

import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


SCREAMING_SNAKE_CASE_        =      get_logger(__name__)


SCREAMING_SNAKE_CASE_        =      Path(__file__).parent / '''model_card_template.md'''
SCREAMING_SNAKE_CASE_        =      uuida().hex
SCREAMING_SNAKE_CASE_        =      os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
SCREAMING_SNAKE_CASE_        =      os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
SCREAMING_SNAKE_CASE_        =      HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[Dict, str, None] = None )   ->       Any:
				_UpperCAmelCase       :		Tuple 		=    F'diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'
				if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
								return ua + "; telemetry/off"
				if is_torch_available():
								ua += F'; torch/{_torch_version}'
				if is_flax_available():
								ua += F'; jax/{_jax_version}'
								ua += F'; flax/{_flax_version}'
				if is_onnx_available():
								ua += F'; onnxruntime/{_onnxruntime_version}'
				# CI will set this value to True
				if os.environ.get("DIFFUSERS_IS_CI"  ,      "" ).upper() in ENV_VARS_TRUE_VALUES:
								ua += "; is_ci/true"
				if isinstance(lowerCAmelCase  ,      lowerCAmelCase ):
								ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items() )
				elif isinstance(lowerCAmelCase  ,      lowerCAmelCase ):
								ua += "; " + user_agent
				return ua

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Optional[str] = None  ,      lowerCAmelCase:  Optional[str] = None )   ->       Optional[int]:




				if token is None:
								_UpperCAmelCase       :		Dict 		=    HfFolder.get_token()
				if organization is None:
								_UpperCAmelCase       :		List[str] 		=    whoami(lowerCAmelCase )["name"]
								return F'{username}/{model_id}'
				else:
								return F'{organization}/{model_id}'

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any]  ,      lowerCAmelCase:  int )   ->       List[Any]:




				if not is_jinja_available():
								raise ValueError(
								    "Modelcard rendering is based on Jinja templates."
								    " Please make sure to have `jinja` installed before using `create_model_card`."
								    " To install it, please run `pip install Jinja2`." )

				if hasattr(lowerCAmelCase  ,      "local_rank" ) and args.local_rank not in [-1, 0]:
								return

				_UpperCAmelCase       :		Optional[Any] 		=    args.hub_token if hasattr(lowerCAmelCase  ,      "hub_token" ) else None
				_UpperCAmelCase       :		Union[str, Any] 		=    get_full_repo_name(lowerCAmelCase  ,      token=lowerCAmelCase )

				_UpperCAmelCase       :		Tuple 		=    ModelCard.from_template(
				    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
				        language="en"  ,      license="apache-2.0"  ,      library_name="diffusers"  ,      tags=[]  ,      datasets=args.dataset_name  ,      metrics=[]  ,      )  ,      template_path=lowerCAmelCase  ,      model_name=lowerCAmelCase  ,      repo_name=lowerCAmelCase  ,      dataset_name=args.dataset_name if hasattr(lowerCAmelCase  ,      "dataset_name" ) else None  ,      learning_rate=args.learning_rate  ,      train_batch_size=args.train_batch_size  ,      eval_batch_size=args.eval_batch_size  ,      gradient_accumulation_steps=(
				        args.gradient_accumulation_steps if hasattr(lowerCAmelCase  ,      "gradient_accumulation_steps" ) else None
				    )  ,      adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase  ,      "adam_beta1" ) else None  ,      adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase  ,      "adam_beta2" ) else None  ,      adam_weight_decay=args.adam_weight_decay if hasattr(lowerCAmelCase  ,      "adam_weight_decay" ) else None  ,      adam_epsilon=args.adam_epsilon if hasattr(lowerCAmelCase  ,      "adam_epsilon" ) else None  ,      lr_scheduler=args.lr_scheduler if hasattr(lowerCAmelCase  ,      "lr_scheduler" ) else None  ,      lr_warmup_steps=args.lr_warmup_steps if hasattr(lowerCAmelCase  ,      "lr_warmup_steps" ) else None  ,      ema_inv_gamma=args.ema_inv_gamma if hasattr(lowerCAmelCase  ,      "ema_inv_gamma" ) else None  ,      ema_power=args.ema_power if hasattr(lowerCAmelCase  ,      "ema_power" ) else None  ,      ema_max_decay=args.ema_max_decay if hasattr(lowerCAmelCase  ,      "ema_max_decay" ) else None  ,      mixed_precision=args.mixed_precision  ,      )

