
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

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





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	1






	@register_to_config
	def __init__(			self   :					List[Any]				,A   :					str=20_00				,A   :					str=0.1				,A   :					List[str]=20				,A   :					int=1E-3					):
					__A       =					None
					__A       =					None
					__A       =					None






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					List[str]				,A   :					Union[str, torch.device] = None					):
					__A       =					torch.linspace(1				,self.config.sampling_eps				,A				,device=A					)






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					List[Any]				,A   :					Optional[Any]				,A   :					Union[str, Any]				,A   :					Union[str, Any]=None					):
					if self.timesteps is None:
									raise ValueError(
									    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

					# TODO(Patrick) better comments + non-PyTorch
					# postprocess model score
					__A       =					(
					    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
					)
					__A       =					torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
					__A       =					std.flatten()
					while len(std.shape					) < len(score.shape					):
									__A       =					std.unsqueeze(-1					)
					__A       =					-score / std

					# compute
					__A       =					-1.0 / len(self.timesteps					)

					__A       =					self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
					__A       =					beta_t.flatten()
					while len(beta_t.shape					) < len(x.shape					):
									__A       =					beta_t.unsqueeze(-1					)
					__A       =					-0.5 * beta_t * x

					__A       =					torch.sqrt(A					)
					__A       =					drift - diffusion**2 * score
					__A       =					x + drift * dt

					# add noise
					__A       =					randn_tensor(x.shape				,layout=x.layout				,generator=A				,device=x.device				,dtype=x.dtype					)
					__A       =					x_mean + diffusion * math.sqrt(-dt					) * noise

					return x, x_mean







	def __len__(			self   :					Optional[Any]					):
					return self.config.num_train_timesteps


'''simple docstring'''

import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
     import torch


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


snake_case									=    logging.get_logger(__name__)
snake_case									=    {"""vocab_file""": """spiece.model"""}

snake_case									=    {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

snake_case									=    {
    """AI-Sweden/gpt-sw3-126m""": 20_48,
    """AI-Sweden/gpt-sw3-350m""": 20_48,
    """AI-Sweden/gpt-sw3-1.6b""": 20_48,
    """AI-Sweden/gpt-sw3-6.7b""": 20_48,
    """AI-Sweden/gpt-sw3-20b""": 20_48,
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Dict	=			VOCAB_FILES_NAMES
 A_				:				Optional[int]	=			PRETRAINED_VOCAB_FILES_MAP
 A_				:				Optional[Any]	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A_				:				Union[str, Any]	=			["""input_ids""", """attention_mask"""]


 def __init__(    self  : List[str]			,							a__  : Any			,							a__  : Dict=False			,							a__  : List[Any]=False			,							a__  : Dict=False			,							a__  : Any=None			,							a__  : Tuple=None			,							a__  : str=None			,							a__  : Optional[int]=None			,							a__  : Optional[Dict[str, Any]] = None			,							**a__  : Union[str, Any]			,							):
  '''simple docstring'''

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

  lowerCAmelCase__     :   List[str]     		=	kwargs.get("name_or_path"    )
  if name_or_path is None:
   logger.warning(
       "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
       " you are testing the model, this can safely be ignored"    )
   lowerCAmelCase__     :   Dict     		=	"None"

  # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
  lowerCAmelCase__     :   Tuple     		=	"<|endoftext|>" if eos_token is None else eos_token
  lowerCAmelCase__     :   List[str]     		=	"<unk>" if unk_token is None else unk_token
  if "gpt-sw3-7b" in name_or_path:
   lowerCAmelCase__     :   List[str]     		=	unk_token if pad_token is None else pad_token
   lowerCAmelCase__     :   Any     		=	eos_token if bos_token is None else bos_token
  else:
   lowerCAmelCase__     :   str     		=	"<pad>" if pad_token is None else pad_token
   lowerCAmelCase__     :   Union[str, Any]     		=	"<s>" if bos_token is None else bos_token

  super().__init__(
      do_lower_case=a__			,							remove_space=a__			,							keep_accents=a__			,							bos_token=a__			,							eos_token=a__			,							unk_token=a__			,							pad_token=a__			,							sp_model_kwargs=self.sp_model_kwargs			,							**a__			,							)

  lowerCAmelCase__     :   Dict     		=	do_lower_case
  lowerCAmelCase__     :   Union[str, Any]     		=	remove_space
  lowerCAmelCase__     :   Any     		=	keep_accents
  lowerCAmelCase__     :   List[str]     		=	vocab_file

  lowerCAmelCase__     :   Dict     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(a__    )

