






"""simple docstring"""



import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
A__	:    Dict			 = 'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
A__	:    Dict			 = direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
A__	:    List[Any]			 = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
A__	:    Optional[int]			 = re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
A__	:    Dict			 = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
A__	:    List[Any]			 = [
    ('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
    ('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
    ('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
    ('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
    ('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
    ('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
    ('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
    ('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
    ('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
    (
        'zero-shot-object-detection',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
        'AutoModelForZeroShotObjectDetection',
    ),
    ('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
    ('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
    ('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
    ('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
    (
        'table-question-answering',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForTableQuestionAnswering',
    ),
    ('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
    ('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
    (
        'next-sentence-prediction',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
        'AutoModelForNextSentencePrediction',
    ),
    (
        'audio-frame-classification',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForAudioFrameClassification',
    ),
    ('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
    (
        'document-question-answering',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForDocumentQuestionAnswering',
    ),
    (
        'visual-question-answering',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForVisualQuestionAnswering',
    ),
    ('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
    (
        'zero-shot-image-classification',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForZeroShotImageClassification',
    ),
    ('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
    ('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
    ('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]

def 			_snake_case    (					lowerCamelCase__     :   Dict  )    ->     Optional[Any]:
							lowerCamelCase_	:							Optional[Any]									=re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,						lowerCamelCase__  )
							return [m.group(0  ) for m in matches]

def 			_snake_case    (					)    ->     Tuple:
							lowerCamelCase_	:							Any									=transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							lowerCamelCase_	:							Any									={
							    config.replace("Config"    ,						""  ): model_type for model_type, config in config_maping_names.items()
							}

							# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
							lowerCamelCase_	:							Any									=collections.defaultdict(lowerCamelCase__  )
							lowerCamelCase_	:							str									=collections.defaultdict(lowerCamelCase__  )
							lowerCamelCase_	:							Any									=collections.defaultdict(lowerCamelCase__  )

							# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
							for attr_name in dir(lowerCamelCase__  ):
														lowerCamelCase_	:							Any									=None
														if _re_tf_models.match(lowerCamelCase__  ) is not None:
																					lowerCamelCase_	:							Union[str, Any]									=tf_models
																					lowerCamelCase_	:							Optional[int]									=_re_tf_models.match(lowerCamelCase__  ).groups()[0]
														elif _re_flax_models.match(lowerCamelCase__  ) is not None:
																					lowerCamelCase_	:							List[str]									=flax_models
																					lowerCamelCase_	:							Any									=_re_flax_models.match(lowerCamelCase__  ).groups()[0]
														elif _re_pt_models.match(lowerCamelCase__  ) is not None:
																					lowerCamelCase_	:							str									=pt_models
																					lowerCamelCase_	:							Union[str, Any]									=_re_pt_models.match(lowerCamelCase__  ).groups()[0]

														if lookup_dict is not None:
																					while len(lowerCamelCase__  ) > 0:
																												if attr_name in model_prefix_to_model_type:
																																			lowerCamelCase_	:							List[str]									=True
																																			break
																												# Try again after removing the last word in the name
																												lowerCamelCase_	:							Dict									="".join(camel_case_split(lowerCamelCase__  )[:-1]  )

							lowerCamelCase_	:							Tuple									=set(list(pt_models.keys()  ) + list(tf_models.keys()  ) + list(flax_models.keys()  )  )
							lowerCamelCase_	:							List[str]									=list(lowerCamelCase__  )
							all_models.sort()

							lowerCamelCase_	:							Dict									={"model_type": all_models}
							lowerCamelCase_	:							Dict									=[pt_models[t] for t in all_models]
							lowerCamelCase_	:							Any									=[tf_models[t] for t in all_models]
							lowerCamelCase_	:							str									=[flax_models[t] for t in all_models]

							# Now let's use the auto-mapping names to make sure
							lowerCamelCase_	:							Union[str, Any]									={}
							for t in all_models:
														if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
																					lowerCamelCase_	:							Union[str, Any]									="AutoProcessor"
														elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
																					lowerCamelCase_	:							Any									="AutoTokenizer"
														elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
																					lowerCamelCase_	:							Tuple									="AutoFeatureExtractor"
														else:
																					# Default to AutoTokenizer if a model has nothing, for backward compatibility.
																					lowerCamelCase_	:							List[str]									="AutoTokenizer"

							lowerCamelCase_	:							List[Any]									=[processors[t] for t in all_models]

							return pd.DataFrame(lowerCamelCase__  )

