
"""simple docstring"""




import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


A__      :	str          =    logging.get_logger(__name__)

A__      :	Any          =    {"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}

A__      :	int          =    {
    """vocab_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
    },
    """emoji_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
    },
}

A__      :	Dict          =    {
    """abeja/gpt-neox-japanese-2.7b""": 2_048,
}



def 							a__ (				lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Optional[Any]		):






     '''simple docstring'''
     with open(lowerCAmelCase       ,    "r"       ,    encoding="utf-8"		) as f:
          UpperCAmelCase__							: Optional[Any] 							=						json.loads(f.read()		)

     UpperCAmelCase__							: Optional[int] 							=						collections.OrderedDict()
     UpperCAmelCase__							: List[str] 							=						collections.OrderedDict()
     UpperCAmelCase__							: int 							=						collections.OrderedDict()
     with open(lowerCAmelCase       ,    "r"       ,    encoding="utf-8"		) as f:
          UpperCAmelCase__							: Dict 							=						f.readlines()
     UpperCAmelCase__							: List[str] 							=						[[t.rstrip("\n"		)] if (t == "," or "," not in t) else t.rstrip("\n"		).split(","		) for t in token]
     for idx, b in enumerate(lowerCAmelCase		):
          UpperCAmelCase__							: List[Any] 							=						b
          UpperCAmelCase__							: str 							=						idx
          for wd in b:
               UpperCAmelCase__							: str 							=						idx

     return vocab, raw_vocab, ids_to_tokens, emoji







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							VOCAB_FILES_NAMES
     _A								=							PRETRAINED_VOCAB_FILES_MAP
     _A								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A								=							['input_ids', 'attention_mask']



     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase="<|endoftext|>" ,			__UpperCamelCase="<|endoftext|>" ,			__UpperCamelCase="<|startoftext|>" ,			__UpperCamelCase="<|endoftext|>" ,			__UpperCamelCase=False ,			**__UpperCamelCase ,			)->					Dict:
          super().__init__(
              unk_token=__UpperCamelCase ,			pad_token=__UpperCamelCase ,			bos_token=__UpperCamelCase ,			eos_token=__UpperCamelCase ,			do_clean_text=__UpperCamelCase ,			**__UpperCamelCase ,			)
          if not os.path.isfile(__UpperCamelCase  ):
               raise ValueError(
                   F"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
                   " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"  )
          if not os.path.isfile(__UpperCamelCase  ):
               raise ValueError(
                   F"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"
                   " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"  )
          UpperCAmelCase__							: List[Any] 							=						do_clean_text
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: int 							=						load_vocab_and_emoji(__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: Tuple 							=						SubWordJapaneseTokenizer(
              vocab=self.vocab ,			ids_to_tokens=self.ids_to_tokens ,			emoji=self.emoji  )



     @property
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
          return len(self.raw_vocab  )



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          return dict(self.raw_vocab ,			**self.added_tokens_encoder  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
          return self.subword_tokenizer.tokenize(__UpperCamelCase ,			clean=self.do_clean_text  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Optional[int]:
          return self.vocab.get(__UpperCamelCase ,			self.vocab.get(self.unk_token  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          return self.subword_tokenizer.convert_id_to_token(__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Optional[Any]:
          UpperCAmelCase__							: str 							=						"".join(__UpperCamelCase  ).strip()
          return out_string



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[int]:
          UpperCAmelCase__							: Dict 							=						[]
          for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__UpperCamelCase ,			add_special_tokens=__UpperCamelCase  ) + [self.eos_token_id]  )

          if len(__UpperCamelCase  ) > self.model_max_length:
               UpperCAmelCase__							: List[str] 							=						input_ids[-self.model_max_length :]
          return input_ids



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					Tuple[str]:
          UpperCAmelCase__							: Any 							=						0
          if os.path.isdir(__UpperCamelCase  ):
               UpperCAmelCase__							: Optional[int] 							=						os.path.join(
                   __UpperCamelCase ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )
               UpperCAmelCase__							: Any 							=						os.path.join(
                   __UpperCamelCase ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]  )
          else:
               UpperCAmelCase__							: Tuple 							=						(
                   (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
               )
               UpperCAmelCase__							: List[str] 							=						(
                   (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
               )
          with open(__UpperCamelCase ,			"w" ,			encoding="utf-8"  ) as writer:
               for token_index, token in self.ids_to_tokens.items():
                    if index != token_index:
                         logger.warning(
                             F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                             " Please check that the vocabulary is not corrupted!"  )
                         UpperCAmelCase__							: str 							=						token_index
                    writer.write(",".join(__UpperCamelCase  ) + "\n"  )
                    index += 1
          with open(__UpperCamelCase ,			"w" ,			encoding="utf-8"  ) as writer:
               json.dump(self.emoji ,			__UpperCamelCase  )
          return vocab_file, emoji_file







