bases__]
  		if "PipelineTesterMixin" not in parent_classes:
  				# Put `PipelineTesterMixin` just before `unittest.TestCase`
  				lowerCAmelCase_     :							List[Any]             =    [x for x in parent_classes if x != 'TestCase'] + ['PipelineTesterMixin']
  				if "TestCase" in parent_classes:
  						# Here we **assume** the original string is always with `unittest.TestCase`.
  						_parent_classes.append("unittest.TestCase" )
  				lowerCAmelCase_     :							Dict             =    ', '.join(_parent_classes )
  				for idx, line in enumerate(lowerCAmelCase__ ):
  						# Find the ending of the declaration of `test_class`
  						if line.strip().endswith("):" ):
  								# mark the lines of the declaration of `test_class` to be removed
  								for _idx in range(idx + 1 ):
  										lowerCAmelCase_     :							Dict             =    None  # noqa
  								break
          # Add the new, one-line, class declaration for `test_class`
  				lowerCAmelCase_     :							List[Any]             =    f'''class {test_class.__name__}({parent_classes}):\n'''
  
  		# Add indentation
  		lowerCAmelCase_     :							str             =    ' ' * indent_level + line_to_add
  		# Insert `pipeline_model_mapping` to `class_lines`.
  		# (The line at `target_idx` should be kept by definition!)
  		lowerCAmelCase_     :							Optional[Any]             =    class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
  		# Remove the lines that are marked to be removed
  		lowerCAmelCase_     :							List[Any]             =    [x for x in class_lines if x is not None]
  
  		# Move from test class to module (in order to write to the test file)
  		lowerCAmelCase_     :							str             =    inspect.getsourcelines(inspect.getmodule(lowerCAmelCase__ ) )[0]
  		# Be careful with the 1-off between line numbers and array indices
  		lowerCAmelCase_     :							List[Any]             =    module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
  		lowerCAmelCase_     :							str             =    ''.join(lowerCAmelCase__ )
  
  		lowerCAmelCase_     :							List[str]             =    inspect.getsourcefile(lowerCAmelCase__ )
  		with open(lowerCAmelCase__   , "w"   , encoding="UTF-8"   , newline="\n" ) as fp:
  				fp.write(lowerCAmelCase__ )
  
  		return line_to_add
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase=False )		->					List[str]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Tuple             =    find_test_class(lowerCAmelCase__ )
  		if test_class:
  				add_pipeline_model_mapping(lowerCAmelCase__   , overwrite=lowerCAmelCase__ )
  
  
  if __name__ == "__main__":
  					lowercase__      =				argparse.ArgumentParser()
  					parser.add_argument(
  					    """--test_file""", type=str, help="""A path to the test file, starting with the repository's `tests` directory."""
  					)
  					parser.add_argument(
  					    """--all""",
  					    action="""store_true""",
  					    help="""If to check and modify all test files.""",
  					)
  					parser.add_argument(
  					    """--overwrite""",
  					    action="""store_true""",
  					    help="""If to overwrite a test class if it has already defined `pipeline_model_mapping`.""",
  					)
  					lowercase__      =				parser.parse_args()
  
  					if not args.all and not args.test_file:
  										raise ValueError("""Please specify either `test_file` or pass `--all` to check/modify all test files.""")
  					elif args.all and args.test_file:
  										raise ValueError("""Only one of `--test_file` and `--all` could be specified.""")
  
  					lowercase__      =				[]
  					if args.test_file:
  										lowercase__      =				[args.test_file]
  					else:
  										lowercase__      =				os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
  										for test_file in glob.glob(pattern):
  															# `Flax` is not concerned at this moment
  															if not test_file.startswith("""test_modeling_flax_"""):
  																				test_files.append(test_file)
  
  					for test_file in test_files:
  										if test_file in TEST_FILE_TO_IGNORE:
  															print(F"""[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.""")
  															continue
  										add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)
  
  
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  import math
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  @dataclass
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       torch.FloatTensor
  		a_			:       torch.FloatTensor
  
  
  
  
  