def 			_snake_case    (					lowerCamelCase__     :   Dict  )    ->     Any:
							lowerCamelCase_	:							str									=[
							    transformers_module.models.auto.modeling_auto,
							    transformers_module.models.auto.modeling_tf_auto,
							    transformers_module.models.auto.modeling_flax_auto,
							]
							for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
														lowerCamelCase_	:							Any									=[model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
														lowerCamelCase_	:							Tuple									=[auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
														# Loop through all three frameworks
														for module, cls, mapping in zip(lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__  ):
																					# The type of pipeline may not exist in this framework
																					if not hasattr(lowerCamelCase__    ,						lowerCamelCase__  ):
																												continue
																					# First extract all model_names
																					lowerCamelCase_	:							str									=[]
																					for name in getattr(lowerCamelCase__    ,						lowerCamelCase__  ).values():
																												if isinstance(lowerCamelCase__    ,						lowerCamelCase__  ):
																																			model_names.append(lowerCamelCase__  )
																												else:
																																			model_names.extend(list(lowerCamelCase__  )  )

            # Add pipeline tag and auto model class for those models
																					table.update({model_name: (pipeline_tag, cls) for model_name in model_names}  )

							return table

def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   List[Any]  )    ->     List[str]:
							lowerCamelCase_	:							List[str]									=get_frameworks_table()
							lowerCamelCase_	:							Dict									=Dataset.from_pandas(lowerCamelCase__  )

							lowerCamelCase_	:							List[Any]									=hf_hub_download(
							    "huggingface/transformers-metadata"    ,						"pipeline_tags.json"    ,						repo_type="dataset"    ,						token=lowerCamelCase__  )
							lowerCamelCase_	:							int									=Dataset.from_json(lowerCamelCase__  )
							lowerCamelCase_	:							str									={
							    tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
							    for i in range(len(lowerCamelCase__  )  )
							}
							lowerCamelCase_	:							Union[str, Any]									=update_pipeline_and_auto_class_table(lowerCamelCase__  )

							# Sort the model classes to avoid some nondeterministic updates to create false update commits.
							lowerCamelCase_	:							Dict									=sorted(table.keys()  )
							lowerCamelCase_	:							Tuple									=pd.DataFrame(
							    {
							        "model_class": model_classes,
							        "pipeline_tag": [table[m][0] for m in model_classes],
							        "auto_class": [table[m][1] for m in model_classes],
							    }  )
							lowerCamelCase_	:							List[Any]									=Dataset.from_pandas(lowerCamelCase__  )

							with tempfile.TemporaryDirectory() as tmp_dir:
														frameworks_dataset.to_json(os.path.join(lowerCamelCase__    ,						"frameworks.json"  )  )
														tags_dataset.to_json(os.path.join(lowerCamelCase__    ,						"pipeline_tags.json"  )  )

														if commit_sha is not None:
																					lowerCamelCase_	:							Union[str, Any]									=(
																					    F"""Update with commit {commit_sha}\n\nSee: """
																					    F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
																					)
														else:
																					lowerCamelCase_	:							Tuple									="Update"

														upload_folder(
														    repo_id="huggingface/transformers-metadata"    ,						folder_path=lowerCamelCase__    ,						repo_type="dataset"    ,						token=lowerCamelCase__    ,						commit_message=lowerCamelCase__    ,						)

def 			_snake_case    (					)    ->     Dict:
							lowerCamelCase_	:							str									={tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
							lowerCamelCase_	:							Any									=transformers_module.pipelines.SUPPORTED_TASKS
							lowerCamelCase_	:							Optional[Any]									=[]
							for key in pipeline_tasks:
														if key not in in_table:
																					lowerCamelCase_	:							int									=pipeline_tasks[key]["pt"]
																					if isinstance(lowerCamelCase__    ,						(list, tuple)  ):
																												lowerCamelCase_	:							int									=model[0]
																					lowerCamelCase_	:							Any									=model.__name__
																					if model not in in_table.values():
																												missing.append(lowerCamelCase__  )

							if len(lowerCamelCase__  ) > 0:
														lowerCamelCase_	:							Tuple									=", ".join(lowerCamelCase__  )
														raise ValueError(
														    "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
														    F"""`utils/update_metadata.py`: {msg}. Please add them!"""  )


if __name__ == "__main__":
						A__	:    Optional[int]			 = argparse.ArgumentParser()
						parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
						parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
						parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
						A__	:    int			 = parser.parse_args()

						if args.check_only:
												check_pipeline_tags()
						else:
												update_metadata(args.token, args.commit_sha)











