
"""simple docstring"""

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				=					None

_A				=					logging.get_logger(__name__)
_A				=					{'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

_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\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'






class 			_lowercase  (							snake_case_						):
	lowercase_								=      VOCAB_FILES_NAMES
	lowercase_								=      LlamaTokenizer
	lowercase_								=      'left'
	lowercase_								=      ['input_ids', 'attention_mask']




	def __init__(					self						,					UpperCAmelCase_=None						,					UpperCAmelCase_=None						,					UpperCAmelCase_=False						,					UpperCAmelCase_="<unk>"						,					UpperCAmelCase_="<s>"						,					UpperCAmelCase_="</s>"						,					UpperCAmelCase_=True						,					UpperCAmelCase_=False						,					**UpperCAmelCase_						,					)						->				Optional[int]:
			super().__init__(
			    vocab_file=__lowerCamelCase						,					tokenizer_file=__lowerCamelCase						,					clean_up_tokenization_spaces=__lowerCamelCase						,					unk_token=__lowerCamelCase						,					bos_token=__lowerCamelCase						,					eos_token=__lowerCamelCase						,					**__lowerCamelCase						,					)
			lowerCamelCase	:     Optional[Any]            =       add_bos_token
			lowerCamelCase	:     List[Any]            =       add_eos_token
			self.update_post_processor()

			lowerCamelCase	:     Optional[Any]            =       vocab_file
			lowerCamelCase	:     Tuple            =       False if not self.vocab_file else True




	def      _UpperCamelCase       (					self   )						->				List[str]:
			lowerCamelCase	:     int            =       self.bos_token
			lowerCamelCase	:     List[Any]            =       self.bos_token_id

			lowerCamelCase	:     Optional[Any]            =       self.eos_token
			lowerCamelCase	:     Union[str, Any]            =       self.eos_token_id

			lowerCamelCase	:     List[Any]            =       F"""{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"""
			lowerCamelCase	:     Dict            =       F"""{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"""

			lowerCamelCase	:     int            =       []
			if self.add_bos_token:
					special_tokens.append((bos, bos_token_id)   )
			if self.add_eos_token:
					special_tokens.append((eos, eos_token_id)   )
			lowerCamelCase	:     Optional[Any]            =       processors.TemplateProcessing(
			    single=__lowerCamelCase						,					pair=__lowerCamelCase						,					special_tokens=__lowerCamelCase   )




	@property
	def      _UpperCamelCase       (					self   )						->				Dict:
			return self._add_eos_token




	@property
	def      _UpperCamelCase       (					self   )						->				Union[str, Any]:
			return self._add_bos_token




	@add_eos_token.setter
	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				str:
			lowerCamelCase	:     Dict            =       value
			self.update_post_processor()




	@add_bos_token.setter
	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Tuple:
			lowerCamelCase	:     Optional[int]            =       value
			self.update_post_processor()




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_ = None   )						->				Tuple[str]:
			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
			lowerCamelCase	:     Dict            =       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      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Union[str, Any]:
			lowerCamelCase	:     Optional[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...)'   )

			lowerCamelCase	:     Tuple            =       []
			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]:
							lowerCamelCase	:     Any            =       (
							    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]:
					lowerCamelCase	:     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














"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel

@require_torch
@require_sentencepiece
@require_tokenizers
class     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     device=__lowerCamelCase    ,     dtype=torch.float    ,     )
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]    ,     __lowerCamelCase    ,     atol=1e-4						)						)



