






import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

require_version('''tokenizers>=0.13.3''')

if is_sentencepiece_available():
							from .tokenization_llama import LlamaTokenizer
else:
							A_			:Optional[Any]	    =		None

A_			:List[Any]	    =		logging.get_logger(__name__)
A_			:Dict	    =		{'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

A_    ,A_			:Tuple	    =		'''[INST]''', '''[/INST]'''
A_    ,A_			:int	    =		'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
A_			:Optional[int]	    =		'''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 							__A	(     a					):





				"""simple docstring"""







				UpperCamelCase__					: Optional[int]     					=VOCAB_FILES_NAMES
				UpperCamelCase__					: List[Any]     					=LlamaTokenizer
				UpperCamelCase__					: List[Any]     					="""left"""
				UpperCamelCase__					: Optional[int]     					=["""input_ids""", """attention_mask"""]


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




									"""simple docstring"""

									super().__init__(
									    vocab_file=lowerCamelCase__				,    tokenizer_file=lowerCamelCase__				,    clean_up_tokenization_spaces=lowerCamelCase__				,    unk_token=lowerCamelCase__				,    bos_token=lowerCamelCase__				,    eos_token=lowerCamelCase__				,    **lowerCamelCase__				,    )
									__UpperCamelCase :   List[str]									=add_bos_token
									__UpperCamelCase :   Optional[Any]									=add_eos_token
									self.update_post_processor()

									__UpperCamelCase :   Any									=vocab_file
									__UpperCamelCase :   Optional[int]									=False if not self.vocab_file else True


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=self.bos_token
									__UpperCamelCase :   Dict									=self.bos_token_id

									__UpperCamelCase :   Optional[Any]									=self.eos_token
									__UpperCamelCase :   Any									=self.eos_token_id

									__UpperCamelCase :   Optional[int]									=f'{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'
									__UpperCamelCase :   str									=f'{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'

									__UpperCamelCase :   Union[str, Any]									=[]
									if self.add_bos_token:
														special_tokens.append((bos, bos_token_id)       )
									if self.add_eos_token:
														special_tokens.append((eos, eos_token_id)       )
									__UpperCamelCase :   Union[str, Any]									=processors.TemplateProcessing(
									    single=lowerCamelCase__				,    pair=lowerCamelCase__				,    special_tokens=lowerCamelCase__       )


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									return self._add_eos_token


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									return self._add_bos_token


				@add_eos_token.setter
				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   int									=value
									self.update_post_processor()


				@add_bos_token.setter
				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   List[Any]									=value
									self.update_post_processor()


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__ = None       ):




									"""simple docstring"""

									if not self.can_save_slow_tokenizer:
														raise ValueError(
														    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
														    'tokenizer.'       )

									if not os.path.isdir(lowerCamelCase__       ):
														logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
														return
									__UpperCamelCase :   str									=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__       ):
														copyfile(self.vocab_file				,    lowerCamelCase__       )

									return (out_vocab_file,)







				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Tuple									=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...)'       )

									__UpperCamelCase :   Dict									=[]
									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]:
																			__UpperCamelCase :   int									=(
																			    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]:
														__UpperCamelCase :   Any									=(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









from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_			:Union[str, Any]	    =		load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
A_    ,A_    ,A_    ,A_			:Tuple	    =		train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

A_			:Any	    =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_			:Optional[Any]	    =		[[1, 2, 1, 4], [2, 3, 4, 5]]

A_			:Union[str, Any]	    =		knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)

