





'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


a__							:							Optional[Any] =(
    "https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
)


a__							:							Tuple =logging.get_logger(__name__)  # pylint: disable=invalid-name
def 				lowercase__   (   ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  '''https://pypi.org/pypi/diffusers/json'''
       __UpperCamelCase       =  json.loads(request.urlopen(__SCREAMING_SNAKE_CASE     ).read()     )['''releases'''].keys()
       return sorted(__SCREAMING_SNAKE_CASE				,  key=lambda __lowercase     : version.Version(__SCREAMING_SNAKE_CASE     )     )
def 				lowercase__   (   ) ->      Any:



       """simple docstring"""






       if HF_MODULES_CACHE in sys.path:
              return

       sys.path.append(__SCREAMING_SNAKE_CASE     )
       os.makedirs(__SCREAMING_SNAKE_CASE				,  exist_ok=__SCREAMING_SNAKE_CASE     )
       __UpperCamelCase       =  Path(__SCREAMING_SNAKE_CASE     ) / '''__init__.py'''
       if not init_path.exists():
              init_path.touch()
def 				lowercase__   (   __lowercase						:  Union[str, os.PathLike]     ) ->      int:



       """simple docstring"""






       init_hf_modules()
       __UpperCamelCase       =  Path(__SCREAMING_SNAKE_CASE     ) / name
       # If the parent module does not exist yet, recursively create it.
       if not dynamic_module_path.parent.exists():
              create_dynamic_module(dynamic_module_path.parent     )
       os.makedirs(__SCREAMING_SNAKE_CASE				,  exist_ok=__SCREAMING_SNAKE_CASE     )
       __UpperCamelCase       =  dynamic_module_path / '''__init__.py'''
       if not init_path.exists():
              init_path.touch()
def 				lowercase__   (   __lowercase						:  List[str]     ) ->      Dict:



       """simple docstring"""






       with open(__SCREAMING_SNAKE_CASE				,  'r'				,  encoding='utf-8'     ) as f:
              __UpperCamelCase       =  f.read()

       # Imports of the form `import .xxx`
       __UpperCamelCase       =  re.findall('^\s*import\s+\.(\S+)\s*$'				,  __SCREAMING_SNAKE_CASE				,  flags=re.MULTILINE     )
       # Imports of the form `from .xxx import yyy`
       relative_imports += re.findall('^\s*from\s+\.(\S+)\s+import'				,  __SCREAMING_SNAKE_CASE				,  flags=re.MULTILINE     )
       # Unique-ify
       return list(set(__SCREAMING_SNAKE_CASE     )     )
def 				lowercase__   (   __lowercase						:  str     ) ->      str:



       """simple docstring"""






       __UpperCamelCase       =  False
       __UpperCamelCase       =  [module_file]
       __UpperCamelCase       =  []

       # Let's recurse through all relative imports
       while not no_change:
              __UpperCamelCase       =  []
              for f in files_to_check:
                     new_imports.extend(get_relative_imports(__SCREAMING_SNAKE_CASE     )     )

              __UpperCamelCase       =  Path(__SCREAMING_SNAKE_CASE     ).parent
              __UpperCamelCase       =  [str(module_path / m     ) for m in new_imports]
              __UpperCamelCase       =  [f for f in new_import_files if f not in all_relative_imports]
              __UpperCamelCase       =  [F'''{f}.py''' for f in new_import_files]

              __UpperCamelCase       =  len(__SCREAMING_SNAKE_CASE     ) == 0
              all_relative_imports.extend(__SCREAMING_SNAKE_CASE     )

       return all_relative_imports
def 				lowercase__   (   __lowercase						:  Any     ) ->      int:



       """simple docstring"""






       with open(__SCREAMING_SNAKE_CASE				,  'r'				,  encoding='utf-8'     ) as f:
              __UpperCamelCase       =  f.read()

       # Imports of the form `import xxx`
       __UpperCamelCase       =  re.findall('^\s*import\s+(\S+)\s*$'				,  __SCREAMING_SNAKE_CASE				,  flags=re.MULTILINE     )
       # Imports of the form `from xxx import yyy`
       imports += re.findall('^\s*from\s+(\S+)\s+import'				,  __SCREAMING_SNAKE_CASE				,  flags=re.MULTILINE     )
       # Only keep the top-level module
       __UpperCamelCase       =  [imp.split('.'     )[0] for imp in imports if not imp.startswith('.'     )]