  # Used for whitespace normalization in input texts
  # fmt : off
  lowerCAmelCase__     :   List[str]     		=	{" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
  # fmt : on

  # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
  lowerCAmelCase__     :   Dict     		=	re.compile(
      F'''[{''.join(map(a__			,							list(range(0			,							9    )    ) + list(range(11			,							32    )    ) + list(range(127			,							160    )    ) + [160, 173, 8203]    )    )}]'''    )


 def __getstate__(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.__dict__.copy()
  lowerCAmelCase__     :   List[str]     		=	None
  return state


 def __setstate__(    self  : List[Any]			,							a__  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	d

  # for backward compatibility
  if not hasattr(self			,							"sp_model_kwargs"    ):
   lowerCAmelCase__     :   int     		=	{}

  lowerCAmelCase__     :   str     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(self.vocab_file    )


 @property
 # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
 def    _A	(    self  : str    ):
  '''simple docstring'''

  return len(self.sp_model    )


 def    _A	(    self  : List[str]			,							a__  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.non_printing_characters_re.sub(""			,							a__    )

  # Normalize whitespaces
  lowerCAmelCase__     :   int     		=	"".join([char if char not in self.whitespaces else " " for char in text]    )

  # NFC Unicode normalization
  lowerCAmelCase__     :   Tuple     		=	unicodedata.normalize("NFC"			,							a__    )
  return text


 def    _A	(    self  : str			,							a__  : str			,							**a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	self.preprocess_text(a__    )
  return self.sp_model.encode(a__			,							out_type=a__    )


 def    _A	(    self  : List[str]			,							a__  : str    ):
  '''simple docstring'''

  return self.sp_model.PieceToId(a__    )


 def    _A	(    self  : Optional[int]			,							a__  : int    ):
  '''simple docstring'''

  return self.sp_model.IdToPiece(a__    )


 @staticmethod
 def    _A	(    a__  : str    ):
  '''simple docstring'''

  return out_string


 def    _A	(    self  : int			,							a__  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	[]
  lowerCAmelCase__     :   Tuple     		=	""
  lowerCAmelCase__     :   str     		=	False
  for token in tokens:
   # make sure that special tokens are not decoded using sentencepiece model
   if token in self.all_special_tokens:
    # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
    if not prev_is_special:
     out_string += " "

    out_string += self.sp_model.decode(a__    ) + token
    lowerCAmelCase__     :   Union[str, Any]     		=	True
    lowerCAmelCase__     :   Dict     		=	[]
   else:
    current_sub_tokens.append(a__    )
    lowerCAmelCase__     :   Dict     		=	False
  out_string += self.sp_model.decode(a__    )

  return out_string


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	{self.convert_ids_to_tokens(a__    ): i for i in range(self.vocab_size    )}
  vocab.update(self.added_tokens_encoder    )
  return vocab


 def    _A	(    self  : str			,							a__  : str			,							a__  : Optional[str] = None    ):
  '''simple docstring'''

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

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

  return (out_vocab_file,)


 def    _A	(    self  : List[Any]			,							a__  : Union[str, List[str]]			,							a__  : Union[str, bool] = False    ):
  '''simple docstring'''

  if isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   int     		=	self.preprocess_text(a__    )
   lowerCAmelCase__     :   Dict     		=	self.sp_model.encode(a__    )
  else:
   lowerCAmelCase__     :   Union[str, Any]     		=	[self.preprocess_text(a__    ) for t in text]
   lowerCAmelCase__     :   Optional[int]     		=	self.sp_model.encode(a__    )

  if return_tensors is True or return_tensors == "pt":
   lowerCAmelCase__     :   Union[str, Any]     		=	torch.tensor(a__    )

  return token_ids


 def    _A	(    self  : Dict			,							a__  : Union[int, List[int]]    ):
  '''simple docstring'''

  return self.sp_model.decode(a__    )


 def    _A	(    self  : int			,							a__  : "Conversation"    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
  lowerCAmelCase__     :   List[str]     		=	(
      F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(a__    ) + F'''{self.bos_token}Bot:'''
  )
  return self.encode(text=a__    )





