





'''simple docstring'''



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

_SCREAMING_SNAKE_CASE    =			logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE    =			{
    '''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''',
    },
}
_SCREAMING_SNAKE_CASE    =			{
    '''hf-internal-testing/llama-tokenizer''': 2_0_4_8,
}
_SCREAMING_SNAKE_CASE    =			'''▁'''

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

# fmt: off
_SCREAMING_SNAKE_CASE    =			'''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				(					lowerCAmelCase__  ):







	'''simple docstring'''

	a			: Union[str, Any]      =				VOCAB_FILES_NAMES
	a			: Union[str, Any]      =				PRETRAINED_VOCAB_FILES_MAP
	a			: Dict      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a			: Optional[int]      =				["input_ids", "attention_mask"]


	def __init__(self   ,_lowerCamelCase   ,_lowerCamelCase="<unk>"   ,_lowerCamelCase="<s>"   ,_lowerCamelCase="</s>"   ,_lowerCamelCase=None   ,_lowerCamelCase = None   ,_lowerCamelCase=True   ,_lowerCamelCase=False   ,_lowerCamelCase=False   ,_lowerCamelCase=True   ,**_lowerCamelCase   ,)							->							Dict:







				'''simple docstring'''





				__lowercase   =							{} if sp_model_kwargs is None else sp_model_kwargs
				__lowercase   =							AddedToken(_lowerCamelCase   ,lstrip=_lowerCamelCase   ,rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase   ,_lowerCamelCase       ) else bos_token
				__lowercase   =							AddedToken(_lowerCamelCase   ,lstrip=_lowerCamelCase   ,rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase   ,_lowerCamelCase       ) else eos_token
				__lowercase   =							AddedToken(_lowerCamelCase   ,lstrip=_lowerCamelCase   ,rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase   ,_lowerCamelCase       ) else unk_token
				__lowercase   =							AddedToken(_lowerCamelCase   ,lstrip=_lowerCamelCase   ,rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase   ,_lowerCamelCase       ) else pad_token
				super().__init__(
				    bos_token=_lowerCamelCase   ,eos_token=_lowerCamelCase   ,unk_token=_lowerCamelCase   ,pad_token=_lowerCamelCase   ,add_bos_token=_lowerCamelCase   ,add_eos_token=_lowerCamelCase   ,sp_model_kwargs=self.sp_model_kwargs   ,clean_up_tokenization_spaces=_lowerCamelCase   ,legacy=_lowerCamelCase   ,**_lowerCamelCase   ,)
				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   =							legacy
				__lowercase   =							vocab_file
				__lowercase   =							add_bos_token
				__lowercase   =							add_eos_token
				__lowercase   =							spm.SentencePieceProcessor(**self.sp_model_kwargs       )
				self.sp_model.Load(_lowerCamelCase       )


	def __getstate__(self       )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase   =							self.__dict__.copy()
				__lowercase   =							None
				__lowercase   =							self.sp_model.serialized_model_proto()
				return state


	def __setstate__(self   ,_lowerCamelCase       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							d
				__lowercase   =							spm.SentencePieceProcessor(**self.sp_model_kwargs       )
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )


	@property
	def   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''simple docstring'''





				return self.sp_model.get_piece_size()


	def   _UpperCAmelCase   (self       )							->							List[Any]:







				'''simple docstring'''





				__lowercase   =							{self.convert_ids_to_tokens(_lowerCamelCase       ): i for i in range(self.vocab_size       )}
				vocab.update(self.added_tokens_encoder       )
				return vocab


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,**_lowerCamelCase       )							->							List[str]:







				'''simple docstring'''





				if not self.legacy:
							__lowercase   =							SPIECE_UNDERLINE + text.replace(_lowerCamelCase   ,''' '''       )
				return super().tokenize(_lowerCamelCase   ,**_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				if not self.legacy:
							__lowercase   =							text.startswith(_lowerCamelCase       )
							if is_first:
										__lowercase   =							text[1:]

				__lowercase   =							self.sp_model.encode(_lowerCamelCase   ,out_type=_lowerCamelCase       )

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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				return self.sp_model.piece_to_id(_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				__lowercase   =							self.sp_model.IdToPiece(_lowerCamelCase       )
				return token


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							[]
				__lowercase   =							''''''
				__lowercase   =							False
				for i, token in enumerate(_lowerCamelCase       ):
							# 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(_lowerCamelCase       ) + token
										__lowercase   =							True
										__lowercase   =							[]
							else:
										current_sub_tokens.append(_lowerCamelCase       )
										__lowercase   =							False
				out_string += self.sp_model.decode(_lowerCamelCase       )
				return out_string


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase = None       )							->							Tuple[str]:







				'''simple docstring'''





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

				if os.path.abspath(self.vocab_file       ) != os.path.abspath(_lowerCamelCase       ) and os.path.isfile(self.vocab_file       ):
							copyfile(self.vocab_file   ,_lowerCamelCase       )
				elif not os.path.isfile(self.vocab_file       ):
							with open(_lowerCamelCase   ,'''wb'''       ) as fi:
										__lowercase   =							self.sp_model.serialized_model_proto()
										fi.write(_lowerCamelCase       )

				return (out_vocab_file,)


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase=None       )							->							Any:







				'''simple docstring'''





				__lowercase   =							[self.bos_token_id] if self.add_bos_token else []
				__lowercase   =							[self.eos_token_id] if self.add_eos_token else []

				__lowercase   =							bos_token_id + token_ids_a + eos_token_id

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

				return output


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase = None   ,_lowerCamelCase = False       )							->							List[int]:







				'''simple docstring'''





				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=_lowerCamelCase   ,token_ids_a=_lowerCamelCase   ,already_has_special_tokens=_lowerCamelCase       )

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

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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase = None       )							->							List[int]:







				'''simple docstring'''





				__lowercase   =							[self.bos_token_id] if self.add_bos_token else []
				__lowercase   =							[self.eos_token_id] if self.add_eos_token else []

				__lowercase   =							[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   ,_lowerCamelCase       )							->							List[int]:







				'''simple docstring'''





				__lowercase   =							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   =							[]
				if len(conversation.past_user_inputs       ) > 0:
							if not conversation.past_user_inputs[0].startswith(_lowerCamelCase       ) or E_SYS not in conversation.past_user_inputs[0]:
										__lowercase   =							(
										    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
										)
				elif not dialogue[0][1].startswith(_lowerCamelCase       ) or E_SYS not in dialogue[0][1]:
							__lowercase   =							(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=_lowerCamelCase       )
				        + [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=_lowerCamelCase       )
				return dialog_tokens













"""simple docstring"""







from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap


@total_ordering
class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       word
												lowercase__				:      Any=       count




						def __eq__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count == other.count




						def __lt__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count < other.count





def 						lowercase__(A					,		A						)				->list[str]:




						"""simple docstring"""



						lowercase__				:      Heap[WordCount]=       Heap()
						lowercase__				:      str=       Counter(A						)
						heap.build_max_heap(
						    [WordCount(A					,		A						) for word, count in count_by_word.items()]						)
						return [heap.extract_max().word for _ in range(min(A					,		len(A						)						)						)]


if __name__ == "__main__":
			import doctest

			doctest.testmod()






