




"""simple docstring"""

import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a					=			logging.get_logger(__name__)

a					=			"▁"

a					=			{"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

a					=			{
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

a					=			{
    "vocab_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
    },
    "sentencepiece_model_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
    },
}

a					=			{
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

a					=			{
    "ernie-m-base": {"do_lower_case": False},
    "ernie-m-large": {"do_lower_case": False},
}





class 				lowercase_       (     _a							):



 '''simple docstring'''
 UpperCAmelCase    :     List[Any]        =	['''input_ids''']

 UpperCAmelCase    :     Tuple        =	VOCAB_FILES_NAMES
 UpperCAmelCase    :     Optional[Any]        =	PRETRAINED_INIT_CONFIGURATION
 UpperCAmelCase    :     List[Any]        =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase    :     Optional[Any]        =	PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase    :     Union[str, Any]        =	RESOURCE_FILES_NAMES

 def __init__( self       :       int    ,							_UpperCAmelCase       :       int    ,							_UpperCAmelCase       :       Tuple=None    ,							_UpperCAmelCase       :       Any=False    ,							_UpperCAmelCase       :       int="utf8"    ,							_UpperCAmelCase       :       Tuple="[UNK]"    ,							_UpperCAmelCase       :       List[Any]="[SEP]"    ,							_UpperCAmelCase       :       str="[PAD]"    ,							_UpperCAmelCase       :       Union[str, Any]="[CLS]"    ,							_UpperCAmelCase       :       List[Any]="[MASK]"    ,							_UpperCAmelCase       :       Optional[Dict[str, Any]] = None    ,							**_UpperCAmelCase       :       int    ,							):
       _A					   =   {} if sp_model_kwargs is None else sp_model_kwargs
       super().__init__(
           do_lower_case=UpperCamelCase__    ,							unk_token=UpperCamelCase__    ,							sep_token=UpperCamelCase__    ,							pad_token=UpperCamelCase__    ,							cls_token=UpperCamelCase__    ,							mask_token=UpperCamelCase__    ,							vocab_file=UpperCamelCase__    ,							encoding=UpperCamelCase__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**UpperCamelCase__    ,							)
       _A					   =   do_lower_case
       _A					   =   sentencepiece_model_ckpt
       _A					   =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
       self.sp_model.Load(UpperCamelCase__		)

       # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
       if vocab_file is not None:
             _A					   =   self.load_vocab(filepath=UpperCamelCase__		)
       else:
             _A					   =   {self.sp_model.id_to_piece(UpperCamelCase__		): id for id in range(self.sp_model.get_piece_size()		)}
       _A					   =   {v: k for k, v in self.vocab.items()}

 def 			lowerCAmelCase_							( self       :       Optional[int]    ,							_UpperCAmelCase       :       Tuple		):







       if text is None:
             return None

       _A					   =   self.tokenize(UpperCamelCase__		)
       _A   ,    _A					   =   '', []

       for i, ch in enumerate(UpperCamelCase__		):
             if ch in self.SP_CHAR_MAPPING:
                   _A					   =   self.SP_CHAR_MAPPING.get(UpperCamelCase__		)
             else:
                   _A					   =   unicodedata.normalize('NFKC'    ,							UpperCamelCase__		)
             if self.is_whitespace(UpperCamelCase__		):
                   continue
             normalized_text += ch
             char_mapping.extend([i] * len(UpperCamelCase__		)		)

       _A   ,    _A   ,    _A					   =   normalized_text, [], 0

       if self.do_lower_case:
             _A					   =   text.lower()

       for token in split_tokens:
             if token[:1] == "▁":
                   _A					   =   token[1:]
             _A					   =   text[offset:].index(UpperCamelCase__		) + offset
             _A					   =   start + len(UpperCamelCase__		)

             token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)		)
             _A					   =   end
       return token_mapping

