



'''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:
		_UpperCamelCase  						=			None

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

_UpperCamelCase						, _UpperCamelCase  						=			"""[INST]""", """[/INST]"""
_UpperCamelCase						, _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\
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      lowerCamelCase__     (     lowercase_				):






	'''simple docstring'''

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




	def __init__(  self		:  int   ,		__A		:  Union[str, Any]=None   ,		__A		:  int=None   ,		__A		:  Optional[int]=False   ,		__A		:  Dict="<unk>"   ,		__A		:  Union[str, Any]="<s>"   ,		__A		:  Optional[int]="</s>"   ,		__A		:  List[str]=True   ,		__A		:  Optional[Any]=False   ,		**__A		:  str   ,		)     ->			Optional[Any]:




								'''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_   ,		)
								lowerCAmelCase__  =   add_bos_token
								lowerCAmelCase__  =   add_eos_token
								self.update_post_processor()

								lowerCAmelCase__  =   vocab_file
								lowerCAmelCase__  =   False if not self.vocab_file else True




	def      lowercase__							(  self		:  int					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   self.bos_token
								lowerCAmelCase__  =   self.bos_token_id

								lowerCAmelCase__  =   self.eos_token
								lowerCAmelCase__  =   self.eos_token_id

								lowerCAmelCase__  =   f'''{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'''
								lowerCAmelCase__  =   f'''{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'''

								lowerCAmelCase__  =   []
								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__  =   processors.TemplateProcessing(
								    single=UpperCAmelCase_   ,		pair=UpperCAmelCase_   ,		special_tokens=UpperCAmelCase_					)




	@property
	def      lowercase__							(  self		:  List[str]					)     ->			Optional[Any]:




								'''simple docstring'''





								return self._add_eos_token




	@property
	def      lowercase__							(  self		:  List[str]					)     ->			List[Any]:




								'''simple docstring'''





								return self._add_bos_token




	@add_eos_token.setter
	def      lowercase__							(  self		:  Any   ,		__A		:  Dict					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   value
								self.update_post_processor()




	@add_bos_token.setter
	def      lowercase__							(  self		:  List[str]   ,		__A		:  List[Any]					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   value
								self.update_post_processor()




	def      lowercase__							(  self		:  Dict   ,		__A		:  Tuple   ,		__A		:  List[Any] = None					)     ->			Tuple[str]:




								'''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
								lowerCAmelCase__  =   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      lowercase__							(  self		:  List[str]   ,		__A		:  Dict					)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   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__  =   []
								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]:
																						lowerCAmelCase__  =   (
																						    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]:
															lowerCAmelCase__  =   (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













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase					=     {
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""LEDTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     [
      """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LEDForConditionalGeneration""",
      """LEDForQuestionAnswering""",
      """LEDForSequenceClassification""",
      """LEDModel""",
      """LEDPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
  from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
  from .tokenization_led import LEDTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_led_fast import LEDTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_led import (
        LED_PRETRAINED_MODEL_ARCHIVE_LIST,
        LEDForConditionalGeneration,
        LEDForQuestionAnswering,
        LEDForSequenceClassification,
        LEDModel,
        LEDPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
  import sys

  UpperCamelCase					=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