				_UpperCAmelCase       :		Optional[Any] 		=    os.path.join(args.output_dir  ,      "README.md" )
				model_card.save(lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[str]  ,      lowerCAmelCase:  Optional[str] = None )   ->       List[str]:




				if resolved_file is None or commit_hash is not None:
								return commit_hash
				_UpperCAmelCase       :		Union[str, Any] 		=    str(Path(lowerCAmelCase ).as_posix() )
				_UpperCAmelCase       :		Dict 		=    re.search(R"snapshots/([^/]+)/"  ,      lowerCAmelCase )
				if search is None:
								return None
				_UpperCAmelCase       :		Tuple 		=    search.groups()[0]
				return commit_hash if REGEX_COMMIT_HASH.match(lowerCAmelCase ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
SCREAMING_SNAKE_CASE_        =      os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
SCREAMING_SNAKE_CASE_        =      os.path.join(hf_cache_home, 'diffusers')

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[str] = None  ,      lowerCAmelCase:  Optional[str] = None )   ->       List[str]:




				if new_cache_dir is None:
								_UpperCAmelCase       :		Optional[int] 		=    DIFFUSERS_CACHE
				if old_cache_dir is None:
								_UpperCAmelCase       :		Union[str, Any] 		=    old_diffusers_cache

				_UpperCAmelCase       :		Optional[int] 		=    Path(lowerCAmelCase ).expanduser()
				_UpperCAmelCase       :		Any 		=    Path(lowerCAmelCase ).expanduser()
				for old_blob_path in old_cache_dir.glob("**/blobs/*" ):
								if old_blob_path.is_file() and not old_blob_path.is_symlink():
												_UpperCAmelCase       :		Optional[int] 		=    new_cache_dir / old_blob_path.relative_to(lowerCAmelCase )
												new_blob_path.parent.mkdir(parents=lowerCAmelCase  ,      exist_ok=lowerCAmelCase )
												os.replace(lowerCAmelCase  ,      lowerCAmelCase )
												try:
																os.symlink(lowerCAmelCase  ,      lowerCAmelCase )
												except OSError:
																logger.warning(
																    "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded." )
				# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


SCREAMING_SNAKE_CASE_        =      os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
					SCREAMING_SNAKE_CASE_        =      0
else:
					with open(cache_version_file) as f:
										try:
															SCREAMING_SNAKE_CASE_        =      int(f.read())
										except ValueError:
															SCREAMING_SNAKE_CASE_        =      0

if cache_version < 1:
					SCREAMING_SNAKE_CASE_        =      os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
					if old_cache_is_not_empty:
										logger.warning(
										    'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
										    'existing cached models. This is a one-time operation, you can interrupt it or run it '
										    'later by calling `diffusers.utils.hub_utils.move_cache()`.'
										)
										try:
															move_cache()
										except Exception as e:
															SCREAMING_SNAKE_CASE_        =      '''\n'''.join(traceback.format_tb(e.__traceback__))
															logger.error(
															    F'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
															    'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
															    'message and we will do our best to help.'
															)

if cache_version < 1:
					try:
										os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
										with open(cache_version_file, 'w') as f:
															f.write('1')
					except Exception:
										logger.warning(
										    F'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
										    'the directory exists and can be written to.'
										)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Optional[str] = None )   ->       Optional[Any]:




