






"""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)










"""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