       # Unique-ify and test we got them all
       __UpperCamelCase       =  list(set(__SCREAMING_SNAKE_CASE     )     )
       __UpperCamelCase       =  []
       for imp in imports:
              try:
                     importlib.import_module(__SCREAMING_SNAKE_CASE     )
              except ImportError:
                     missing_packages.append(__SCREAMING_SNAKE_CASE     )

       if len(__SCREAMING_SNAKE_CASE     ) > 0:
              raise ImportError(
                  'This modeling file requires the following packages that were not found in your environment: '
                  F'''{', '.join(__SCREAMING_SNAKE_CASE     )}. Run `pip install {' '.join(__SCREAMING_SNAKE_CASE     )}`'''     )

       return get_relative_imports(__SCREAMING_SNAKE_CASE     )
def 				lowercase__   (   __lowercase						:  List[Any]				,  __lowercase						:  Optional[Any]     ) ->      str:



       """simple docstring"""






       __UpperCamelCase       =  module_path.replace(os.path.sep				,  '.'     )
       __UpperCamelCase       =  importlib.import_module(__SCREAMING_SNAKE_CASE     )

       if class_name is None:
              return find_pipeline_class(__SCREAMING_SNAKE_CASE     )
       return getattr(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      Dict:



       """simple docstring"""






       from ..pipelines import DiffusionPipeline

       __UpperCamelCase       =  dict(inspect.getmembers(__SCREAMING_SNAKE_CASE				,  inspect.isclass     )     )

       __UpperCamelCase       =  None
       for cls_name, cls in cls_members.items():
              if (
                  cls_name != DiffusionPipeline.__name__
                  and issubclass(cls				,  __SCREAMING_SNAKE_CASE     )
                  and cls.__module__.split('.'     )[0] != "diffusers"
              ):
                     if pipeline_class is not None:
                            raise ValueError(
                                F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
                                F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
                                F''' {loaded_module}.'''     )
                     __UpperCamelCase       =  cls

       return pipeline_class
def 				lowercase__   (   __lowercase						:  Union[str, os.PathLike]				,  __lowercase						:  str				,  __lowercase						:  Optional[Union[str, os.PathLike]] = None				,  __lowercase						:  bool = False				,  __lowercase						:  bool = False				,  __lowercase						:  Optional[Dict[str, str]] = None				,  __lowercase						:  Optional[Union[bool, str]] = None				,  __lowercase						:  Optional[str] = None				,  __lowercase						:  bool = False				,  ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  str(__SCREAMING_SNAKE_CASE     )

       __UpperCamelCase       =  os.path.join(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE     )

       if os.path.isfile(__SCREAMING_SNAKE_CASE     ):
              __UpperCamelCase       =  module_file_or_url
              __UpperCamelCase       =  '''local'''
       elif pretrained_model_name_or_path.count('/'     ) == 0:
              __UpperCamelCase       =  get_diffusers_versions()
              # cut ".dev0"
              __UpperCamelCase       =  '''v''' + '''.'''.join(__version__.split('.'     )[:3]     )

              # retrieve github version that matches
              if revision is None:
                     __UpperCamelCase       =  latest_version if latest_version[1:] in available_versions else '''main'''
                     logger.info(F'''Defaulting to latest_version: {revision}.'''     )
              elif revision in available_versions:
                     __UpperCamelCase       =  F'''v{revision}'''
              elif revision == "main":
                     __UpperCamelCase       =  revision
              else:
                     raise ValueError(
                         F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
                         F''' {', '.join(available_versions + ['main']     )}.'''     )

              # community pipeline on GitHub
              __UpperCamelCase       =  COMMUNITY_PIPELINES_URL.format(revision=__SCREAMING_SNAKE_CASE				,  pipeline=__SCREAMING_SNAKE_CASE     )
              try:
                     __UpperCamelCase       =  cached_download(
                         __SCREAMING_SNAKE_CASE				,  cache_dir=__SCREAMING_SNAKE_CASE				,  force_download=__SCREAMING_SNAKE_CASE				,  proxies=__SCREAMING_SNAKE_CASE				,  resume_download=__SCREAMING_SNAKE_CASE				,  local_files_only=__SCREAMING_SNAKE_CASE				,  use_auth_token=__SCREAMING_SNAKE_CASE				,  )
                     __UpperCamelCase       =  '''git'''
                     __UpperCamelCase       =  pretrained_model_name_or_path + '''.py'''
              except EnvironmentError:
                     logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''     )
                     raise
       else:
              try:
                     # Load from URL or cache if already cached
                     __UpperCamelCase       =  hf_hub_download(
                         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  cache_dir=__SCREAMING_SNAKE_CASE				,  force_download=__SCREAMING_SNAKE_CASE				,  proxies=__SCREAMING_SNAKE_CASE				,  resume_download=__SCREAMING_SNAKE_CASE				,  local_files_only=__SCREAMING_SNAKE_CASE				,  use_auth_token=__SCREAMING_SNAKE_CASE				,  )
                     __UpperCamelCase       =  os.path.join('local'				,  '--'.join(pretrained_model_name_or_path.split('/'     )     )     )
              except EnvironmentError:
                     logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''     )
                     raise