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Optional[int]:
          UpperCAmelCase__							: Dict 							=						vocab  # same as swe
          UpperCAmelCase__							: Optional[int] 							=						ids_to_tokens  # same as bpe
          UpperCAmelCase__							: str 							=						emoji
          UpperCAmelCase__							: List[Any] 							=						np.max([len(__UpperCamelCase  ) for w in self.vocab.keys()]  )
          UpperCAmelCase__							: Tuple 							=						re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"  )
          UpperCAmelCase__							: Optional[int] 							=						re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"  )
          UpperCAmelCase__							: List[str] 							=						re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"  )
          UpperCAmelCase__							: Any 							=						re.compile(
              r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"  )
          UpperCAmelCase__							: List[str] 							=						re.compile(
              r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"  )
          UpperCAmelCase__							: int 							=						re.compile(
              r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"  )
          UpperCAmelCase__							: List[str] 							=						"─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
          UpperCAmelCase__							: Tuple 							=						"▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
          UpperCAmelCase__							: Optional[int] 							=						str.maketrans({k: "<BLOCK>" for k in keisen + blocks}  )



     def __len__(      self  )->					List[Any]:
          return len(self.ids_to_tokens  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Dict:
          UpperCAmelCase__							: Optional[int] 							=						self.content_repattera.sub("<URL>" ,			__UpperCamelCase  )
          UpperCAmelCase__							: Any 							=						self.content_repattera.sub("<EMAIL>" ,			__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						self.content_repattera.sub("<TEL>" ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						self.content_repattera.sub("<DATE>" ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						self.content_repattera.sub("<DATE>" ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						self.content_repattera.sub("<PRICE>" ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						content.translate(self.content_transa  )
          while "<BLOCK><BLOCK>" in content:
               UpperCAmelCase__							: Union[str, Any] 							=						content.replace("<BLOCK><BLOCK>" ,			"<BLOCK>"  )
          return content



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=False  )->					List[Any]:
          UpperCAmelCase__							: Optional[int] 							=						text.replace(" " ,			"<SP>"  )
          UpperCAmelCase__							: Any 							=						text.replace("　" ,			"<SP>"  )
          UpperCAmelCase__							: Union[str, Any] 							=						text.replace("\r\n" ,			"<BR>"  )
          UpperCAmelCase__							: List[str] 							=						text.replace("\n" ,			"<BR>"  )
          UpperCAmelCase__							: Any 							=						text.replace("\r" ,			"<BR>"  )
          UpperCAmelCase__							: int 							=						text.replace("\t" ,			"<TAB>"  )
          UpperCAmelCase__							: str 							=						text.replace("—" ,			"ー"  )
          UpperCAmelCase__							: Optional[Any] 							=						text.replace("−" ,			"ー"  )
          for k, v in self.emoji["emoji"].items():
               if k in text:
                    UpperCAmelCase__							: Any 							=						text.replace(__UpperCamelCase ,			__UpperCamelCase  )
          if clean:
               UpperCAmelCase__							: Any 							=						self.clean_text(__UpperCamelCase  )

          def check_simbol(__UpperCamelCase  ):
               UpperCAmelCase__							: Dict 							=						x.encode()
               if len(__UpperCamelCase  ) == 1 and len(__UpperCamelCase  ) == 2:
                    UpperCAmelCase__							: Optional[Any] 							=						(int(e[0]  ) << 8) + int(e[1]  )
                    if (
                        (c >= 0Xc2a1 and c <= 0Xc2bf)
                        or (c >= 0Xc780 and c <= 0Xc783)
                        or (c >= 0Xcab9 and c <= 0Xcbbf)
                        or (c >= 0Xcc80 and c <= 0Xcda2)
                    ):
                         return True
               return False

          def checkuae(__UpperCamelCase  ):
               UpperCAmelCase__							: Any 							=						x.encode()
               if len(__UpperCamelCase  ) == 1 and len(__UpperCamelCase  ) == 3:
                    UpperCAmelCase__							: Optional[int] 							=						(int(e[0]  ) << 16) + (int(e[1]  ) << 8) + int(e[2]  )
                    if c >= 0Xe2_8080 and c <= 0Xe2_b07f:
                         return True
               return False

