


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 				__SCREAMING_SNAKE_CASE   (			A__   ):
      A				:							Tuple    							=     VOCAB_FILES_NAMES
      A				:							List[str]    							=     PRETRAINED_VOCAB_FILES_MAP
      A				:							Union[str, Any]    							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A				:							List[Any]    							=     ['input_ids', 'attention_mask']


      def __init__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__="<unk>"							,						SCREAMING_SNAKE_CASE__="<s>"							,						SCREAMING_SNAKE_CASE__="</s>"							,						SCREAMING_SNAKE_CASE__=None							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__=True							,						SCREAMING_SNAKE_CASE__=False							,						SCREAMING_SNAKE_CASE__=False							,						SCREAMING_SNAKE_CASE__=True							,						**SCREAMING_SNAKE_CASE__							,						):
            lowercase			:   Dict					   =		{} if sp_model_kwargs is None else sp_model_kwargs
            lowercase			:   Tuple					   =		AddedToken(SCREAMING_SNAKE_CASE__							,						lstrip=SCREAMING_SNAKE_CASE__							,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else bos_token
            lowercase			:   Any					   =		AddedToken(SCREAMING_SNAKE_CASE__							,						lstrip=SCREAMING_SNAKE_CASE__							,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else eos_token
            lowercase			:   Optional[int]					   =		AddedToken(SCREAMING_SNAKE_CASE__							,						lstrip=SCREAMING_SNAKE_CASE__							,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else unk_token
            lowercase			:   List[Any]					   =		AddedToken(SCREAMING_SNAKE_CASE__							,						lstrip=SCREAMING_SNAKE_CASE__							,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else pad_token
            super().__init__(
                bos_token=SCREAMING_SNAKE_CASE__							,						eos_token=SCREAMING_SNAKE_CASE__							,						unk_token=SCREAMING_SNAKE_CASE__							,						pad_token=SCREAMING_SNAKE_CASE__							,						add_bos_token=SCREAMING_SNAKE_CASE__							,						add_eos_token=SCREAMING_SNAKE_CASE__							,						sp_model_kwargs=self.sp_model_kwargs							,						clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__							,						legacy=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__							,						)
            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'''		)
            lowercase			:   List[str]					   =		legacy
            lowercase			:   str					   =		vocab_file
            lowercase			:   Dict					   =		add_bos_token
            lowercase			:   List[Any]					   =		add_eos_token
            lowercase			:   Tuple					   =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.Load(SCREAMING_SNAKE_CASE__		)


      def __getstate__(							self		):
            lowercase			:   Optional[int]					   =		self.__dict__.copy()
            lowercase			:   Optional[int]					   =		None
            lowercase			:   List[str]					   =		self.sp_model.serialized_model_proto()
            return state


      def __setstate__(							self							,						SCREAMING_SNAKE_CASE__		):
            lowercase			:   Optional[Any]					   =		d
            lowercase			:   List[Any]					   =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)


      @property
      def        __lowerCamelCase			(							self		):
            return self.sp_model.get_piece_size()


      def        __lowerCamelCase			(							self		):
            lowercase			:   Optional[Any]					   =		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__		): i for i in range(self.vocab_size		)}
            vocab.update(self.added_tokens_encoder		)
            return vocab


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		):
            # 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:
                  lowercase			:   Union[str, Any]					   =		SPIECE_UNDERLINE + text.replace(SCREAMING_SNAKE_CASE__							,						''' '''		)
            return super().tokenize(SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
            if not self.legacy:
                  lowercase			:   Tuple					   =		text.startswith(SCREAMING_SNAKE_CASE__		)
                  if is_first:
                        lowercase			:   str					   =		text[1:]

            lowercase			:   List[str]					   =		self.sp_model.encode(SCREAMING_SNAKE_CASE__							,						out_type=SCREAMING_SNAKE_CASE__		)

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


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
            return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__		)


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
            lowercase			:   Optional[int]					   =		self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__		)
            return token


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
            lowercase			:   Optional[Any]					   =		[]
            lowercase			:   Optional[int]					   =		''''''
            lowercase			:   Union[str, Any]					   =		False
            for i, token in enumerate(SCREAMING_SNAKE_CASE__		):
                  # 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(SCREAMING_SNAKE_CASE__		) + token
                        lowercase			:   Any					   =		True
                        lowercase			:   str					   =		[]
                  else:
                        current_sub_tokens.append(SCREAMING_SNAKE_CASE__		)
                        lowercase			:   Dict					   =		False
            out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__		)
            return out_string


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = None		):
            if not os.path.isdir(SCREAMING_SNAKE_CASE__		):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
                  return
            lowercase			:   Optional[int]					   =		os.path.join(
                SCREAMING_SNAKE_CASE__							,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

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

            return (out_vocab_file,)


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=None		):
            lowercase			:   List[str]					   =		[self.bos_token_id] if self.add_bos_token else []
            lowercase			:   Optional[Any]					   =		[self.eos_token_id] if self.add_eos_token else []

            lowercase			:   int					   =		bos_token_id + token_ids_a + eos_token_id

            if token_ids_a is not None:
                  lowercase			:   List[Any]					   =		output + bos_token_id + token_ids_a + eos_token_id

            return output


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = False		):
            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=SCREAMING_SNAKE_CASE__							,						token_ids_a=SCREAMING_SNAKE_CASE__							,						already_has_special_tokens=SCREAMING_SNAKE_CASE__		)

            lowercase			:   int					   =		[1] if self.add_bos_token else []
            lowercase			:   Dict					   =		[1] if self.add_eos_token else []

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


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = None		):
            lowercase			:   str					   =		[self.bos_token_id] if self.add_bos_token else []
            lowercase			:   Optional[int]					   =		[self.eos_token_id] if self.add_eos_token else []

            lowercase			:   Optional[int]					   =		[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        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
            lowercase			:   Dict					   =		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...)'''		)

            lowercase			:   List[int]					   =		[]
            if len(conversation.past_user_inputs		) > 0:
                  if not conversation.past_user_inputs[0].startswith(SCREAMING_SNAKE_CASE__		) or E_SYS not in conversation.past_user_inputs[0]:
                        lowercase			:   str					   =		(
                            B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                        )
            elif not dialogue[0][1].startswith(SCREAMING_SNAKE_CASE__		) or E_SYS not in dialogue[0][1]:
                  lowercase			:   str					   =		(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=SCREAMING_SNAKE_CASE__		)
                    + [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=SCREAMING_SNAKE_CASE__		)
            return dialog_tokens





import math




def 			__lowercase	(						_UpperCamelCase     )    ->Union[str, Any]:







      """simple docstring"""

      lowercase			:   List[Any]					   =		0
      if n % 2 == 0:
            count += 1
            while n % 2 == 0:
                  lowercase			:   Any					   =		int(n / 2     )
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

      lowercase			:   Optional[int]					   =		3

      while i <= int(math.sqrt(_UpperCamelCase     )     ):
            if n % i == 0:
                  count += 1
                  while n % i == 0:
                        lowercase			:   Optional[Any]					   =		int(n / i     )
            lowercase			:   str					   =		i + 2

      # this condition checks the prime
      # number n is greater than 2

      if n > 2:
            count += 1
      return count


if __name__ == "__main__":
  __a   				=      51_24_21_83
  print(F'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
  print(F'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')

