"""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:
   __magic_name__    				=					None

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

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

# fmt: off
__magic_name__    				=					'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     (    lowercase_	):







 """simple docstring"""


 a_       :	int  			=VOCAB_FILES_NAMES
 a_       :	List[str]  			=LlamaTokenizer
 a_       :	Tuple  			="""left"""
 a_       :	int  			=["""input_ids""", """attention_mask"""]


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




    '''simple docstring'''






    super().__init__(
        vocab_file=_snake_case					,   tokenizer_file=_snake_case					,   clean_up_tokenization_spaces=_snake_case					,   unk_token=_snake_case					,   bos_token=_snake_case					,   eos_token=_snake_case					,   **_snake_case					,   )
    a__     				=		add_bos_token
    a__     				=		add_eos_token
    self.update_post_processor()

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


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Tuple:




    '''simple docstring'''






    a__     				=		self.bos_token
    a__     				=		self.bos_token_id

    a__     				=		self.eos_token
    a__     				=		self.eos_token_id

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

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


 @property
 def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Dict:




    '''simple docstring'''






    return self._add_eos_token


 @property
 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Any:




    '''simple docstring'''






    return self._add_bos_token


 @add_eos_token.setter
 def        _lowerCAmelCase				(   self		:      Union[str, Any]					,   _snake_case		:      int				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		value
    self.update_post_processor()


 @add_bos_token.setter
 def        _lowerCAmelCase				(   self		:      Optional[Any]					,   _snake_case		:      Optional[int]				)				->			Dict:




    '''simple docstring'''






    a__     				=		value
    self.update_post_processor()


 def        _lowerCAmelCase				(   self		:      Optional[Any]					,   _snake_case		:      str					,   _snake_case		:      Optional[str] = None				)				->			Tuple:




    '''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(_snake_case				):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
       return
    a__     				=		os.path.join(
        _snake_case					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

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

    return (out_vocab_file,)



 def        _lowerCAmelCase				(   self		:      str					,   _snake_case		:      "Conversation"				)				->			Tuple:




    '''simple docstring'''






    a__     				=		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...)'				)

    a__     				=		[]
    if len(conversation.past_user_inputs				) > 0:
       if not conversation.past_user_inputs[0].startswith(_snake_case				) or E_SYS not in conversation.past_user_inputs[0]:
          a__     				=		(
              B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
          )
    elif not dialogue[0][1].startswith(_snake_case				) or E_SYS not in dialogue[0][1]:
       a__     				=		(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=_snake_case				)
            + [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=_snake_case				)
    return dialog_tokens



"""simple docstring"""



from typing import TYPE_CHECKING

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


__magic_name__    				=					{
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DonutSwinModel",
         "DonutSwinPreTrainedModel",
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					["DonutFeatureExtractor"]
     __magic_name__    				=					["DonutImageProcessor"]


if TYPE_CHECKING:
     from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
     from .processing_donut import DonutProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_donut_swin import (
              DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              DonutSwinModel,
              DonutSwinPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_donut import DonutFeatureExtractor
          from .image_processing_donut import DonutImageProcessor

else:
     import sys

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


