




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

SCREAMING_SNAKE_CASE_						:					Dict			   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       {"""vocab_file""": """tokenizer.model"""}

SCREAMING_SNAKE_CASE_						:					Union[str, Any]			   =       {
    """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""",
    },
}
SCREAMING_SNAKE_CASE_						:					str			   =       {
    """hf-internal-testing/llama-tokenizer""": 20_48,
}
SCREAMING_SNAKE_CASE_						:					Any			   =       """▁"""

SCREAMING_SNAKE_CASE_						:					List[str]			   =       """[INST]""", """[/INST]"""
SCREAMING_SNAKE_CASE_						:					Tuple			   =       """<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
SCREAMING_SNAKE_CASE_						:					Dict			   =       """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 		__UpperCAmelCase(		UpperCAmelCase__ ):





     """simple docstring"""



     __UpperCAmelCase	: Dict						=    VOCAB_FILES_NAMES
     __UpperCAmelCase	: str						=    PRETRAINED_VOCAB_FILES_MAP
     __UpperCAmelCase	: Optional[int]						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCAmelCase	: List[str]						=    ['input_ids', 'attention_mask']

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






         """simple docstring"""
         a__      =       {} if sp_model_kwargs is None else sp_model_kwargs
         a__      =       AddedToken(lowerCamelCase__						,				lstrip=lowerCamelCase__						,				rstrip=lowerCamelCase__    ) if isinstance(lowerCamelCase__						,				lowerCamelCase__    ) else bos_token
         a__      =       AddedToken(lowerCamelCase__						,				lstrip=lowerCamelCase__						,				rstrip=lowerCamelCase__    ) if isinstance(lowerCamelCase__						,				lowerCamelCase__    ) else eos_token
         a__      =       AddedToken(lowerCamelCase__						,				lstrip=lowerCamelCase__						,				rstrip=lowerCamelCase__    ) if isinstance(lowerCamelCase__						,				lowerCamelCase__    ) else unk_token
         a__      =       AddedToken(lowerCamelCase__						,				lstrip=lowerCamelCase__						,				rstrip=lowerCamelCase__    ) if isinstance(lowerCamelCase__						,				lowerCamelCase__    ) else pad_token
         super().__init__(
             bos_token=lowerCamelCase__						,				eos_token=lowerCamelCase__						,				unk_token=lowerCamelCase__						,				pad_token=lowerCamelCase__						,				add_bos_token=lowerCamelCase__						,				add_eos_token=lowerCamelCase__						,				sp_model_kwargs=self.sp_model_kwargs						,				clean_up_tokenization_spaces=lowerCamelCase__						,				legacy=lowerCamelCase__						,				**lowerCamelCase__						,				)
         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"""    )
         a__      =       legacy
         a__      =       vocab_file
         a__      =       add_bos_token
         a__      =       add_eos_token
         a__      =       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(lowerCamelCase__    )

     def __getstate__(      self :       List[str]    )			->			int:






         """simple docstring"""
         a__      =       self.__dict__.copy()
         a__      =       None
         a__      =       self.sp_model.serialized_model_proto()
         return state

     def __setstate__(      self :       List[str]						,				A :       List[str]    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       d
         a__      =       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )

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






         """simple docstring"""
         return self.sp_model.get_piece_size()

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			List[Any]:






         """simple docstring"""
         a__      =       {self.convert_ids_to_tokens(lowerCamelCase__    ): i for i in range(self.vocab_size    )}
         vocab.update(self.added_tokens_encoder    )
         return vocab

     def 				__SCREAMING_SNAKE_CASE		(      self :       int						,				A :       List[Any]						,				**A :       Dict    )			->			Tuple:






         """simple docstring"""
         # 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:
             a__      =       SPIECE_UNDERLINE + text.replace(lowerCamelCase__						,				""" """    )
         return super().tokenize(lowerCamelCase__						,				**lowerCamelCase__    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       str    )			->			int:






         """simple docstring"""
         if not self.legacy:
             a__      =       text.startswith(lowerCamelCase__    )
             if is_first:
                 a__      =       text[1:]

         a__      =       self.sp_model.encode(lowerCamelCase__						,				out_type=lowerCamelCase__    )

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       str						,				A :       List[Any]    )			->			Tuple:






         """simple docstring"""
         return self.sp_model.piece_to_id(lowerCamelCase__    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict						,				A :       Optional[int]    )			->			Any:






         """simple docstring"""
         a__      =       self.sp_model.IdToPiece(lowerCamelCase__    )
         return token

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple						,				A :       int    )			->			Dict:






         """simple docstring"""
         a__      =       []
         a__      =       ""
         a__      =       False
         for i, token in enumerate(lowerCamelCase__    ):
             # 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(lowerCamelCase__    ) + token
                 a__      =       True
                 a__      =       []
             else:
                 current_sub_tokens.append(lowerCamelCase__    )
                 a__      =       False
         out_string += self.sp_model.decode(lowerCamelCase__    )
         return out_string

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict						,				A :       Union[str, Any]						,				A :       List[Any] = None    )			->			Any:






         """simple docstring"""
         if not os.path.isdir(lowerCamelCase__    ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
             return
         a__      =       os.path.join(
             lowerCamelCase__						,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

         if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowerCamelCase__    ) and os.path.isfile(self.vocab_file    ):
             copyfile(self.vocab_file						,				lowerCamelCase__    )
         elif not os.path.isfile(self.vocab_file    ):
             with open(lowerCamelCase__						,				"""wb"""    ) as fi:
                 a__      =       self.sp_model.serialized_model_proto()
                 fi.write(lowerCamelCase__    )

         return (out_vocab_file,)

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any						,				A :       Any						,				A :       Any=None    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       [self.bos_token_id] if self.add_bos_token else []
         a__      =       [self.eos_token_id] if self.add_eos_token else []

         a__      =       bos_token_id + token_ids_a + eos_token_id

         if token_ids_a is not None:
             a__      =       output + bos_token_id + token_ids_a + eos_token_id

         return output

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]						,				A :       Optional[Any]						,				A :       int = None						,				A :       List[str] = False    )			->			Dict:






         """simple docstring"""
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=lowerCamelCase__						,				token_ids_a=lowerCamelCase__						,				already_has_special_tokens=lowerCamelCase__    )

         a__      =       [1] if self.add_bos_token else []
         a__      =       [1] if self.add_eos_token else []

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       Dict						,				A :       List[str] = None    )			->			Dict:






         """simple docstring"""
         a__      =       [self.bos_token_id] if self.add_bos_token else []
         a__      =       [self.eos_token_id] if self.add_eos_token else []

         a__      =       [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 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       List[Any]    )			->			Optional[int]:






         """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(lowerCamelCase__    ) 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(lowerCamelCase__    ) 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=lowerCamelCase__    )
                 + [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=lowerCamelCase__    )
         return dialog_tokens












from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_						:					List[str]			   =       {'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					Tuple			   =       [
     '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTHybridForImageClassification''',
     '''ViTHybridModel''',
     '''ViTHybridPreTrainedModel''',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					int			   =       ['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

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