          UpperCAmelCase__							: Any 							=						0
          UpperCAmelCase__							: str 							=						[]
          while pos < len(__UpperCamelCase  ):
               UpperCAmelCase__							: Any 							=						min(len(__UpperCamelCase  ) ,			pos + self.maxlen + 1  ) if text[pos] == "<" else pos + 3
               UpperCAmelCase__							: Any 							=						[]  # (token_id, token, pos)
               for e in range(__UpperCamelCase ,			__UpperCamelCase ,			-1  ):
                    UpperCAmelCase__							: str 							=						text[pos:e]
                    if wd in self.vocab:
                         if wd[0] == "<" and len(__UpperCamelCase  ) > 2:
                              UpperCAmelCase__							: Optional[int] 							=						[(self.vocab[wd], wd, e)]
                              break
                         else:
                              candidates.append((self.vocab[wd], wd, e)  )
               if len(__UpperCamelCase  ) > 0:
                    # the smallest token_id is adopted
                    UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						sorted(__UpperCamelCase ,			key=lambda __UpperCamelCase  : x[0]  )[0]
                    result.append(__UpperCamelCase  )
                    UpperCAmelCase__							: List[Any] 							=						e
               else:
                    UpperCAmelCase__							: Union[str, Any] 							=						pos + 1
                    UpperCAmelCase__							: Optional[int] 							=						text[pos:end]
                    if check_simbol(__UpperCamelCase  ):
                         result.append("<KIGOU>"  )
                    elif checkuae(__UpperCamelCase  ):
                         result.append("<U2000U2BFF>"  )
                    else:
                         for i in wd.encode("utf-8"  ):
                              result.append("<|byte%d|>" % i  )
                    UpperCAmelCase__							: List[Any] 							=						end
          return result



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase="\n"  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						[]
          UpperCAmelCase__							: str 							=						[]
          UpperCAmelCase__							: List[Any] 							=						self.ids_to_tokens[index][0]
          if word[:6] == "<|byte" and word[-2:] == "|>":
               byte_tokens.append(int(word[6:-2]  )  )
          else:
               if len(__UpperCamelCase  ) > 0:
                    words.append(bytearray(__UpperCamelCase  ).decode("utf-8" ,			errors="replace"  )  )
                    UpperCAmelCase__							: Dict 							=						[]
               if word[:7] == "<|emoji" and word[-2:] == "|>":
                    words.append(self.emoji["emoji_inv"][word]  )
               elif word == "<SP>":
                    words.append(" "  )
               elif word == "<BR>":
                    words.append(__UpperCamelCase  )
               elif word == "<TAB>":
                    words.append("\t"  )
               elif word == "<BLOCK>":
                    words.append("▀"  )
               elif word == "<KIGOU>":
                    words.append("ǀ"  )
               elif word == "<U2000U2BFF>":
                    words.append("‖"  )
               else:
                    words.append(__UpperCamelCase  )
          if len(__UpperCamelCase  ) > 0:
               words.append(bytearray(__UpperCamelCase  ).decode("utf-8" ,			errors="replace"  )  )
          UpperCAmelCase__							: str 							=						"".join(__UpperCamelCase  )
          return text





"""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						:


     '''simple docstring'''





     _A								=							42

     # setable values
     _A								=							42
     _A								=							42
     _A								=							None



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          return cls(common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase  )







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							[e.name for e in FlaxKarrasDiffusionSchedulers]

     _A								=							42



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return True



     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 10_00 ,			__UpperCamelCase = 0.0001 ,			__UpperCamelCase = 0.02 ,			__UpperCamelCase = "linear" ,			__UpperCamelCase = None ,			__UpperCamelCase = "fixed_small" ,			__UpperCamelCase = True ,			__UpperCamelCase = "epsilon" ,			__UpperCamelCase = jnp.floataa ,			)->					List[str]:
          UpperCAmelCase__							: int 							=						dtype



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None  )->					DDPMSchedulerState:
          if common is None:
               UpperCAmelCase__							: int 							=						CommonSchedulerState.create(self  )

          # standard deviation of the initial noise distribution
          UpperCAmelCase__							: Tuple 							=						jnp.array(1.0 ,			dtype=self.dtype  )

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

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					jnp.ndarray:
          return sample



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = ()  )->					DDPMSchedulerState:
          UpperCAmelCase__							: Dict 							=						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
          UpperCAmelCase__							: Optional[int] 							=						(jnp.arange(0 ,			__UpperCamelCase  ) * step_ratio).round()[::-1]

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			__UpperCamelCase=None  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: int 							=						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
          UpperCAmelCase__							: Any 							=						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

          if variance_type is None:
               UpperCAmelCase__							: Union[str, Any] 							=						self.config.variance_type

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

          return variance



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			)->					Union[FlaxDDPMSchedulerOutput, Tuple]:
          UpperCAmelCase__							: List[str] 							=						timestep

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

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

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

          # 6. Add noise
          def random_variance():
               UpperCAmelCase__							: Any 							=						jax.random.split(__UpperCamelCase ,			num=1  )
               UpperCAmelCase__							: int 							=						jax.random.normal(__UpperCamelCase ,			shape=model_output.shape ,			dtype=self.dtype  )
               return (self._get_variance(__UpperCamelCase ,			__UpperCamelCase ,			predicted_variance=__UpperCamelCase  ) ** 0.5) * noise

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

          UpperCAmelCase__							: Any 							=						pred_prev_sample + variance

          if not return_dict:
               return (pred_prev_sample, state)

          return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase ,			state=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return add_noise_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return get_velocity_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



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