				if variant is not None:
								_UpperCAmelCase       :		Optional[Any] 		=    weights_name.split("." )
								_UpperCAmelCase       :		Optional[Any] 		=    splits[:-1] + [variant] + splits[-1:]
								_UpperCAmelCase       :		str 		=    ".".join(lowerCAmelCase )

				return weights_name





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      *,
    lowerCAmelCase:  Dict  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Tuple=None  ,      )   ->       Optional[int]:
				_UpperCAmelCase       :		Any 		=    str(lowerCAmelCase )
				if os.path.isfile(lowerCAmelCase ):
								return pretrained_model_name_or_path
				elif os.path.isdir(lowerCAmelCase ):
								if os.path.isfile(os.path.join(lowerCAmelCase  ,      lowerCAmelCase ) ):
												# Load from a PyTorch checkpoint
												_UpperCAmelCase       :		Optional[Any] 		=    os.path.join(lowerCAmelCase  ,      lowerCAmelCase )
												return model_file
								elif subfolder is not None and os.path.isfile(
								    os.path.join(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase ) ):
												_UpperCAmelCase       :		Optional[int] 		=    os.path.join(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
												return model_file
								else:
												raise EnvironmentError(
												    F'Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.' )
				else:
								# 1. First check if deprecated way of loading from branches is used
								if (
								    revision in DEPRECATED_REVISION_ARGS
								    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
								    and version.parse(version.parse(lowerCAmelCase ).base_version ) >= version.parse("0.20.0" )
								):
												try:
																_UpperCAmelCase       :		Union[str, Any] 		=    hf_hub_download(
																    lowerCAmelCase  ,      filename=_add_variant(lowerCAmelCase  ,      lowerCAmelCase )  ,      cache_dir=lowerCAmelCase  ,      force_download=lowerCAmelCase  ,      proxies=lowerCAmelCase  ,      resume_download=lowerCAmelCase  ,      local_files_only=lowerCAmelCase  ,      use_auth_token=lowerCAmelCase  ,      user_agent=lowerCAmelCase  ,      subfolder=lowerCAmelCase  ,      revision=revision or commit_hash  ,      )
																warnings.warn(
																    F'Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'  ,      lowerCAmelCase  ,      )
																return model_file
												except:  # noqa: E722
																warnings.warn(
																    F'You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(lowerCAmelCase  ,      lowerCAmelCase )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(lowerCAmelCase  ,      lowerCAmelCase )}\' so that the correct variant file can be added.'  ,      lowerCAmelCase  ,      )
								try:
												# 2. Load model file as usual
												_UpperCAmelCase       :		Dict 		=    hf_hub_download(
												    lowerCAmelCase  ,      filename=lowerCAmelCase  ,      cache_dir=lowerCAmelCase  ,      force_download=lowerCAmelCase  ,      proxies=lowerCAmelCase  ,      resume_download=lowerCAmelCase  ,      local_files_only=lowerCAmelCase  ,      use_auth_token=lowerCAmelCase  ,      user_agent=lowerCAmelCase  ,      subfolder=lowerCAmelCase  ,      revision=revision or commit_hash  ,      )
												return model_file

								except RepositoryNotFoundError:
												raise EnvironmentError(
												    F'{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '
												    "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
												    "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
												    "login`." )
								except RevisionNotFoundError:
												raise EnvironmentError(
												    F'{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '
												    "this model name. Check the model page at "
												    F'\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.' )
								except EntryNotFoundError:
												raise EnvironmentError(
												    F'{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.' )
								except HTTPError as err:
												raise EnvironmentError(
												    F'There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}' )
								except ValueError:
												raise EnvironmentError(
												    F'We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'
												    F' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'
												    F' directory containing a file named {weights_name} or'
												    " \nCheckout your internet connection or see how to run the library in"
												    " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." )
								except EnvironmentError:
												raise EnvironmentError(
												    F'Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '
												    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
												    F'Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '
												    F'containing a file named {weights_name}' )





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_        =      'scheduler_config.json'

