



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:
						UpperCAmelCase_			      =  None

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

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

# fmt: off
UpperCAmelCase_			      =  '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 		__SCREAMING_SNAKE_CASE      (				SCREAMING_SNAKE_CASE_      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_ 				=  LlamaTokenizer
				SCREAMING_SNAKE_CASE_ 				=  'left'
				SCREAMING_SNAKE_CASE_ 				=  ['input_ids', 'attention_mask']







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






					"""simple docstring"""

					super().__init__(
					    vocab_file=UpperCamelCase__						,	tokenizer_file=UpperCamelCase__						,	clean_up_tokenization_spaces=UpperCamelCase__						,	unk_token=UpperCamelCase__						,	bos_token=UpperCamelCase__						,	eos_token=UpperCamelCase__						,	**UpperCamelCase__						,	)
					_snake_case					:							Optional[Any]					     =   add_bos_token
					_snake_case					:							Dict					     =   add_eos_token
					self.update_post_processor()

					_snake_case					:							Optional[int]					     =   vocab_file
					_snake_case					:							Union[str, Any]					     =   False if not self.vocab_file else True







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							int					     =   self.bos_token
					_snake_case					:							Dict					     =   self.bos_token_id

					_snake_case					:							Any					     =   self.eos_token
					_snake_case					:							Any					     =   self.eos_token_id

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

					_snake_case					:							str					     =   []
					if self.add_bos_token:
						special_tokens.append((bos, bos_token_id)      )
					if self.add_eos_token:
						special_tokens.append((eos, eos_token_id)      )
					_snake_case					:							Tuple					     =   processors.TemplateProcessing(
					    single=UpperCamelCase__						,	pair=UpperCamelCase__						,	special_tokens=UpperCamelCase__      )







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					return self._add_eos_token







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					return self._add_bos_token







				@add_eos_token.setter
				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Tuple					     =   value
					self.update_post_processor()







				@add_bos_token.setter
				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = 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(UpperCamelCase__      ):
						logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
						return
					_snake_case					:							Dict					     =   os.path.join(
					    UpperCamelCase__						,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

					if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase__      ):
						copyfile(self.vocab_file						,	UpperCamelCase__      )

					return (out_vocab_file,)







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[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...)"""      )

					_snake_case					:							str					     =   []
					if len(conversation.past_user_inputs      ) > 0:
						if not conversation.past_user_inputs[0].startswith(UpperCamelCase__      ) or E_SYS not in conversation.past_user_inputs[0]:
							_snake_case					:							Union[str, Any]					     =   (
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
					elif not dialogue[0][1].startswith(UpperCamelCase__      ) or E_SYS not in dialogue[0][1]:
						_snake_case					:							List[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=UpperCamelCase__      )
					        + [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=UpperCamelCase__      )
					return dialog_tokens







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Tuple							 =      [
					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaubertForMultipleChoice',
					    'FlaubertForQuestionAnswering',
					    'FlaubertForQuestionAnsweringSimple',
					    'FlaubertForSequenceClassification',
					    'FlaubertForTokenClassification',
					    'FlaubertModel',
					    'FlaubertWithLMHeadModel',
					    'FlaubertPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Dict							 =      [
					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFFlaubertForMultipleChoice',
					    'TFFlaubertForQuestionAnsweringSimple',
					    'TFFlaubertForSequenceClassification',
					    'TFFlaubertForTokenClassification',
					    'TFFlaubertModel',
					    'TFFlaubertPreTrainedModel',
					    'TFFlaubertWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)