import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

UpperCamelCase													=		logging.get_logger(__name__)

UpperCamelCase													=		{'''vocab_file''': '''tokenizer.model'''}

UpperCamelCase													=		{
    '''vocab_file''': {
        '''hf-internal-testing/llama-tokenizer''': '''https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model''',
    },
    '''tokenizer_file''': {
        '''hf-internal-testing/llama-tokenizer''': '''https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json''',
    },
}
UpperCamelCase													=		{
    '''hf-internal-testing/llama-tokenizer''': 2048,
}
UpperCamelCase													=		'''▁'''

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 						snake_case_ (	__A ):
   __A			:	List[Any]        =       VOCAB_FILES_NAMES
   __A			:	Dict        =       PRETRAINED_VOCAB_FILES_MAP
   __A			:	Tuple        =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A			:	Optional[Any]        =       ["input_ids", "attention_mask"]






   def __init__(   self       :  str			,		lowercase_       :  str			,		lowercase_       :  str="<unk>"			,		lowercase_       :  List[str]="<s>"			,		lowercase_       :  Union[str, Any]="</s>"			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Optional[Dict[str, Any]] = None			,		lowercase_       :  Optional[Any]=True			,		lowercase_       :  List[Any]=False			,		lowercase_       :  Dict=False			,		lowercase_       :  int=True			,		**lowercase_       :  Tuple			,		)      ->		Dict:
       lowercase__				:     List[Any]					      =       {} if sp_model_kwargs is None else sp_model_kwargs
       lowercase__				:     int					      =       AddedToken(lowercase_			,		lstrip=lowercase_			,		rstrip=lowercase_						) if isinstance(lowercase_			,		lowercase_						) else bos_token
       lowercase__				:     List[Any]					      =       AddedToken(lowercase_			,		lstrip=lowercase_			,		rstrip=lowercase_						) if isinstance(lowercase_			,		lowercase_						) else eos_token
       lowercase__				:     str					      =       AddedToken(lowercase_			,		lstrip=lowercase_			,		rstrip=lowercase_						) if isinstance(lowercase_			,		lowercase_						) else unk_token
       lowercase__				:     Dict					      =       AddedToken(lowercase_			,		lstrip=lowercase_			,		rstrip=lowercase_						) if isinstance(lowercase_			,		lowercase_						) else pad_token
       super().__init__(
           bos_token=lowercase_			,		eos_token=lowercase_			,		unk_token=lowercase_			,		pad_token=lowercase_			,		add_bos_token=lowercase_			,		add_eos_token=lowercase_			,		sp_model_kwargs=self.sp_model_kwargs			,		clean_up_tokenization_spaces=lowercase_			,		legacy=lowercase_			,		**lowercase_			,		)
       if legacy:
           logger.warning_once(
               F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
               " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"						)
       lowercase__				:     List[str]					      =       legacy
       lowercase__				:     Any					      =       vocab_file
       lowercase__				:     Union[str, Any]					      =       add_bos_token
       lowercase__				:     Optional[Any]					      =       add_eos_token
       lowercase__				:     Tuple					      =       spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(lowercase_						)






   def __getstate__(   self       :  List[Any]						)      ->		Any:
       lowercase__				:     List[Any]					      =       self.__dict__.copy()
       lowercase__				:     str					      =       None
       lowercase__				:     int					      =       self.sp_model.serialized_model_proto()
       return state






   def __setstate__(   self       :  Dict			,		lowercase_       :  Optional[int]						)      ->		int:
       lowercase__				:     int					      =       d
       lowercase__				:     Dict					      =       spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)






   @property
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		str:
       return self.sp_model.get_piece_size()






   def    __UpperCamelCase  (   self       :  List[str]						)      ->		Union[str, Any]:
       lowercase__				:     List[str]					      =       {self.convert_ids_to_tokens(lowercase_						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab






   def    __UpperCamelCase  (   self       :  int			,		lowercase_       :  Tuple			,		**lowercase_       :  Optional[Any]						)      ->		List[str]:
       # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
       # the beginning of the text
       if not self.legacy:
           lowercase__				:     Tuple					      =       SPIECE_UNDERLINE + text.replace(lowercase_			,		" "						)
       return super().tokenize(lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  List[Any]						)      ->		Tuple:
       if not self.legacy:
           lowercase__				:     Dict					      =       text.startswith(lowercase_						)
           if is_first:
               lowercase__				:     List[Any]					      =       text[1:]

       lowercase__				:     Tuple					      =       self.sp_model.encode(lowercase_			,		out_type=lowercase_						)

       if not self.legacy and not is_first and not text.startswith(" "						) and tokens[0].startswith(lowercase_						):
           lowercase__				:     Tuple					      =       ([tokens[0][1:]] if len(tokens[0]						) > 1 else []) + tokens[1:]
       return tokens






   def    __UpperCamelCase  (   self       :  int			,		lowercase_       :  Optional[int]						)      ->		Any:
       return self.sp_model.piece_to_id(lowercase_						)