 @property
 def 			lowerCAmelCase_							( self       :       List[str]		):
       return len(self.vocab		)

 def 			lowerCAmelCase_							( self       :       List[str]		):
       return dict(self.vocab    ,							**self.added_tokens_encoder		)

 def __getstate__( self       :       Optional[Any]		):
       _A					   =   self.__dict__.copy()
       _A					   =   None
       return state

 def __setstate__( self       :       List[Any]    ,							_UpperCAmelCase       :       List[str]		):
       _A					   =   d

       # for backward compatibility
       if not hasattr(self    ,							'sp_model_kwargs'		):
             _A					   =   {}

       _A					   =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
       self.sp_model.Load(self.sentencepiece_model_ckpt		)

 def 			lowerCAmelCase_							( self       :       Optional[int]    ,							_UpperCAmelCase       :       Any		):
       return "".join((self.SP_CHAR_MAPPING.get(UpperCamelCase__    ,							UpperCamelCase__		) for c in text)		)

 def 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       List[str]=False    ,							_UpperCAmelCase       :       str=64    ,							_UpperCAmelCase       :       Optional[Any]=0.1		):







       if self.sp_model_kwargs.get('enable_sampling'		) is True:
             _A					   =   True
       if self.sp_model_kwargs.get('alpha'		) is not None:
             _A					   =   self.sp_model_kwargs.get('alpha'		)
       if self.sp_model_kwargs.get('nbest_size'		) is not None:
             _A					   =   self.sp_model_kwargs.get('nbest_size'		)

       if not enable_sampling:
             _A					   =   self.sp_model.EncodeAsPieces(UpperCamelCase__		)
       else:
             _A					   =   self.sp_model.SampleEncodeAsPieces(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__		)
       _A					   =   []
       for pi, piece in enumerate(UpperCamelCase__		):
             if piece == SPIECE_UNDERLINE:
                   if not pieces[pi + 1].startswith(UpperCamelCase__		) and pi != 0:
                         new_pieces.append(UpperCamelCase__		)
                         continue
                   else:
                         continue
             _A					   =   0
             for i, chunk in enumerate(UpperCamelCase__		):
                   if chunk == SPIECE_UNDERLINE:
                         continue
                   if self.is_ch_char(UpperCamelCase__		) or self.is_punct(UpperCamelCase__		):
                         if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]		)
                         new_pieces.append(UpperCamelCase__		)
                         _A					   =   i + 1
                   elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                         if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]		)
                         _A					   =   i
                   elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                         if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]		)
                         _A					   =   i
             if len(UpperCamelCase__		) > lst_i:
                   new_pieces.append(piece[lst_i:]		)
       return new_pieces

 def 			lowerCAmelCase_							( self       :       Tuple    ,							_UpperCAmelCase       :       Any		):
       _A					   =   ''.join(UpperCamelCase__		).replace(UpperCamelCase__    ,							' '		).strip()
       return out_string

 def 			lowerCAmelCase_							( self       :       Dict    ,							_UpperCAmelCase       :       Union[str, Any]		):
       _A					   =   self.convert_ids_to_tokens(UpperCamelCase__		)
       _A					   =   ''.join(UpperCamelCase__		).replace(UpperCamelCase__    ,							' '		).strip()
       return out_string

 def 			lowerCAmelCase_							( self       :       int    ,							_UpperCAmelCase       :       List[Any]		):
       return self.vocab.get(UpperCamelCase__    ,							self.vocab.get(self.unk_token		)		)

 def 			lowerCAmelCase_							( self       :       Optional[int]    ,							_UpperCAmelCase       :       List[Any]		):
       return self.reverse_vocab.get(UpperCamelCase__    ,							self.unk_token		)

 def 			lowerCAmelCase_							( self       :       List[str]    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       str=None		):