"""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        lowercase__      :
			_UpperCAmelCase		:CommonSchedulerState

			# setable values
			_UpperCAmelCase		:jnp.ndarray
			_UpperCAmelCase		:jnp.ndarray
			_UpperCAmelCase		:Optional[int]             =			None


			@classmethod
			def      UpperCAmelCase__						(				cls					:  int       ,	snake_case__					:  CommonSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray ):
										return cls(common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__ )







@dataclass
class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:DDPMSchedulerState







class        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:Any             =			[e.name for e in FlaxKarrasDiffusionSchedulers]

			_UpperCAmelCase		:jnp.dtype


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return True


			@register_to_config
			def __init__(				self					:  Optional[int]       ,	snake_case__					:  int = 1000       ,	snake_case__					:  float = 0.0_001       ,	snake_case__					:  float = 0.02       ,	snake_case__					:  str = "linear"       ,	snake_case__					:  Optional[jnp.ndarray] = None       ,	snake_case__					:  str = "fixed_small"       ,	snake_case__					:  bool = True       ,	snake_case__					:  str = "epsilon"       ,	snake_case__					:  jnp.dtype = jnp.floataa       ,	):
										lowerCamelCase_	:							str									=dtype


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Optional[CommonSchedulerState] = None ):
										if common is None:
																	lowerCamelCase_	:							int									=CommonSchedulerState.create(self )

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

										lowerCamelCase_	:							str									=jnp.arange(0       ,	self.config.num_train_timesteps ).round()[::-1]

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


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[int] = None ):
										return sample


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  int       ,	snake_case__					:  Tuple = () ):
										lowerCamelCase_	:							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
										lowerCamelCase_	:							List[str]									=(jnp.arange(0       ,	snake_case__ ) * step_ratio).round()[::-1]

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


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  Any=None ):
										lowerCamelCase_	:							List[str]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Union[str, Any]									=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
										lowerCamelCase_	:							Tuple									=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

										if variance_type is None:
																	lowerCamelCase_	:							List[Any]									=self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small":
																	lowerCamelCase_	:							List[str]									=jnp.clip(snake_case__       ,	a_min=1E-20 )
										# for rl-diffuser https://arxiv.org/abs/2205.09991
										elif variance_type == "fixed_small_log":
																	lowerCamelCase_	:							Dict									=jnp.log(jnp.clip(snake_case__       ,	a_min=1E-20 ) )
										elif variance_type == "fixed_large":
																	lowerCamelCase_	:							Optional[Any]									=state.common.betas[t]
										elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowerCamelCase_	:							Any									=jnp.log(state.common.betas[t] )
										elif variance_type == "learned":
																	return predicted_variance
										elif variance_type == "learned_range":
																	lowerCamelCase_	:							List[str]									=variance
																	lowerCamelCase_	:							Optional[int]									=state.common.betas[t]
																	lowerCamelCase_	:							Dict									=(predicted_variance + 1) / 2
																	lowerCamelCase_	:							Dict									=frac * max_log + (1 - frac) * min_log

										return variance


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  int       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[jax.random.KeyArray] = None       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							Union[str, Any]									=timestep

										if key is None:
																	lowerCamelCase_	:							Dict									=jax.random.PRNGKey(0 )

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

										# 1. compute alphas, betas
										lowerCamelCase_	:							Union[str, Any]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Dict									=jnp.where(t > 0       ,	state.common.alphas_cumprod[t - 1]       ,	jnp.array(1.0       ,	dtype=self.dtype ) )
										lowerCamelCase_	:							Any									=1 - alpha_prod_t
										lowerCamelCase_	:							List[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":
																	lowerCamelCase_	:							int									=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
																	lowerCamelCase_	:							List[Any]									=model_output
										elif self.config.prediction_type == "v_prediction":
																	lowerCamelCase_	:							Tuple									=(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:
																	lowerCamelCase_	:							List[Any]									=jnp.clip(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
										lowerCamelCase_	:							int									=(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
										lowerCamelCase_	:							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
										lowerCamelCase_	:							Any									=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

										lowerCamelCase_	:							Tuple									=jnp.where(t > 0       ,	random_variance()       ,	jnp.zeros(model_output.shape       ,	dtype=self.dtype ) )

										lowerCamelCase_	:							str									=pred_prev_sample + variance

										if not return_dict:
																	return (pred_prev_sample, state)

										return FlaxDDPMSchedulerOutput(prev_sample=snake_case__       ,	state=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return add_noise_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return get_velocity_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def __len__(				self					:  Tuple ):
										return self.config.num_train_timesteps



