


"""simple docstring"""





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


snake_case__		:     str    				=				logging.get_logger(__name__)

snake_case__		:     Union[str, Any]    				=				'''▁'''

snake_case__		:     Optional[Any]    				=				{'''vocab_file''': '''sentencepiece.bpe.model'''}

snake_case__		:     Optional[int]    				=				{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
        ),
    }
}

snake_case__		:     Tuple    				=				{
    '''facebook/nllb-200-distilled-600M''': 1_024,
}

# fmt: off
snake_case__		:     Optional[Any]    				=				['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']



class 		snake_case_(       a__			):
     __UpperCamelCase  =					VOCAB_FILES_NAMES
     __UpperCamelCase  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase  =					PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase  =					['''input_ids''', '''attention_mask''']

     __UpperCamelCase  =					[]
     __UpperCamelCase  =					[]






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					List[str]="<s>"					,				UpperCamelCase_  :					Dict="</s>"					,				UpperCamelCase_  :					int="</s>"					,				UpperCamelCase_  :					Optional[Any]="<s>"					,				UpperCamelCase_  :					List[Any]="<unk>"					,				UpperCamelCase_  :					Union[str, Any]="<pad>"					,				UpperCamelCase_  :					Dict="<mask>"					,				UpperCamelCase_  :					str=None					,				UpperCamelCase_  :					Tuple=None					,				UpperCamelCase_  :					int=None					,				UpperCamelCase_  :					Optional[Dict[str, Any]] = None					,				UpperCamelCase_  :					Tuple=None					,				UpperCamelCase_  :					Tuple=False					,				**UpperCamelCase_  :					Optional[int]					,				):
           # Mask token behave like a normal word, i.e. include the space before it
           lowerCAmelCase					:	Tuple       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else mask_token

           lowerCAmelCase					:	Optional[Any]       =   {} if sp_model_kwargs is None else sp_model_kwargs
           lowerCAmelCase					:	Union[str, Any]       =   legacy_behaviour

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

           lowerCAmelCase					:	str       =   spm.SentencePieceProcessor(**self.sp_model_kwargs				)
           self.sp_model.Load(str(UpperCamelCase_				)				)
           lowerCAmelCase					:	List[str]       =   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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
           # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

           # Mimic fairseq token-to-id alignment for the first 4 token
           lowerCAmelCase					:	Optional[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					:	Tuple       =   1

           lowerCAmelCase					:	Dict       =   len(self.sp_model				)
           lowerCAmelCase					:	Optional[Any]       =   {
               code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase_				)
           }
           lowerCAmelCase					:	int       =   {v: k for k, v in self.lang_code_to_id.items()}
           lowerCAmelCase					:	List[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					:	Any       =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
           lowerCAmelCase					:	Union[str, Any]       =   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					:	Tuple       =   src_lang if src_lang is not None else '''eng_Latn'''
           lowerCAmelCase					:	Union[str, Any]       =   self.lang_code_to_id[self._src_lang]
           lowerCAmelCase					:	List[str]       =   tgt_lang
           self.set_src_lang_special_tokens(self._src_lang				)






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






     def __setstate__(	self  :					Any					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	str       =   d

           # for backward compatibility
           if not hasattr(self					,				'''sp_model_kwargs'''				):
                 lowerCAmelCase					:	List[str]       =   {}

           lowerCAmelCase					:	Union[str, Any]       =   spm.SentencePieceProcessor(**self.sp_model_kwargs				)
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)






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






     @property
     def      lowerCamelCase__     (	self  :					Optional[int]				):
           return self._src_lang






     @src_lang.setter
     def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	Tuple       =   new_src_lang
           self.set_src_lang_special_tokens(self._src_lang				)






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None					,				UpperCamelCase_  :					bool = False				):

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

           lowerCAmelCase					:	Any       =   [1] * len(self.prefix_tokens				)
           lowerCAmelCase					:	Tuple       =   [1] * len(self.suffix_tokens				)
           if token_ids_a is None:
                 return prefix_ones + ([0] * len(UpperCamelCase_				)) + suffix_ones
           return prefix_ones + ([0] * len(UpperCamelCase_				)) + ([0] * len(UpperCamelCase_				)) + suffix_ones