       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       _A					   =   [self.cls_token_id]
       _A					   =   [self.sep_token_id]
       return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep

 def 			lowerCAmelCase_							( self       :       Optional[Any]    ,							_UpperCAmelCase       :       Optional[Any]    ,							_UpperCAmelCase       :       int=None		):







       if offset_mapping_a is None:
             return [(0, 0)] + offset_mapping_a + [(0, 0)]

       return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]

 def 			lowerCAmelCase_							( self       :       List[Any]    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       Union[str, Any]=None    ,							_UpperCAmelCase       :       Union[str, Any]=False		):







       if already_has_special_tokens:
             if token_ids_a is not None:
                   raise ValueError(
                       'You should not supply a second sequence if the provided sequence of '
                       'ids is already formatted with special tokens for the model.'		)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
             return [1] + ([0] * len(UpperCamelCase__		)) + [1, 1] + ([0] * len(UpperCamelCase__		)) + [1]
       return [1] + ([0] * len(UpperCamelCase__		)) + [1]

 def 			lowerCAmelCase_							( self       :       str    ,							_UpperCAmelCase       :       List[int]    ,							_UpperCAmelCase       :       Optional[List[int]] = None		):







       if token_ids_a is None:
             # [CLS] X [SEP]
             return (len(UpperCamelCase__		) + 2) * [0]

       # [CLS] A [SEP] [SEP] B [SEP]
       return [0] * (len(UpperCamelCase__		) + 1) + [1] * (len(UpperCamelCase__		) + 3)

 def 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       int		):







       if "\u4e00" <= char <= "\u9fff":
             return True
       return False

 def 			lowerCAmelCase_							( self       :       List[str]    ,							_UpperCAmelCase       :       str		):







       if ("a" <= char <= "z") or ("A" <= char <= "Z"):
             return True
       return False

 def 			lowerCAmelCase_							( self       :       Optional[Any]    ,							_UpperCAmelCase       :       List[Any]		):







       if char in ",;:.?!~，；：。？！《》【】":
             return True
       return False

 def 			lowerCAmelCase_							( self       :       str    ,							_UpperCAmelCase       :       str		):







       if char == " " or char == "\t" or char == "\n" or char == "\r":
             return True
       if len(UpperCamelCase__		) == 1:
             _A					   =   unicodedata.category(UpperCamelCase__		)
             if cat == "Zs":
                   return True
       return False

 def 			lowerCAmelCase_							( self       :       List[Any]    ,							_UpperCAmelCase       :       List[str]		):
       _A					   =   {}
       with io.open(UpperCamelCase__    ,							'r'    ,							encoding='utf-8'		) as f:
             for index, line in enumerate(UpperCamelCase__		):
                   _A					   =   line.rstrip('\n'		)
                   _A					   =   int(UpperCamelCase__		)

       return token_to_idx



 def 			lowerCAmelCase_							( self       :       str    ,							_UpperCAmelCase       :       str    ,							_UpperCAmelCase       :       Optional[str] = None		):
       _A					   =   0
       if os.path.isdir(UpperCamelCase__		):
             _A					   =   os.path.join(
                 UpperCamelCase__    ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)
       else:
             _A					   =   (filename_prefix + '-' if filename_prefix else '') + save_directory
       with open(UpperCamelCase__    ,							'w'    ,							encoding='utf-8'		) as writer:
             for token, token_index in sorted(self.vocab.items()    ,							key=lambda _UpperCAmelCase		: kv[1]		):
                   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!'		)
                         _A					   =   token_index
                   writer.write(token + '\n'		)
                   index += 1

       _A					   =   os.path.join(UpperCamelCase__    ,							'sentencepiece.bpe.model'		)
       with open(UpperCamelCase__    ,							'wb'		) as fi:
             _A					   =   self.sp_model.serialized_model_proto()
             fi.write(UpperCamelCase__		)

       return (vocab_file,)






'''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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None







def 				__lowerCamelCase				(   A__    ,						A__=0.999    ,						A__="cosine"    ,						)       ->	Tuple:






   """simple docstring"""
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(A__    ):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(A__    ):
         return math.exp(t * -12.0    )

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""    )