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  Optional[int]						)      ->		Union[str, Any]:
       lowercase__				:     Union[str, Any]					      =       self.sp_model.IdToPiece(lowercase_						)
       return token






   def    __UpperCamelCase  (   self       :  Optional[Any]			,		lowercase_       :  int						)      ->		Dict:
       lowercase__				:     Any					      =       []
       lowercase__				:     Union[str, Any]					      =       ""
       lowercase__				:     Any					      =       False
       for i, token in enumerate(lowercase_						):
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               if not prev_is_special and i != 0:
                   out_string += " "
               out_string += self.sp_model.decode(lowercase_						) + token
               lowercase__				:     Optional[int]					      =       True
               lowercase__				:     List[str]					      =       []
           else:
               current_sub_tokens.append(lowercase_						)
               lowercase__				:     Any					      =       False
       out_string += self.sp_model.decode(lowercase_						)
       return out_string






   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  List[str]			,		lowercase_       :  Optional[str] = None						)      ->		Tuple[str]:
       if not os.path.isdir(lowercase_						):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
           return
       lowercase__				:     List[str]					      =       os.path.join(
           lowercase_			,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase_						) and os.path.isfile(self.vocab_file						):
           copyfile(self.vocab_file			,		lowercase_						)
       elif not os.path.isfile(self.vocab_file						):
           with open(lowercase_			,		"wb"						) as fi:
               lowercase__				:     Optional[Any]					      =       self.sp_model.serialized_model_proto()
               fi.write(lowercase_						)

       return (out_vocab_file,)






   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  Optional[Any]			,		lowercase_       :  str=None						)      ->		Optional[int]:
       lowercase__				:     int					      =       [self.bos_token_id] if self.add_bos_token else []
       lowercase__				:     int					      =       [self.eos_token_id] if self.add_eos_token else []

       lowercase__				:     Optional[Any]					      =       bos_token_id + token_ids_a + eos_token_id

       if token_ids_a is not None:
           lowercase__				:     str					      =       output + bos_token_id + token_ids_a + eos_token_id

       return output






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  List[int]			,		lowercase_       :  Optional[List[int]] = None			,		lowercase_       :  bool = False						)      ->		List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=lowercase_			,		token_ids_a=lowercase_			,		already_has_special_tokens=lowercase_						)

       lowercase__				:     List[Any]					      =       [1] if self.add_bos_token else []
       lowercase__				:     Dict					      =       [1] if self.add_eos_token else []

       if token_ids_a is None:
           return bos_token_id + ([0] * len(lowercase_						)) + eos_token_id
       return (
           bos_token_id
           + ([0] * len(lowercase_						))
           + eos_token_id
           + bos_token_id
           + ([0] * len(lowercase_						))
           + eos_token_id
       )






   def    __UpperCamelCase  (   self       :  Dict			,		lowercase_       :  List[int]			,		lowercase_       :  Optional[List[int]] = None						)      ->		List[int]:
       lowercase__				:     List[str]					      =       [self.bos_token_id] if self.add_bos_token else []
       lowercase__				:     str					      =       [self.eos_token_id] if self.add_eos_token else []

       lowercase__				:     List[Any]					      =       [0] * len(bos_token_id + token_ids_a + eos_token_id						)

       if token_ids_a is not None:
           output += [1] * len(bos_token_id + token_ids_a + eos_token_id						)

       return output







   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  "Conversation"						)      ->		List[int]:
       lowercase__				:     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...)"						)

       lowercase__				:     List[int]					      =       []
       if len(conversation.past_user_inputs						) > 0:
           if not conversation.past_user_inputs[0].startswith(lowercase_						) or E_SYS not in conversation.past_user_inputs[0]:
               lowercase__				:     Optional[int]					      =       (
                   B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
               )
       elif not dialogue[0][1].startswith(lowercase_						) or E_SYS not in dialogue[0][1]:
           lowercase__				:     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=lowercase_						)
               + [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=lowercase_						)
       return dialog_tokens




from typing import TYPE_CHECKING

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


UpperCamelCase													=		{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''MobileNetV1FeatureExtractor''']
      UpperCamelCase													=		['''MobileNetV1ImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MobileNetV1ForImageClassification''',
          '''MobileNetV1Model''',
          '''MobileNetV1PreTrainedModel''',
          '''load_tf_weights_in_mobilenet_v1''',
      ]


if TYPE_CHECKING:
      from .configuration_mobilenet_va import (
          MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileNetVaConfig,
          MobileNetVaOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
            from .image_processing_mobilenet_va import MobileNetVaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilenet_va import (
                MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileNetVaForImageClassification,
                MobileNetVaModel,
                MobileNetVaPreTrainedModel,
                load_tf_weights_in_mobilenet_va,
            )


else:
      import sys

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