    # Check we have all the requirements in our environment
       __UpperCamelCase       =  check_imports(__SCREAMING_SNAKE_CASE     )

       # Now we move the module inside our cached dynamic modules.
       __UpperCamelCase       =  DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
       create_dynamic_module(__SCREAMING_SNAKE_CASE     )
       __UpperCamelCase       =  Path(__SCREAMING_SNAKE_CASE     ) / full_submodule
       if submodule == "local" or submodule == "git":
              # We always copy local files (we could hash the file to see if there was a change, and give them the name of
              # that hash, to only copy when there is a modification but it seems overkill for now).
              # The only reason we do the copy is to avoid putting too many folders in sys.path.
              shutil.copy(__SCREAMING_SNAKE_CASE				,  submodule_path / module_file     )
              for module_needed in modules_needed:
                     __UpperCamelCase       =  F'''{module_needed}.py'''
                     shutil.copy(os.path.join(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE     )				,  submodule_path / module_needed     )
       else:
              # Get the commit hash
              # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
              if isinstance(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE     ):
                     __UpperCamelCase       =  use_auth_token
              elif use_auth_token is True:
                     __UpperCamelCase       =  HfFolder.get_token()
              else:
                     __UpperCamelCase       =  None

              __UpperCamelCase       =  model_info(__SCREAMING_SNAKE_CASE				,  revision=__SCREAMING_SNAKE_CASE				,  token=__SCREAMING_SNAKE_CASE     ).sha

              # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
              # benefit of versioning.
              __UpperCamelCase       =  submodule_path / commit_hash
              __UpperCamelCase       =  full_submodule + os.path.sep + commit_hash
              create_dynamic_module(__SCREAMING_SNAKE_CASE     )

              if not (submodule_path / module_file).exists():
                     shutil.copy(__SCREAMING_SNAKE_CASE				,  submodule_path / module_file     )
              # Make sure we also have every file with relative
              for module_needed in modules_needed:
                     if not (submodule_path / module_needed).exists():
                            get_cached_module_file(
                                __SCREAMING_SNAKE_CASE				,  F'''{module_needed}.py'''				,  cache_dir=__SCREAMING_SNAKE_CASE				,  force_download=__SCREAMING_SNAKE_CASE				,  resume_download=__SCREAMING_SNAKE_CASE				,  proxies=__SCREAMING_SNAKE_CASE				,  use_auth_token=__SCREAMING_SNAKE_CASE				,  revision=__SCREAMING_SNAKE_CASE				,  local_files_only=__SCREAMING_SNAKE_CASE				,  )
       return os.path.join(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE     )


def 				lowercase__   (   __lowercase						:  Union[str, os.PathLike]				,  __lowercase						:  str				,  __lowercase						:  Optional[str] = None				,  __lowercase						:  Optional[Union[str, os.PathLike]] = None				,  __lowercase						:  bool = False				,  __lowercase						:  bool = False				,  __lowercase						:  Optional[Dict[str, str]] = None				,  __lowercase						:  Optional[Union[bool, str]] = None				,  __lowercase						:  Optional[str] = None				,  __lowercase						:  bool = False				,  **__lowercase						:  Optional[Any]				,  ) ->      List[Any]:



       """simple docstring"""






       __UpperCamelCase       =  get_cached_module_file(
           __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  cache_dir=__SCREAMING_SNAKE_CASE				,  force_download=__SCREAMING_SNAKE_CASE				,  resume_download=__SCREAMING_SNAKE_CASE				,  proxies=__SCREAMING_SNAKE_CASE				,  use_auth_token=__SCREAMING_SNAKE_CASE				,  revision=__SCREAMING_SNAKE_CASE				,  local_files_only=__SCREAMING_SNAKE_CASE				,  )
       return get_class_in_module(__SCREAMING_SNAKE_CASE				,  final_module.replace('.py'				,  ''     )     )


'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class  lowerCAmelCase__   :
					lowerCAmelCase_        =     42

					# setable values
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     None






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return cls(common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE		)