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			1
						_lowercase    						=			2
						_lowercase    						=			3
						_lowercase    						=			4
						_lowercase    						=			5







@dataclass
class 				a  (	UpperCAmelCase				):
						_lowercase    						=			42







class 				a  :
						_lowercase    						=			SCHEDULER_CONFIG_NAME
						_lowercase    						=			["dtype"]
						_lowercase    						=			[]
						_lowercase    						=			True


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_ = None						,					A_ = None						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    cls.load_config(
										    pretrained_model_name_or_path=A_						,					subfolder=A_						,					return_unused_kwargs=A_						,					**A_						,					)
										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    cls.from_config(A_						,					return_unused_kwargs=A_						,					**A_	)

										if hasattr(A_						,					"create_state"	) and getattr(A_						,					"has_state"						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    scheduler.create_state()

										if return_unused_kwargs:
														return scheduler, state, unused_kwargs

										return scheduler, state


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False						,					**A_	):







										'''simple docstring'''


										self.save_config(save_directory=A_						,					push_to_hub=A_						,					**A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._get_compatibles()





						@classmethod
						def        _UpperCAmelCase				(       cls	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    list(set([cls.__name__] + cls._compatibles	)	)
										_UpperCAmelCase       :		Optional[Any] 		=    importlib.import_module(__name__.split("."	)[0]	)
										_UpperCAmelCase       :		Dict 		=    [
										    getattr(A_						,					A_	) for c in compatible_classes_str if hasattr(A_						,					A_	)
										]
										return compatible_classes







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  Tuple[int] )   ->       jnp.ndarray:
				assert len(lowerCAmelCase ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase ) - x.ndim) )  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple=0.999  ,      lowerCAmelCase:  int=jnp.floataa )   ->       jnp.ndarray:

				def alpha_bar(lowerCAmelCase:  Union[str, Any] ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

				_UpperCAmelCase       :		str 		=    []
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Optional[int] 		=    i / num_diffusion_timesteps
								_UpperCAmelCase       :		str 		=    (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowerCAmelCase ) / alpha_bar(lowerCAmelCase )  ,      lowerCAmelCase ) )
				return jnp.array(lowerCAmelCase  ,      dtype=lowerCAmelCase )

@flax.struct.dataclass
class 				a  :
						_lowercase    						=			42
						_lowercase    						=			42
						_lowercase    						=			42


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    scheduler.config

										if config.trained_betas is not None:
														_UpperCAmelCase       :		List[Any] 		=    jnp.asarray(config.trained_betas						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "linear":
														_UpperCAmelCase       :		List[Any] 		=    jnp.linspace(config.beta_start						,					config.beta_end						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_UpperCAmelCase       :		List[str] 		=    (
														    jnp.linspace(
														        config.beta_start**0.5						,					config.beta_end**0.5						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
														    ** 2
														)
										elif config.beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_UpperCAmelCase       :		str 		=    betas_for_alpha_bar(config.num_train_timesteps						,					dtype=scheduler.dtype	)
										else:
														raise NotImplementedError(
														    f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'	)

										_UpperCAmelCase       :		Optional[int] 		=    1.0 - betas

										_UpperCAmelCase       :		int 		=    jnp.cumprod(A_						,					axis=0	)

										return cls(
										    alphas=A_						,					betas=A_						,					alphas_cumprod=A_						,					)







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Optional[int] 		=    state.alphas_cumprod

				_UpperCAmelCase       :		Optional[Any] 		=    alphas_cumprod[timesteps] ** 0.5
				_UpperCAmelCase       :		str 		=    sqrt_alpha_prod.flatten()
				_UpperCAmelCase       :		List[Any] 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				_UpperCAmelCase       :		Optional[int] 		=    (1 - alphas_cumprod[timesteps]) ** 0.5
				_UpperCAmelCase       :		List[Any] 		=    sqrt_one_minus_alpha_prod.flatten()
				_UpperCAmelCase       :		int 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       List[Any]:
				_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Dict:
				_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity


