






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


A__	:    Tuple			 = logging.get_logger(__name__)

A__	:    Optional[int]			 = '▁'

A__	:    List[str]			 = {'vocab_file': 'sentencepiece.bpe.model'}

A__	:    str			 = {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

A__	:    Optional[Any]			 = {
    'facebook/mbart-large-en-ro': 1_024,
    'facebook/mbart-large-cc25': 1_024,
}

# fmt: off
A__	:    Tuple			 = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[Any]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Any             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Optional[int]             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Optional[Any]             =			["input_ids", "attention_mask"]

			_UpperCAmelCase		:Optional[int]             =			[]
			_UpperCAmelCase		:Optional[Any]             =			[]


			def __init__(				self					:  Any       ,	snake_case__					:  Dict       ,	snake_case__					:  Tuple="<s>"       ,	snake_case__					:  Dict="</s>"       ,	snake_case__					:  Any="</s>"       ,	snake_case__					:  List[Any]="<s>"       ,	snake_case__					:  Dict="<unk>"       ,	snake_case__					:  Tuple="<pad>"       ,	snake_case__					:  Optional[int]="<mask>"       ,	snake_case__					:  int=None       ,	snake_case__					:  int=None       ,	snake_case__					:  str=None       ,	snake_case__					:  Any = None       ,	snake_case__					:  Union[str, Any]=None       ,	**snake_case__					:  Union[str, Any]       ,	):
										lowerCamelCase_	:							Dict									=AddedToken(_SCREAMING_SNAKE_CASE       ,	lstrip=_SCREAMING_SNAKE_CASE       ,	rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE ) else mask_token

										lowerCamelCase_	:							Union[str, Any]									={} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=_SCREAMING_SNAKE_CASE       ,	eos_token=_SCREAMING_SNAKE_CASE       ,	unk_token=_SCREAMING_SNAKE_CASE       ,	sep_token=_SCREAMING_SNAKE_CASE       ,	cls_token=_SCREAMING_SNAKE_CASE       ,	pad_token=_SCREAMING_SNAKE_CASE       ,	mask_token=_SCREAMING_SNAKE_CASE       ,	tokenizer_file=_SCREAMING_SNAKE_CASE       ,	src_lang=_SCREAMING_SNAKE_CASE       ,	tgt_lang=_SCREAMING_SNAKE_CASE       ,	additional_special_tokens=_SCREAMING_SNAKE_CASE       ,	sp_model_kwargs=self.sp_model_kwargs       ,	**_SCREAMING_SNAKE_CASE       ,	)

										lowerCamelCase_	:							Tuple									=spm.SentencePieceProcessor(**self.sp_model_kwargs )
										self.sp_model.Load(str(_SCREAMING_SNAKE_CASE ) )
										lowerCamelCase_	:							int									=vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# Mimic fairseq token-to-id alignment for the first 4 token
										lowerCamelCase_	:							List[Any]									={'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										lowerCamelCase_	:							Union[str, Any]									=1

										lowerCamelCase_	:							Tuple									=len(self.sp_model )
										lowerCamelCase_	:							Optional[int]									={
										    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_SCREAMING_SNAKE_CASE )
										}
										lowerCamelCase_	:							str									={v: k for k, v in self.lang_code_to_id.items()}
										lowerCamelCase_	:							str									=len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset

										self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
										lowerCamelCase_	:							int									={v: k for k, v in self.fairseq_tokens_to_ids.items()}
										lowerCamelCase_	:							int									=list(self.lang_code_to_id.keys() )

										if additional_special_tokens is not None:
																	# Only add those special tokens if they are not already there.
																	self._additional_special_tokens.extend(
																	    [t for t in additional_special_tokens if t not in self._additional_special_tokens] )

										lowerCamelCase_	:							Optional[Any]									=src_lang if src_lang is not None else '''en_XX'''
										lowerCamelCase_	:							List[Any]									=self.lang_code_to_id[self._src_lang]
										lowerCamelCase_	:							Tuple									=tgt_lang
										self.set_src_lang_special_tokens(self._src_lang )


			def __getstate__(				self					:  int ):
										lowerCamelCase_	:							Tuple									=self.__dict__.copy()
										lowerCamelCase_	:							Optional[int]									=None
										lowerCamelCase_	:							Union[str, Any]									=self.sp_model.serialized_model_proto()
										return state


			def __setstate__(				self					:  Optional[Any]       ,	snake_case__					:  Optional[Any] ):
										lowerCamelCase_	:							List[Any]									=d

										# for backward compatibility
										if not hasattr(self       ,	"sp_model_kwargs" ):
																	lowerCamelCase_	:							Optional[int]									={}

										lowerCamelCase_	:							str									=spm.SentencePieceProcessor(**self.sp_model_kwargs )
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto )


			@property
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1  # Plus 1 for the mask token


			@property
			def      UpperCAmelCase__						(				self					:  Dict ):
										return self._src_lang


			@src_lang.setter
			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  Optional[int] ):
										lowerCamelCase_	:							str									=new_src_lang
										self.set_src_lang_special_tokens(self._src_lang )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  str       ,	snake_case__					:  str = None       ,	snake_case__					:  Optional[Any] = False ):





										if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_SCREAMING_SNAKE_CASE       ,	token_ids_a=_SCREAMING_SNAKE_CASE       ,	already_has_special_tokens=_SCREAMING_SNAKE_CASE )

										lowerCamelCase_	:							str									=[1] * len(self.prefix_tokens )
										lowerCamelCase_	:							Union[str, Any]									=[1] * len(self.suffix_tokens )
										if token_ids_a is None:
																	return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE )) + suffix_ones
										return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE )) + ([0] * len(_SCREAMING_SNAKE_CASE )) + suffix_ones


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  Dict       ,	snake_case__					:  str = None ):





										if token_ids_a is None:
																	return self.prefix_tokens + token_ids_a + self.suffix_tokens
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  Dict = None ):
										lowerCamelCase_	:							Any									=[self.sep_token_id]
										lowerCamelCase_	:							Optional[int]									=[self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  str       ,	snake_case__					:  Optional[int]       ,	**snake_case__					:  Tuple ):





										if src_lang is None or tgt_lang is None:
																	raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
										lowerCamelCase_	:							List[str]									=src_lang
										lowerCamelCase_	:							Any									=self(_SCREAMING_SNAKE_CASE       ,	add_special_tokens=_SCREAMING_SNAKE_CASE       ,	return_tensors=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE )
										lowerCamelCase_	:							Optional[Any]									=self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE )
										lowerCamelCase_	:							List[Any]									=tgt_lang_id
										return inputs


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Any									={self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
										vocab.update(self.added_tokens_encoder )
										return vocab


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  Any ):
										return self.sp_model.encode(_SCREAMING_SNAKE_CASE       ,	out_type=_SCREAMING_SNAKE_CASE )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  Tuple ):





										if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
										lowerCamelCase_	:							Tuple									=self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE )

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  List[Any] ):





										if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  Tuple ):
										lowerCamelCase_	:							int									=''''''.join(_SCREAMING_SNAKE_CASE ).replace(_SCREAMING_SNAKE_CASE       ,	" " ).strip()
										return out_string


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  List[str]       ,	snake_case__					:  Optional[Any] = None ):





										if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
																	return
										lowerCamelCase_	:							int									=os.path.join(
										    _SCREAMING_SNAKE_CASE       ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

										if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
																	copyfile(self.vocab_file       ,	_SCREAMING_SNAKE_CASE )
										elif not os.path.isfile(self.vocab_file ):
																	with open(_SCREAMING_SNAKE_CASE       ,	"wb" ) as fi:
																								lowerCamelCase_	:							Optional[int]									=self.sp_model.serialized_model_proto()
																								fi.write(_SCREAMING_SNAKE_CASE )

										return (out_vocab_file,)


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  List[Any]       ,	snake_case__					:  List[str] = "en_XX"       ,	snake_case__					:  Tuple = None       ,	snake_case__					:  Any = "ro_RO"       ,	**snake_case__					:  Union[str, Any]       ,	):
										lowerCamelCase_	:							Optional[Any]									=src_lang
										lowerCamelCase_	:							List[Any]									=tgt_lang
										return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE )


			def      UpperCAmelCase__						(				self					:  Dict ):
										return self.set_src_lang_special_tokens(self.src_lang )


			def      UpperCAmelCase__						(				self					:  Dict ):
										return self.set_tgt_lang_special_tokens(self.tgt_lang )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  str ):
										lowerCamelCase_	:							Optional[int]									=self.lang_code_to_id[src_lang]
										lowerCamelCase_	:							int									=[]
										lowerCamelCase_	:							Any									=[self.eos_token_id, self.cur_lang_code]


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  List[str] ):
										lowerCamelCase_	:							Tuple									=self.lang_code_to_id[lang]
										lowerCamelCase_	:							Dict									=[]
										lowerCamelCase_	:							List[Any]									=[self.eos_token_id, self.cur_lang_code]











"""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