  
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase=0.9_99   , __UpperCamelCase="cosine"   , )		->					Any:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if alpha_transform_type == "cosine":
  
  				def alpha_bar_fn(__UpperCamelCase ):
  						return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
  
  		elif alpha_transform_type == "exp":
  
  				def alpha_bar_fn(__UpperCamelCase ):
  						return math.exp(t * -12.0 )
  
  		else:
  				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
  
  		lowerCAmelCase_     :							List[str]             =    []
  		for i in range(__UpperCamelCase ):
  				lowerCAmelCase_     :							int             =    i / num_diffusion_timesteps
  				lowerCAmelCase_     :							Optional[Any]             =    (i + 1) / num_diffusion_timesteps
  				betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase )   , __UpperCamelCase ) )
  		return torch.tensor(__UpperCamelCase   , dtype=torch.floataa )
  
  
  
  class 	__lowerCamelCase       (				A__				,							A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Tuple      =							1
  
  
  
  
  
  		@register_to_config
  		def __init__(     self							:			Optional[int]		,    a_							:			int = 10_00		,    a_							:			float = 0.0001		,    a_							:			float = 0.02		,    a_							:			str = "linear"		,    a_							:			float = 0.0		,    a_							:			Optional[np.ndarray] = None		,    a_							:			bool = True		,    ):
  				if trained_betas is not None:
  						lowerCAmelCase_     :							Tuple             =    torch.from_numpy(a_			)
  				elif beta_schedule == "linear":
  						lowerCAmelCase_     :							int             =    torch.linspace(a_		,    a_		,    a_		,    dtype=torch.floataa			)
  				elif beta_schedule == "scaled_linear":
  						# this schedule is very specific to the latent diffusion model.
  						lowerCAmelCase_     :							Optional[Any]             =    (
  						    torch.linspace(beta_start**0.5		,    beta_end**0.5		,    a_		,    dtype=torch.floataa			) ** 2
  						)
  				elif beta_schedule == "squaredcos_cap_v2":
  						# Glide cosine schedule
  						lowerCAmelCase_     :							Tuple             =    betas_for_alpha_bar(a_			)
  				elif beta_schedule == "sigmoid":
  						# GeoDiff sigmoid schedule
  						lowerCAmelCase_     :							List[str]             =    torch.linspace(-6		,    6		,    a_			)
  						lowerCAmelCase_     :							int             =    torch.sigmoid(a_			) * (beta_end - beta_start) + beta_start
  				else:
  						raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    1.0 - self.betas
  				lowerCAmelCase_     :							Optional[int]             =    torch.cumprod(self.alphas		,    dim=0			)
  				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)
  
  				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)
  
  				# standard deviation of the initial noise distribution
  				lowerCAmelCase_     :							List[str]             =    1.0
  
  				# setable values
  				lowerCAmelCase_     :							Dict             =    None
  				lowerCAmelCase_     :							List[str]             =    torch.from_numpy(np.arange(0		,    a_			)[::-1].copy()			)
  
  				lowerCAmelCase_     :							int             =    eta
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			Optional[int] = None			):
  				return sample
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			int		,    a_							:			int = 10		,    a_							:			int = 10		,    a_							:			Union[str, torch.device] = None		,    ):
  				lowerCAmelCase_     :							Union[str, Any]             =    min(self.config.num_train_timesteps		,    a_			)
  				lowerCAmelCase_     :							Dict             =    num_inference_steps
  
  				lowerCAmelCase_     :							List[str]             =    []
  
  				lowerCAmelCase_     :							Dict             =    {}
  				for j in range(0		,    num_inference_steps - jump_length		,    a_			):
  						lowerCAmelCase_     :							Dict             =    jump_n_sample - 1
  
  				lowerCAmelCase_     :							List[Any]             =    num_inference_steps
  				while t >= 1:
  						lowerCAmelCase_     :							int             =    t - 1
  						timesteps.append(a_			)
  
  						if jumps.get(a_		,    0			) > 0:
  								lowerCAmelCase_     :							Optional[int]             =    jumps[t] - 1
  								for _ in range(a_			):
  										lowerCAmelCase_     :							Any             =    t + 1
  										timesteps.append(a_			)
  
  				lowerCAmelCase_     :							List[Any]             =    np.array(a_			) * (self.config.num_train_timesteps // self.num_inference_steps)
  				lowerCAmelCase_     :							int             =    torch.from_numpy(a_			).to(a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			Optional[Any]			):
  				lowerCAmelCase_     :							Optional[Any]             =    t - self.config.num_train_timesteps // self.num_inference_steps
  
  				lowerCAmelCase_     :							List[Any]             =    self.alphas_cumprod[t]
  				lowerCAmelCase_     :							Dict             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  				lowerCAmelCase_     :							List[str]             =    1 - alpha_prod_t
  				lowerCAmelCase_     :							str             =    1 - alpha_prod_t_prev
  
