



"""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


__A      :							Dict   =      logging.get_logger(__name__)
__A      :							Optional[int]   =      {"vocab_file": "spiece.model"}

__A      :							List[Any]   =      {
    "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",
    }
}

__A      :							List[str]   =      {
    "AI-Sweden/gpt-sw3-126m": 2048,
    "AI-Sweden/gpt-sw3-350m": 2048,
    "AI-Sweden/gpt-sw3-1.6b": 2048,
    "AI-Sweden/gpt-sw3-6.7b": 2048,
    "AI-Sweden/gpt-sw3-20b": 2048,
}
class 	_a       (    lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__						=		VOCAB_FILES_NAMES
			UpperCamelCase__						=		PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__						=		["""input_ids""", """attention_mask"""]



			def __init__(						self   :			List[Any] ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			Optional[int]=False ,      __UpperCamelCase   :			List[str]=False ,      __UpperCamelCase   :			Dict=False ,      __UpperCamelCase   :			Dict=None ,      __UpperCamelCase   :			int=None ,      __UpperCamelCase   :			str=None ,      __UpperCamelCase   :			Union[str, Any]=None ,      __UpperCamelCase   :			Optional[Dict[str, Any]] = None ,      **__UpperCamelCase   :			List[Any] ,      )->None:
							_UpperCAmelCase							=			{} if sp_model_kwargs is None else sp_model_kwargs

							_UpperCAmelCase							=			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'''      )
											_UpperCAmelCase							=			'''None'''

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

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

							_UpperCAmelCase							=			do_lower_case
							_UpperCAmelCase							=			remove_space
							_UpperCAmelCase							=			keep_accents
							_UpperCAmelCase							=			vocab_file

							_UpperCAmelCase							=			spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(__UpperCAmelCase      )

							# Used for whitespace normalization in input texts
							# fmt : off
							_UpperCAmelCase							=			{''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
							# fmt : on

							# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
							_UpperCAmelCase							=			re.compile(
							    F'[{"".join(map(__UpperCAmelCase ,      list(range(0 ,      9      )      ) + list(range(1_1 ,      3_2      )      ) + list(range(1_2_7 ,      1_6_0      )      ) + [1_6_0, 1_7_3, 8_2_0_3]      )      )}]'      )



			def __getstate__(						self   :			int      )->Any:
							_UpperCAmelCase							=			self.__dict__.copy()
							_UpperCAmelCase							=			None
							return state



			def __setstate__(						self   :			int ,      __UpperCamelCase   :			List[Any]      )->Optional[int]:
							_UpperCAmelCase							=			d

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

							_UpperCAmelCase							=			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        lowercase__  (						self   :			Optional[Any]      )->int:
							return len(self.sp_model      )



			def        lowercase__  (						self   :			List[Any] ,      __UpperCamelCase   :			str      )->str:
							_UpperCAmelCase							=			self.non_printing_characters_re.sub('''''' ,      __UpperCAmelCase      )

							# Normalize whitespaces
							_UpperCAmelCase							=			''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]      )

							# NFC Unicode normalization
							_UpperCAmelCase							=			unicodedata.normalize('''NFC''' ,      __UpperCAmelCase      )
							return text



			def        lowercase__  (						self   :			List[Any] ,      __UpperCamelCase   :			str ,      **__UpperCamelCase   :			Tuple      )->List[str]:
							_UpperCAmelCase							=			self.preprocess_text(__UpperCAmelCase      )
							return self.sp_model.encode(__UpperCAmelCase ,      out_type=__UpperCAmelCase      )



			def        lowercase__  (						self   :			List[Any] ,      __UpperCamelCase   :			str      )->int:
							return self.sp_model.PieceToId(__UpperCAmelCase      )



			def        lowercase__  (						self   :			Any ,      __UpperCamelCase   :			int      )->str:
							return self.sp_model.IdToPiece(__UpperCAmelCase      )



			@staticmethod
			def        lowercase__  (						__UpperCamelCase   :			str      )->str:
							return out_string



			def        lowercase__  (						self   :			str ,      __UpperCamelCase   :			List[str]      )->str:
							_UpperCAmelCase							=			[]
							_UpperCAmelCase							=			''''''
							_UpperCAmelCase							=			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(__UpperCAmelCase      ) + token
															_UpperCAmelCase							=			True
															_UpperCAmelCase							=			[]
											else:
															current_sub_tokens.append(__UpperCAmelCase      )
															_UpperCAmelCase							=			False
							out_string += self.sp_model.decode(__UpperCAmelCase      )

							return out_string



			def        lowercase__  (						self   :			List[str]      )->Dict[str, int]:
							_UpperCAmelCase							=			{self.convert_ids_to_tokens(__UpperCAmelCase      ): i for i in range(self.vocab_size      )}
							vocab.update(self.added_tokens_encoder      )
							return vocab



			def        lowercase__  (						self   :			Dict ,      __UpperCamelCase   :			str ,      __UpperCamelCase   :			Optional[str] = None      )->Tuple[str]:


							if not os.path.isdir(__UpperCAmelCase      ):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
											return
							_UpperCAmelCase							=			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:
															_UpperCAmelCase							=			self.sp_model.serialized_model_proto()
															fi.write(__UpperCAmelCase      )

							return (out_vocab_file,)



			def        lowercase__  (						self   :			Optional[int] ,      __UpperCamelCase   :			Union[str, List[str]] ,      __UpperCamelCase   :			Union[str, bool] = False      )->Union[List[int], List[List[int]], "torch.Tensor"]:


							if isinstance(__UpperCAmelCase ,      __UpperCAmelCase      ):
											_UpperCAmelCase							=			self.preprocess_text(__UpperCAmelCase      )
											_UpperCAmelCase							=			self.sp_model.encode(__UpperCAmelCase      )
							else:
											_UpperCAmelCase							=			[self.preprocess_text(__UpperCAmelCase      ) for t in text]
											_UpperCAmelCase							=			self.sp_model.encode(__UpperCAmelCase      )

							if return_tensors is True or return_tensors == "pt":
											_UpperCAmelCase							=			torch.tensor(__UpperCAmelCase      )

							return token_ids



			def        lowercase__  (						self   :			Dict ,      __UpperCamelCase   :			Union[int, List[int]]      )->str:
							return self.sp_model.decode(__UpperCAmelCase      )



			def        lowercase__  (						self   :			Optional[Any] ,      __UpperCamelCase   :			"Conversation"      )->List[int]:
							_UpperCAmelCase							=			[F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
							_UpperCAmelCase							=			(
							    F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(__UpperCAmelCase      ) + F'{self.bos_token}Bot:'
							)
							return self.encode(text=__UpperCAmelCase      )











def 						_a    (      a :int )							->			list:

 # bit count represents no. of bits in the gray code
 if bit_count < 0:
  raise ValueError('''The given input must be positive''' )

 # get the generated string sequence
 a		  =      gray_code_sequence_string(a )
 #
 # convert them to integers
 for i in range(len(a ) ):
  a		  =      int(sequence[i]	,							2 )

 return sequence
def 						_a    (      a :int )							->			list:

 # The approach is a recursive one
 # Base case achieved when either n = 0 or n=1
 if bit_count == 0:
  return ["0"]

 if bit_count == 1:
  return ["0", "1"]

 a		  =      1 << bit_count  # defines the length of the sequence
 # 1<< n is equivalent to 2^n

 # recursive answer will generate answer for n-1 bits
 a		  =      gray_code_sequence_string(bit_count - 1 )

 a		  =      []

 # append 0 to first half of the smaller sequence generated
 for i in range(seq_len // 2 ):
  a		  =      '''0''' + smaller_sequence[i]
  sequence.append(a )

 # append 1 to second half ... start from the end of the list
 for i in reversed(range(seq_len // 2 ) ):
  a		  =      '''1''' + smaller_sequence[i]
  sequence.append(a )

 return sequence


if __name__ == "__main__":
     import doctest

     doctest.testmod()