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = 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      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           lowerCAmelCase					:	int       =   [self.sep_token_id]
           lowerCAmelCase					:	Any       =   [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      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[str]					,				UpperCamelCase_  :					Optional[str]					,				**UpperCamelCase_  :					Union[str, Any]				):
           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					:	str       =   self(UpperCamelCase_					,				add_special_tokens=UpperCamelCase_					,				return_tensors=UpperCamelCase_					,				**UpperCamelCase_				)
           lowerCAmelCase					:	str       =   self.convert_tokens_to_ids(UpperCamelCase_				)
           lowerCAmelCase					:	Dict       =   tgt_lang_id
           return inputs






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Optional[int]       =   {self.convert_ids_to_tokens(UpperCamelCase_				): i for i in range(self.vocab_size				)}
           vocab.update(self.added_tokens_encoder				)
           return vocab






     def      lowerCamelCase__     (	self  :					Tuple					,				UpperCamelCase_  :					str				):
           return self.sp_model.encode(UpperCamelCase_					,				out_type=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Optional[Any]				):
           if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
           lowerCAmelCase					:	List[Any]       =   self.sp_model.PieceToId(UpperCamelCase_				)

           # 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      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str				):
           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      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Optional[int]				):
           lowerCAmelCase					:	Optional[int]       =   ''''''.join(UpperCamelCase_				).replace(UpperCamelCase_					,				''' '''				).strip()
           return out_string






     def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[str] = None				):
           if not os.path.isdir(UpperCamelCase_				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           lowerCAmelCase					:	List[Any]       =   os.path.join(
               UpperCamelCase_					,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

           if os.path.abspath(self.vocab_file				) != os.path.abspath(UpperCamelCase_				) and os.path.isfile(self.vocab_file				):
                 copyfile(self.vocab_file					,				UpperCamelCase_				)
           elif not os.path.isfile(self.vocab_file				):
                 with open(UpperCamelCase_					,				'''wb'''				) as fi:
                       lowerCAmelCase					:	List[str]       =   self.sp_model.serialized_model_proto()
                       fi.write(UpperCamelCase_				)

           return (out_vocab_file,)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					str = "eng_Latn"					,				UpperCamelCase_  :					Optional[List[str]] = None					,				UpperCamelCase_  :					str = "fra_Latn"					,				**UpperCamelCase_  :					Optional[Any]					,				):
           lowerCAmelCase					:	List[str]       =   src_lang
           lowerCAmelCase					:	Union[str, Any]       =   tgt_lang
           return super().prepare_seqaseq_batch(UpperCamelCase_					,				UpperCamelCase_					,				**UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					int				):
           return self.set_src_lang_special_tokens(self.src_lang				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           return self.set_tgt_lang_special_tokens(self.tgt_lang				)






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					Dict				):
           lowerCAmelCase					:	Tuple       =   self.lang_code_to_id[src_lang]
           if self.legacy_behaviour:
                 lowerCAmelCase					:	List[str]       =   []
                 lowerCAmelCase					:	str       =   [self.eos_token_id, self.cur_lang_code]
           else:
                 lowerCAmelCase					:	Optional[Any]       =   [self.cur_lang_code]
                 lowerCAmelCase					:	Tuple       =   [self.eos_token_id]






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	str       =   self.lang_code_to_id[lang]
           if self.legacy_behaviour:
                 lowerCAmelCase					:	List[Any]       =   []
                 lowerCAmelCase					:	int       =   [self.eos_token_id, self.cur_lang_code]
           else:
                 lowerCAmelCase					:	Tuple       =   [self.cur_lang_code]
                 lowerCAmelCase					:	Dict       =   [self.eos_token_id]






"""simple docstring"""