   UpperCamelCase					=  []
   for i in range(A__    ):
      UpperCamelCase					=  i / num_diffusion_timesteps
      UpperCamelCase					=  (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(A__    ) / alpha_bar_fn(A__    )    ,						A__    )    )
   return torch.tensor(A__    ,						dtype=torch.floataa    )

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    @register_to_config
    def __init__(  self      :					List[str]  ,						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"  ,						):


       """simple docstring"""







       if beta_schedule != "squaredcos_cap_v2":
          raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'' )

       UpperCamelCase					=  betas_for_alpha_bar(UpperCamelCase__ )

       UpperCamelCase					=  1.0 - self.betas
       UpperCamelCase					=  torch.cumprod(self.alphas  ,						dim=0 )
       UpperCamelCase					=  torch.tensor(1.0 )

       # standard deviation of the initial noise distribution
       UpperCamelCase					=  1.0

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

       UpperCamelCase					=  variance_type






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       UpperCamelCase					=  (np.arange(0  ,						UpperCamelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					Tuple=None ):


       """simple docstring"""







       if prev_timestep is None:
          UpperCamelCase					=  t - 1

       UpperCamelCase					=  self.alphas_cumprod[t]
       UpperCamelCase					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase					=  1 - alpha_prod_t
       UpperCamelCase					=  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
       else:
          UpperCamelCase					=  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
       UpperCamelCase					=  beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
          UpperCamelCase					=  self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
          UpperCamelCase					=  torch.log(torch.clamp(UpperCamelCase__  ,						min=1E-2_0 ) )
          UpperCamelCase					=  torch.exp(0.5 * variance )
       elif variance_type == "learned_range":
          # NOTE difference with DDPM scheduler
          UpperCamelCase					=  variance.log()
          UpperCamelCase					=  beta.log()

          UpperCamelCase					=  (predicted_variance + 1) / 2
          UpperCamelCase					=  frac * max_log + (1 - frac) * min_log

       return variance






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
          UpperCamelCase				,      UpperCamelCase					=  torch.split(UpperCamelCase__  ,						sample.shape[1]  ,						dim=1 )
       else:
          UpperCamelCase					=  None

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

       UpperCamelCase					=  self.alphas_cumprod[t]
       UpperCamelCase					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase					=  1 - alpha_prod_t
       UpperCamelCase					=  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
          UpperCamelCase					=  self.alphas[t]
       else:
          UpperCamelCase					=  1 - alpha_prod_t / alpha_prod_t_prev
          UpperCamelCase					=  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":
          UpperCamelCase					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
          UpperCamelCase					=  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:
          UpperCamelCase					=  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
       UpperCamelCase					=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       UpperCamelCase					=  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
       UpperCamelCase					=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       UpperCamelCase					=  0
       if t > 0:
          UpperCamelCase					=  randn_tensor(
              model_output.shape  ,						dtype=model_output.dtype  ,						generator=UpperCamelCase__  ,						device=model_output.device )

          UpperCamelCase					=  self._get_variance(
              UpperCamelCase__  ,						predicted_variance=UpperCamelCase__  ,						prev_timestep=UpperCamelCase__  ,						)

          if self.variance_type == "fixed_small_log":
             UpperCamelCase					=  variance
          elif self.variance_type == "learned_range":
             UpperCamelCase					=  (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.' )

          UpperCamelCase					=  variance * variance_noise

       UpperCamelCase					=  pred_prev_sample + variance

       if not return_dict:
          return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.IntTensor  ,						):


       """simple docstring"""







       UpperCamelCase					=  self.alphas_cumprod.to(device=original_samples.device  ,						dtype=original_samples.dtype )
       UpperCamelCase					=  timesteps.to(original_samples.device )

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

       UpperCamelCase					=  (1 - alphas_cumprod[timesteps]) ** 0.5
       UpperCamelCase					=  sqrt_one_minus_alpha_prod.flatten()
       while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
          UpperCamelCase					=  sqrt_one_minus_alpha_prod.unsqueeze(-1 )

       UpperCamelCase					=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples

