

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

import sentencepiece as spm

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


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

__A    	=			logging.get_logger(__name__)

__A    	=			{'''vocab_file''': '''tokenizer.model'''}

__A    	=			{
    '''vocab_file''': {
        '''hf-internal-testing/llama-tokenizer''': '''https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model''',
    },
    '''tokenizer_file''': {
        '''hf-internal-testing/llama-tokenizer''': '''https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json''',
    },
}
__A    	=			{
    '''hf-internal-testing/llama-tokenizer''': 20_48,
}
__A    	=			'''▁'''

__A	,  __A    	=			'''[INST]''', '''[/INST]'''
__A	,  __A    	=			'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
__A    	=			'''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''



class 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	Any		   =       VOCAB_FILES_NAMES
      UpperCamelCase_    :	Optional[int]		   =       PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_    :	int		   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_    :	Optional[Any]		   =       ["input_ids", "attention_mask"]



      def __init__(    self   :       List[str]    ,      A__   :       Optional[Any]    ,      A__   :       Optional[Any]="<unk>"    ,      A__   :       List[str]="<s>"    ,      A__   :       Optional[int]="</s>"    ,      A__   :       Tuple=None    ,      A__   :       Optional[Dict[str, Any]] = None    ,      A__   :       Optional[int]=True    ,      A__   :       Optional[Any]=False    ,      A__   :       Tuple=False    ,      A__   :       Tuple=True    ,      **A__   :       List[str]    ,      )					->  List[Any]:
        _snake_case		=							{} if sp_model_kwargs is None else sp_model_kwargs
        _snake_case		=							AddedToken(A__    ,      lstrip=A__    ,      rstrip=A__	) if isinstance(A__    ,      A__	) else bos_token
        _snake_case		=							AddedToken(A__    ,      lstrip=A__    ,      rstrip=A__	) if isinstance(A__    ,      A__	) else eos_token
        _snake_case		=							AddedToken(A__    ,      lstrip=A__    ,      rstrip=A__	) if isinstance(A__    ,      A__	) else unk_token
        _snake_case		=							AddedToken(A__    ,      lstrip=A__    ,      rstrip=A__	) if isinstance(A__    ,      A__	) else pad_token
        super().__init__(
            bos_token=A__    ,      eos_token=A__    ,      unk_token=A__    ,      pad_token=A__    ,      add_bos_token=A__    ,      add_eos_token=A__    ,      sp_model_kwargs=self.sp_model_kwargs    ,      clean_up_tokenization_spaces=A__    ,      legacy=A__    ,      **A__    ,      )
        if legacy:
          logger.warning_once(
              f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
              ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''	)
        _snake_case		=							legacy
        _snake_case		=							vocab_file
        _snake_case		=							add_bos_token
        _snake_case		=							add_eos_token
        _snake_case		=							spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(A__	)



      def __getstate__(    self   :       List[Any]	)					->  Union[str, Any]:
        _snake_case		=							self.__dict__.copy()
        _snake_case		=							None
        _snake_case		=							self.sp_model.serialized_model_proto()
        return state



      def __setstate__(    self   :       List[str]    ,      A__   :       Optional[Any]	)					->  Tuple:
        _snake_case		=							d
        _snake_case		=							spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)



      @property
      def 		UpperCamelCase_	(    self   :       str	)					->  Any:
        return self.sp_model.get_piece_size()



      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  Union[str, Any]:
        _snake_case		=							{self.convert_ids_to_tokens(A__	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab



      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       Any    ,      **A__   :       Dict	)					->  List[str]:
        # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
        # the beginning of the text
        if not self.legacy:
          _snake_case		=							SPIECE_UNDERLINE + text.replace(A__    ,      ''' '''	)
        return super().tokenize(A__    ,      **A__	)



      def 		UpperCamelCase_	(    self   :       Optional[int]    ,      A__   :       int	)					->  Tuple:
        if not self.legacy:
          _snake_case		=							text.startswith(A__	)
          if is_first:
            _snake_case		=							text[1:]

        _snake_case		=							self.sp_model.encode(A__    ,      out_type=A__	)

        if not self.legacy and not is_first and not text.startswith(''' '''	) and tokens[0].startswith(A__	):
          _snake_case		=							([tokens[0][1:]] if len(tokens[0]	) > 1 else []) + tokens[1:]
        return tokens



      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       Union[str, Any]	)					->  Union[str, Any]:
        return self.sp_model.piece_to_id(A__	)



      def 		UpperCamelCase_	(    self   :       Dict    ,      A__   :       Optional[Any]	)					->  List[Any]:
        _snake_case		=							self.sp_model.IdToPiece(A__	)
        return token



      def 		UpperCamelCase_	(    self   :       Dict    ,      A__   :       Union[str, Any]	)					->  int:
        _snake_case		=							[]
        _snake_case		=							''''''
        _snake_case		=							False
        for i, token in enumerate(A__	):
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
            if not prev_is_special and i != 0:
              out_string += " "
            out_string += self.sp_model.decode(A__	) + token
            _snake_case		=							True
            _snake_case		=							[]
          else:
            current_sub_tokens.append(A__	)
            _snake_case		=							False
        out_string += self.sp_model.decode(A__	)
        return out_string



      def 		UpperCamelCase_	(    self   :       Optional[Any]    ,      A__   :       int    ,      A__   :       Optional[str] = None	)					->  Tuple[str]:
        if not os.path.isdir(A__	):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
          return
        _snake_case		=							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:
            _snake_case		=							self.sp_model.serialized_model_proto()
            fi.write(A__	)

        return (out_vocab_file,)



      def 		UpperCamelCase_	(    self   :       Dict    ,      A__   :       Any    ,      A__   :       int=None	)					->  Tuple:
        _snake_case		=							[self.bos_token_id] if self.add_bos_token else []
        _snake_case		=							[self.eos_token_id] if self.add_eos_token else []

        _snake_case		=							bos_token_id + token_ids_a + eos_token_id

        if token_ids_a is not None:
          _snake_case		=							output + bos_token_id + token_ids_a + eos_token_id

        return output



      def 		UpperCamelCase_	(    self   :       Dict    ,      A__   :       List[int]    ,      A__   :       Optional[List[int]] = None    ,      A__   :       bool = False	)					->  List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=A__    ,      token_ids_a=A__    ,      already_has_special_tokens=A__	)

        _snake_case		=							[1] if self.add_bos_token else []
        _snake_case		=							[1] if self.add_eos_token else []

        if token_ids_a is None:
          return bos_token_id + ([0] * len(A__	)) + eos_token_id
        return (
            bos_token_id
            + ([0] * len(A__	))
            + eos_token_id
            + bos_token_id
            + ([0] * len(A__	))
            + eos_token_id
        )



      def 		UpperCamelCase_	(    self   :       Tuple    ,      A__   :       List[int]    ,      A__   :       Optional[List[int]] = None	)					->  List[int]:
        _snake_case		=							[self.bos_token_id] if self.add_bos_token else []
        _snake_case		=							[self.eos_token_id] if self.add_eos_token else []

        _snake_case		=							[0] * len(bos_token_id + token_ids_a + eos_token_id	)

        if token_ids_a is not None:
          output += [1] * len(bos_token_id + token_ids_a + eos_token_id	)

        return output







      def 		UpperCamelCase_	(    self   :       str    ,      A__   :       "Conversation"	)					->  List[int]:
        _snake_case		=							list(conversation.iter_texts()	)
        if not all([is_user for is_user, msg in dialogue[::2]]	) or not all(
            [not is_user for is_user, msg in dialogue[1::2]]	):
          raise ValueError(
              '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''	)

        _snake_case		=							[]
        if len(conversation.past_user_inputs	) > 0:
          if not conversation.past_user_inputs[0].startswith(A__	) or E_SYS not in conversation.past_user_inputs[0]:
            _snake_case		=							(
                B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
            )
        elif not dialogue[0][1].startswith(A__	) or E_SYS not in dialogue[0][1]:
          _snake_case		=							(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

        dialog_tokens += sum(
            [
                [self.bos_token_id]
                + self.encode(
                    f"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """    ,      add_special_tokens=A__	)
                + [self.eos_token_id]
                for prompt, answer in zip(dialogue[::2]    ,      dialogue[1::2]	)
            ]    ,      []    ,      )
        if not (dialogue[-1][0]):
          raise ValueError(f"""Last message must be from user, got {dialogue[-1]['role']}"""	)
        dialog_tokens += [self.bos_token_id] + self.encode(
            f"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""    ,      add_special_tokens=A__	)
        return dialog_tokens




from __future__ import annotations

from typing import Generic, TypeVar

__A    	=			TypeVar('''T''')



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       str    ,      A__   :       T	)					->  Union[str, Any]:
        _snake_case		=							data  # Assign data
        _snake_case		=							None  # Initialize next as null
        _snake_case		=							None  # Initialize prev as null



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       List[str]	)					->  None:
        _snake_case		=							None



      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       T	)					->  None:
        if self.head is None:
          _snake_case		=							Node(A__	)
        else:
          _snake_case		=							Node(A__	)
          _snake_case		=							new_node
          _snake_case		=							self.head
          _snake_case		=							None
          _snake_case		=							new_node



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  T | None:
        if self.head is None:
          return None
        else:
          assert self.head is not None
          _snake_case		=							self.head.data
          _snake_case		=							self.head.next
          if self.head is not None:
            _snake_case		=							None
          return temp



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  T | None:
        return self.head.data if self.head is not None else None



      def __len__(    self   :       Optional[int]	)					->  int:
        _snake_case		=							self.head
        _snake_case		=							0
        while temp is not None:
          count += 1
          _snake_case		=							temp.next
        return count



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  bool:
        return self.head is None







      def 		UpperCamelCase_	(    self   :       Dict	)					->  None:
        print('''stack elements are:'''	)
        _snake_case		=							self.head
        while temp is not None:
          print(temp.data    ,      end='''->'''	)
          _snake_case		=							temp.next


# Code execution starts here
if __name__ == "__main__":
 # Start with the empty stack
 __A    	=			Stack()

 # Insert 4 at the beginning. So stack becomes 4->None
 print('''Stack operations using Doubly LinkedList''')
 stack.push(4)

 # Insert 5 at the beginning. So stack becomes 4->5->None
 stack.push(5)

 # Insert 6 at the beginning. So stack becomes 4->5->6->None
 stack.push(6)

 # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
 stack.push(7)

 # Print the stack
 stack.print_stack()

 # Print the top element
 print('''\nTop element is ''', stack.top())

 # Print the stack size
 print('''Size of the stack is ''', len(stack))

 # pop the top element
 stack.pop()

 # pop the top element
 stack.pop()

 # two elements have now been popped off
 stack.print_stack()

 # Print True if the stack is empty else False
 print('''\nstack is empty:''', stack.is_empty())