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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 		snake_case_(       a__			):
     __UpperCamelCase  =					42
     __UpperCamelCase  =					None



def      _snake_case						(						_snake_case				:			Dict ,   _snake_case				:			List[str]=0.999 ,   _snake_case				:			Dict="cosine" ,   ):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_snake_case				:			List[Any]			):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_snake_case				:			Optional[int]			):
                  return math.exp(t * -12.0			)

      else:
            raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''			)

      lowerCAmelCase					:	List[Any]       =   []
      for i in range(_snake_case			):
            lowerCAmelCase					:	int       =   i / num_diffusion_timesteps
            lowerCAmelCase					:	Tuple       =   (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_snake_case			) / alpha_bar_fn(_snake_case			) ,   _snake_case			)			)
      return torch.tensor(_snake_case ,   dtype=torch.floataa			)






class 		snake_case_(       a__							,					a__			):






     @register_to_config
     def __init__(	self  :					Any					,				UpperCamelCase_  :					int = 1_0_0_0					,				UpperCamelCase_  :					str = "fixed_small_log"					,				UpperCamelCase_  :					bool = True					,				UpperCamelCase_  :					Optional[float] = 1.0					,				UpperCamelCase_  :					str = "epsilon"					,				UpperCamelCase_  :					str = "squaredcos_cap_v2"					,				):
           if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''				)

           lowerCAmelCase					:	Any       =   betas_for_alpha_bar(UpperCamelCase_				)

           lowerCAmelCase					:	str       =   1.0 - self.betas
           lowerCAmelCase					:	Union[str, Any]       =   torch.cumprod(self.alphas					,				dim=0				)
           lowerCAmelCase					:	Tuple       =   torch.tensor(1.0				)

           # standard deviation of the initial noise distribution
           lowerCAmelCase					:	Any       =   1.0

           # setable values
           lowerCAmelCase					:	Any       =   None
           lowerCAmelCase					:	Any       =   torch.from_numpy(np.arange(0					,				UpperCamelCase_				)[::-1].copy()				)

           lowerCAmelCase					:	List[str]       =   variance_type






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					Optional[int] = None				):
           return sample






     def      lowerCamelCase__     (	self  :					Tuple					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Union[str, torch.device] = None				):
           lowerCAmelCase					:	Any       =   num_inference_steps
           lowerCAmelCase					:	str       =   (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
           lowerCAmelCase					:	Tuple       =   (np.arange(0					,				UpperCamelCase_				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
           lowerCAmelCase					:	Any       =   torch.from_numpy(UpperCamelCase_				).to(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					str=None					,				UpperCamelCase_  :					Tuple=None					,				UpperCamelCase_  :					Any=None				):
           if prev_timestep is None:
                 lowerCAmelCase					:	Any       =   t - 1

           lowerCAmelCase					:	int       =   self.alphas_cumprod[t]
           lowerCAmelCase					:	Union[str, Any]       =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCAmelCase					:	Dict       =   1 - alpha_prod_t
           lowerCAmelCase					:	str       =   1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                 lowerCAmelCase					:	Tuple       =   self.betas[t]
           else:
                 lowerCAmelCase					:	Union[str, Any]       =   1 - alpha_prod_t / alpha_prod_t_prev

           # 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					:	Optional[Any]       =   beta_prod_t_prev / beta_prod_t * beta

           if variance_type is None:
                 lowerCAmelCase					:	List[str]       =   self.config.variance_type

           # hacks - were probably added for training stability
           if variance_type == "fixed_small_log":
                 lowerCAmelCase					:	Any       =   torch.log(torch.clamp(UpperCamelCase_					,				min=1E-20				)				)
                 lowerCAmelCase					:	Union[str, Any]       =   torch.exp(0.5 * variance				)
           elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 lowerCAmelCase					:	Optional[Any]       =   variance.log()
                 lowerCAmelCase					:	Union[str, Any]       =   beta.log()

                 lowerCAmelCase					:	Dict       =   (predicted_variance + 1) / 2
                 lowerCAmelCase					:	Union[str, Any]       =   frac * max_log + (1 - frac) * min_log

           return variance






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					List[Any]=None					,				UpperCamelCase_  :					bool = True					,				):
           lowerCAmelCase					:	Optional[Any]       =   timestep

           if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 lowerCAmelCase,      lowerCAmelCase					:	List[Any]       =   torch.split(UpperCamelCase_					,				sample.shape[1]					,				dim=1				)
           else:
                 lowerCAmelCase					:	Optional[int]       =   None

           # 1. compute alphas, betas
           if prev_timestep is None:
                 lowerCAmelCase					:	Any       =   t - 1

           lowerCAmelCase					:	Union[str, Any]       =   self.alphas_cumprod[t]
           lowerCAmelCase					:	Optional[int]       =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           lowerCAmelCase					:	int       =   1 - alpha_prod_t
           lowerCAmelCase					:	str       =   1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                 lowerCAmelCase					:	List[Any]       =   self.betas[t]
                 lowerCAmelCase					:	Optional[int]       =   self.alphas[t]
           else:
                 lowerCAmelCase					:	List[Any]       =   1 - alpha_prod_t / alpha_prod_t_prev
                 lowerCAmelCase					:	Dict       =   1 - beta

           # 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					:	List[Any]       =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           elif self.config.prediction_type == "sample":
                 lowerCAmelCase					:	Tuple       =   model_output
           else:
                 raise ValueError(
                     F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
                     ''' for the UnCLIPScheduler.'''				)

           # 3. Clip "predicted x_0"
           if self.config.clip_sample:
                 lowerCAmelCase					:	Dict       =   torch.clamp(
                     UpperCamelCase_					,				-self.config.clip_sample_range					,				self.config.clip_sample_range				)

           # 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 * beta) / beta_prod_t
           lowerCAmelCase					:	List[Any]       =   alpha ** 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					:	str       =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

           # 6. Add noise
           lowerCAmelCase					:	int       =   0
           if t > 0:
                 lowerCAmelCase					:	Union[str, Any]       =   randn_tensor(
                     model_output.shape					,				dtype=model_output.dtype					,				generator=UpperCamelCase_					,				device=model_output.device				)

                 lowerCAmelCase					:	Any       =   self._get_variance(
                     UpperCamelCase_					,				predicted_variance=UpperCamelCase_					,				prev_timestep=UpperCamelCase_					,				)

                 if self.variance_type == "fixed_small_log":
                       lowerCAmelCase					:	str       =   variance
                 elif self.variance_type == "learned_range":
                       lowerCAmelCase					:	Optional[Any]       =   (0.5 * variance).exp()
                 else:
                       raise ValueError(
                           F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                           ''' for the UnCLIPScheduler.'''				)

                 lowerCAmelCase					:	List[Any]       =   variance * variance_noise

           lowerCAmelCase					:	int       =   pred_prev_sample + variance

           if not return_dict:
                 return (pred_prev_sample,)

           return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase_					,				pred_original_sample=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					torch.FloatTensor					,				UpperCamelCase_  :					torch.IntTensor					,				):
           # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
           lowerCAmelCase					:	Tuple       =   self.alphas_cumprod.to(device=original_samples.device					,				dtype=original_samples.dtype				)
           lowerCAmelCase					:	int       =   timesteps.to(original_samples.device				)

           lowerCAmelCase					:	Dict       =   alphas_cumprod[timesteps] ** 0.5
           lowerCAmelCase					:	str       =   sqrt_alpha_prod.flatten()
           while len(sqrt_alpha_prod.shape				) < len(original_samples.shape				):
                 lowerCAmelCase					:	Any       =   sqrt_alpha_prod.unsqueeze(-1				)

           lowerCAmelCase					:	List[str]       =   (1 - alphas_cumprod[timesteps]) ** 0.5
           lowerCAmelCase					:	Tuple       =   sqrt_one_minus_alpha_prod.flatten()
           while len(sqrt_one_minus_alpha_prod.shape				) < len(original_samples.shape				):
                 lowerCAmelCase					:	int       =   sqrt_one_minus_alpha_prod.unsqueeze(-1				)

           lowerCAmelCase					:	Dict       =   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
           return noisy_samples


