


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:
							SCREAMING_SNAKE_CASE__       			=				None

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

SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				"""[INST]""", """[/INST]"""
SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
SCREAMING_SNAKE_CASE__       			=				"""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     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				VOCAB_FILES_NAMES
							lowerCAmelCase__          =				LlamaTokenizer
							lowerCAmelCase__          =				"left"
							lowerCAmelCase__          =				["input_ids", "attention_mask"]




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






								'''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						,       )
								lowercase_			 = add_bos_token
								lowercase_			 = add_eos_token
								self.update_post_processor()

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




							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								lowercase_			 = self.bos_token
								lowercase_			 = self.bos_token_id

								lowercase_			 = self.eos_token
								lowercase_			 = self.eos_token_id

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

								lowercase_			 = []
								if self.add_bos_token:
									special_tokens.append((bos, bos_token_id)  )
								if self.add_eos_token:
									special_tokens.append((eos, eos_token_id)  )
								lowercase_			 = processors.TemplateProcessing(
								    single=UpperCAmelCase						,       pair=UpperCAmelCase						,       special_tokens=UpperCAmelCase  )




							@property
							def 				A__      (  self  )     ->       Optional[Any]:






								'''simple docstring'''






								return self._add_eos_token




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






								'''simple docstring'''






								return self._add_bos_token




							@add_eos_token.setter
							def 				A__      (  self						,       UpperCAmelCase  )     ->       List[Any]:






								'''simple docstring'''






								lowercase_			 = value
								self.update_post_processor()




							@add_bos_token.setter
							def 				A__      (  self						,       UpperCAmelCase  )     ->       Any:






								'''simple docstring'''






								lowercase_			 = value
								self.update_post_processor()




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = 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
								lowercase_			 = 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 				A__      (  self						,       UpperCAmelCase  )     ->       List[Any]:






								'''simple docstring'''






								lowercase_			 = 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...)"  )

								lowercase_			 = []
								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]:
										lowercase_			 = (
										    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]:
									lowercase_			 = (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_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       			=				{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       			=				[
							    """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MarkupLMForQuestionAnswering""",
							    """MarkupLMForSequenceClassification""",
							    """MarkupLMForTokenClassification""",
							    """MarkupLMModel""",
							    """MarkupLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE__       			=				_LazyModule(__name__, globals()["""__file__"""], _import_structure)

