





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:
    lowerCamelCase_  						=       None

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

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

# fmt: off
lowerCamelCase_  						=       "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 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   Dict     =  VOCAB_FILES_NAMES
     _A    :   Tuple     =  LlamaTokenizer
     _A    :   int     =  "left"
     _A    :   List[Any]     =  ["input_ids", "attention_mask"]




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

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




     def   __A    (     self      :			Union[str, Any]						)		->       Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.bos_token
         SCREAMING_SNAKE_CASE__       =self.bos_token_id

         SCREAMING_SNAKE_CASE__       =self.eos_token
         SCREAMING_SNAKE_CASE__       =self.eos_token_id

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

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




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


         '''simple docstring'''





         return self._add_eos_token




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


         '''simple docstring'''





         return self._add_bos_token




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =value
         self.update_post_processor()




     @add_bos_token.setter
     def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			Tuple						)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =value
         self.update_post_processor()




     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Optional[str] = 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
         SCREAMING_SNAKE_CASE__       =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      :			Optional[Any]    ,_UpperCamelCase      :			"Conversation"						)		->       Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =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...)"""						)

         SCREAMING_SNAKE_CASE__       =[]
         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]:
                 SCREAMING_SNAKE_CASE__       =(
                     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]:
             SCREAMING_SNAKE_CASE__       =(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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_  						=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["MarianTokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       [
        "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MarianForCausalLM",
        "MarianModel",
        "MarianMTModel",
        "MarianPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
    from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_marian import MarianTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_marian import (
            MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
            MarianForCausalLM,
            MarianModel,
            MarianMTModel,
            MarianPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
    import sys

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