  				# For t > 0, compute predicted variance βt (see formula (6) and (7) from
  				# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
  				# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
  				# variance to pred_sample
  				# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
  				# without eta.
  				# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
  				lowerCAmelCase_     :							Dict             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  				return variance
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			int		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			Optional[torch.Generator] = None		,    a_							:			bool = True		,    ):
  				lowerCAmelCase_     :							Any             =    timestep
  				lowerCAmelCase_     :							str             =    timestep - self.config.num_train_timesteps // self.num_inference_steps
  
  				# 1. compute alphas, betas
  				lowerCAmelCase_     :							Any             =    self.alphas_cumprod[t]
  				lowerCAmelCase_     :							List[str]             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  				lowerCAmelCase_     :							Optional[Any]             =    1 - alpha_prod_t
  
  				# 2. compute predicted original sample from predicted noise also called
  				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  				lowerCAmelCase_     :							List[Any]             =    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
  
  				# 3. Clip "predicted x_0"
  				if self.config.clip_sample:
  						lowerCAmelCase_     :							Optional[int]             =    torch.clamp(a_		,    -1		,    1			)
  
  				# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
  				# substitute formula (7) in the algorithm coming from DDPM paper
  				# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
  				# DDIM schedule gives the same results as DDPM with eta = 1.0
  				# Noise is being reused in 7. and 8., but no impact on quality has
  				# been observed.
  
  				# 5. Add noise
  				lowerCAmelCase_     :							Optional[Any]             =    model_output.device
  				lowerCAmelCase_     :							List[str]             =    randn_tensor(model_output.shape		,    generator=a_		,    device=a_		,    dtype=model_output.dtype			)
  				lowerCAmelCase_     :							List[Any]             =    self.eta * self._get_variance(a_			) ** 0.5
  
  				lowerCAmelCase_     :							Optional[int]             =    0
  				if t > 0 and self.eta > 0:
  						lowerCAmelCase_     :							Optional[int]             =    std_dev_t * noise
  
  				# 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-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  				lowerCAmelCase_     :							Dict             =    alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
  
  				# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
  				lowerCAmelCase_     :							Union[str, Any]             =    (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
  
  				# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
  				lowerCAmelCase_     :							Tuple             =    mask * prev_known_part + (1.0 - mask) * prev_unknown_part
  
  				if not return_dict:
  						return (
  						    pred_prev_sample,
  						    pred_original_sample,
  						)
  
  				return RePaintSchedulerOutput(prev_sample=a_		,    pred_original_sample=a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			Any		,    a_							:			Dict		,    a_							:			Optional[int]=None			):
  				lowerCAmelCase_     :							Dict             =    self.config.num_train_timesteps // self.num_inference_steps
  
  				for i in range(a_			):
  						lowerCAmelCase_     :							Optional[Any]             =    self.betas[timestep + i]
  						if sample.device.type == "mps":
  								# randn does not work reproducibly on mps
  								lowerCAmelCase_     :							str             =    randn_tensor(sample.shape		,    dtype=sample.dtype		,    generator=a_			)
  								lowerCAmelCase_     :							List[str]             =    noise.to(sample.device			)
  						else:
  								lowerCAmelCase_     :							Tuple             =    randn_tensor(sample.shape		,    generator=a_		,    device=sample.device		,    dtype=sample.dtype			)
  
  						# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
  						lowerCAmelCase_     :							int             =    (1 - beta) ** 0.5 * sample + beta**0.5 * noise
  
  				return sample
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.IntTensor		,    ):
  				raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."			)
  
  
  
  
  
  		def __len__(     self							:			Union[str, Any]			):
  				return self.config.num_train_timesteps
  