






"""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

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''': 2048,
}
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    (					UpperCamelCase		):
							snake_case_  =				VOCAB_FILES_NAMES
							snake_case_  =				PRETRAINED_VOCAB_FILES_MAP
							snake_case_  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_  =				['input_ids', 'attention_mask']


							def __init__(      self   :    Any      ,     snake_case   :    Optional[int]      ,     snake_case   :    Optional[int]="<unk>"      ,     snake_case   :    Optional[Any]="<s>"      ,     snake_case   :    Tuple="</s>"      ,     snake_case   :    Tuple=None      ,     snake_case   :    Optional[Dict[str, Any]] = None      ,     snake_case   :    List[str]=True      ,     snake_case   :    str=False      ,     snake_case   :    List[str]=False      ,     snake_case   :    Dict=True      ,     **snake_case   :    int      ,     ):
													'''simple docstring'''





													A__    :	str			=	{} if sp_model_kwargs is None else sp_model_kwargs
													A__    :	str			=	AddedToken(snake_case      ,     lstrip=snake_case      ,     rstrip=snake_case				) if isinstance(snake_case      ,     snake_case				) else bos_token
													A__    :	List[str]			=	AddedToken(snake_case      ,     lstrip=snake_case      ,     rstrip=snake_case				) if isinstance(snake_case      ,     snake_case				) else eos_token
													A__    :	Tuple			=	AddedToken(snake_case      ,     lstrip=snake_case      ,     rstrip=snake_case				) if isinstance(snake_case      ,     snake_case				) else unk_token
													A__    :	int			=	AddedToken(snake_case      ,     lstrip=snake_case      ,     rstrip=snake_case				) if isinstance(snake_case      ,     snake_case				) else pad_token
													super().__init__(
													    bos_token=snake_case      ,     eos_token=snake_case      ,     unk_token=snake_case      ,     pad_token=snake_case      ,     add_bos_token=snake_case      ,     add_eos_token=snake_case      ,     sp_model_kwargs=self.sp_model_kwargs      ,     clean_up_tokenization_spaces=snake_case      ,     legacy=snake_case      ,     **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"""				)
													A__    :	List[Any]			=	legacy
													A__    :	Dict			=	vocab_file
													A__    :	Tuple			=	add_bos_token
													A__    :	Any			=	add_eos_token
													A__    :	int			=	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.Load(snake_case				)


							def __getstate__(      self   :    List[str]				):
													'''simple docstring'''





													A__    :	Union[str, Any]			=	self.__dict__.copy()
													A__    :	Any			=	None
													A__    :	Union[str, Any]			=	self.sp_model.serialized_model_proto()
													return state


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





													A__    :	str			=	d
													A__    :	List[str]			=	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)


							@property
							def     _UpperCamelCase							(      self   :    Any				):
													'''simple docstring'''





													return self.sp_model.get_piece_size()


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





													A__    :	Any			=	{self.convert_ids_to_tokens(snake_case				): i for i in range(self.vocab_size				)}
													vocab.update(self.added_tokens_encoder				)
													return vocab


							def     _UpperCamelCase							(      self   :    int      ,     snake_case   :    Union[str, Any]      ,     **snake_case   :    Tuple				):
													'''simple docstring'''





													if not self.legacy:
																			A__    :	int			=	SPIECE_UNDERLINE + text.replace(snake_case      ,     """ """				)
													return super().tokenize(snake_case      ,     **snake_case				)


							def     _UpperCamelCase							(      self   :    List[str]      ,     snake_case   :    List[str]				):
													'''simple docstring'''





													if not self.legacy:
																			A__    :	Any			=	text.startswith(snake_case				)
																			if is_first:
																									A__    :	Tuple			=	text[1:]

													A__    :	Optional[int]			=	self.sp_model.encode(snake_case      ,     out_type=snake_case				)

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


							def     _UpperCamelCase							(      self   :    Optional[int]      ,     snake_case   :    Union[str, Any]				):
													'''simple docstring'''





													return self.sp_model.piece_to_id(snake_case				)


							def     _UpperCamelCase							(      self   :    Any      ,     snake_case   :    int				):
													'''simple docstring'''





													A__    :	Optional[int]			=	self.sp_model.IdToPiece(snake_case				)
													return token


							def     _UpperCamelCase							(      self   :    Tuple      ,     snake_case   :    int				):
													'''simple docstring'''





													A__    :	Optional[Any]			=	[]
													A__    :	Any			=	""""""
													A__    :	Optional[Any]			=	False
													for i, token in enumerate(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(snake_case				) + token
																									A__    :	Tuple			=	True
																									A__    :	int			=	[]
																			else:
																									current_sub_tokens.append(snake_case				)
																									A__    :	List[str]			=	False
													out_string += self.sp_model.decode(snake_case				)
													return out_string


							def     _UpperCamelCase							(      self   :    Any      ,     snake_case   :    List[str]      ,     snake_case   :    Optional[str] = None				):
													'''simple docstring'''





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

													if os.path.abspath(self.vocab_file				) != os.path.abspath(snake_case				) and os.path.isfile(self.vocab_file				):
																			copyfile(self.vocab_file      ,     snake_case				)
													elif not os.path.isfile(self.vocab_file				):
																			with open(snake_case      ,     """wb"""				) as fi:
																									A__    :	List[Any]			=	self.sp_model.serialized_model_proto()
																									fi.write(snake_case				)

													return (out_vocab_file,)


							def     _UpperCamelCase							(      self   :    List[str]      ,     snake_case   :    Tuple      ,     snake_case   :    List[Any]=None				):
													'''simple docstring'''





													A__    :	int			=	[self.bos_token_id] if self.add_bos_token else []
													A__    :	Optional[Any]			=	[self.eos_token_id] if self.add_eos_token else []

													A__    :	List[str]			=	bos_token_id + token_ids_a + eos_token_id

													if token_ids_a is not None:
																			A__    :	int			=	output + bos_token_id + token_ids_a + eos_token_id

													return output


							def     _UpperCamelCase							(      self   :    Any      ,     snake_case   :    List[int]      ,     snake_case   :    Optional[List[int]] = None      ,     snake_case   :    bool = False				):
													'''simple docstring'''





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

													A__    :	Union[str, Any]			=	[1] if self.add_bos_token else []
													A__    :	Optional[Any]			=	[1] if self.add_eos_token else []

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


							def     _UpperCamelCase							(      self   :    Tuple      ,     snake_case   :    List[int]      ,     snake_case   :    Optional[List[int]] = None				):
													'''simple docstring'''





													A__    :	str			=	[self.bos_token_id] if self.add_bos_token else []
													A__    :	int			=	[self.eos_token_id] if self.add_eos_token else []

													A__    :	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     _UpperCamelCase							(      self   :    Dict      ,     snake_case   :    "Conversation"				):
													'''simple docstring'''





													A__    :	Any			=	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...)"""				)

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














"""simple docstring"""



from itertools import combinations, product




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int   )		->bool:
						if xa == ya == 0 or xa == ya == 0:
												return False
						A__    :	List[str]			=	xa * xa + ya * ya
						A__    :	List[Any]			=	xa * xa + ya * ya
						A__    :	Optional[int]			=	(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
						return (
						    a_square + b_square == c_square
						    or a_square + c_square == b_square
						    or b_square + c_square == a_square
						)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int = 5_0   )		->int:
						return sum(
						    1
						    for pta, pta in combinations(product(range(limit + 1   ),    repeat=2   ),    2   )
						    if is_right(*UpperCAmelCase__,    *UpperCAmelCase__   )   )


if __name__ == "__main__":
						print(F'{solution() = }')