@dataclass
class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     42







class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_     ):
					lowerCAmelCase_        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

					lowerCAmelCase_        =     42






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return True






					@register_to_config
					def __init__(   self				,							__SCREAMING_SNAKE_CASE = 10_00				,							__SCREAMING_SNAKE_CASE = 0.0_001				,							__SCREAMING_SNAKE_CASE = 0.02				,							__SCREAMING_SNAKE_CASE = "linear"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "fixed_small"				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = "epsilon"				,							__SCREAMING_SNAKE_CASE = jnp.floataa				,							):


											"""simple docstring"""





											lowercase_	:     Dict													=       dtype






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if common is None:
																	lowercase_	:     Tuple													=       CommonSchedulerState.create(self		)

											# standard deviation of the initial noise distribution
											lowercase_	:     Union[str, Any]													=       jnp.array(1.0				,							dtype=self.dtype		)

											lowercase_	:     List[Any]													=       jnp.arange(0				,							self.config.num_train_timesteps		).round()[::-1]

											return DDPMSchedulerState.create(
											    common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE				,							)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											return sample






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = ()		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       self.config.num_train_timesteps // num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# rounding to avoid issues when num_inference_step is power of 3
											lowercase_	:     int													=       (jnp.arange(0				,							__SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1]

											return state.replace(
											    num_inference_steps=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE				,							)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       state.common.alphas_cumprod[t]
											lowercase_	:     str													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)

											# 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
											lowercase_	:     int													=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

											if variance_type is None:
																	lowercase_	:     str													=       self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small":
																	lowercase_	:     int													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)
											# for rl-diffuser https://arxiv.org/abs/2205.09991
											elif variance_type == "fixed_small_log":
																	lowercase_	:     List[str]													=       jnp.log(jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)		)
											elif variance_type == "fixed_large":
																	lowercase_	:     List[Any]													=       state.common.betas[t]
											elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowercase_	:     List[Any]													=       jnp.log(state.common.betas[t]		)
											elif variance_type == "learned":
																	return predicted_variance
											elif variance_type == "learned_range":
																	lowercase_	:     Optional[Any]													=       variance
																	lowercase_	:     Union[str, Any]													=       state.common.betas[t]
																	lowercase_	:     Union[str, Any]													=       (predicted_variance + 1) / 2
																	lowercase_	:     Any													=       frac * max_log + (1 - frac) * min_log

											return variance






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = True				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       timestep

											if key is None:
																	lowercase_	:     int													=       jax.random.PRNGKey(0		)

											if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																	lowercase_						, lowercase_	:     Optional[Any]													=       jnp.split(__SCREAMING_SNAKE_CASE				,							sample.shape[1]				,							axis=1		)
											else:
																	lowercase_	:     int													=       None

											# 1. compute alphas, betas
											lowercase_	:     Any													=       state.common.alphas_cumprod[t]
											lowercase_	:     Optional[int]													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)
											lowercase_	:     int													=       1 - alpha_prod_t
											lowercase_	:     str													=       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 self.config.prediction_type == "epsilon":
																	lowercase_	:     Tuple													=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																	lowercase_	:     Any													=       model_output
											elif self.config.prediction_type == "v_prediction":
																	lowercase_	:     List[Any]													=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											else:
																	raise ValueError(
																	    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
																	    ''' for the FlaxDDPMScheduler.'''		)

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																	lowercase_	:     Optional[Any]													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							-1				,							1		)

											# 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
											lowercase_	:     List[Any]													=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
											lowercase_	:     Optional[Any]													=       state.common.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
											lowercase_	:     Optional[Any]													=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											def random_variance():
																	lowercase_	:     str													=       jax.random.split(__SCREAMING_SNAKE_CASE				,							num=1		)
																	lowercase_	:     List[Any]													=       jax.random.normal(__SCREAMING_SNAKE_CASE				,							shape=model_output.shape				,							dtype=self.dtype		)
																	return (self._get_variance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							predicted_variance=__SCREAMING_SNAKE_CASE		) ** 0.5) * noise

											lowercase_	:     Optional[Any]													=       jnp.where(t > 0				,							random_variance()				,							jnp.zeros(model_output.shape				,							dtype=self.dtype		)		)

											lowercase_	:     Any													=       pred_prev_sample + variance

											if not return_dict:
																	return (pred_prev_sample, state)

											return FlaxDDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE				,							state=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return add_noise_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return get_velocity_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def __len__(   self		):


											"""simple docstring"""





											return self.config.num_train_timesteps



