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:
   a_					      = None

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

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

# fmt: off
a_					      = '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 	_lowercase		(      snake_case_ ):
 lowercase         =     VOCAB_FILES_NAMES
 lowercase         =     LlamaTokenizer
 lowercase         =     'left'
 lowercase         =     ['input_ids', 'attention_mask']

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

  """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     ,   )
  UpperCamelCase_      :		List[str]				=      add_bos_token
  UpperCamelCase_      :		str				=      add_eos_token
  self.update_post_processor()

  UpperCamelCase_      :		List[str]				=      vocab_file
  UpperCamelCase_      :		str				=      False if not self.vocab_file else True

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      self.bos_token
  UpperCamelCase_      :		int				=      self.bos_token_id

  UpperCamelCase_      :		List[str]				=      self.eos_token
  UpperCamelCase_      :		List[Any]				=      self.eos_token_id

  UpperCamelCase_      :		Optional[int]				=      f"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
  UpperCamelCase_      :		Optional[Any]				=      f"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

  UpperCamelCase_      :		int				=      []
  if self.add_bos_token:
   special_tokens.append((bos, bos_token_id)    )
  if self.add_eos_token:
   special_tokens.append((eos, eos_token_id)    )
  UpperCamelCase_      :		Optional[int]				=      processors.TemplateProcessing(
      single=snake_case     ,   pair=snake_case     ,   special_tokens=snake_case    )

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		int:

  """simple docstring"""





  return self._add_eos_token

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

  """simple docstring"""





  return self._add_bos_token

 @add_eos_token.setter
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							Optional[int]    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      value
  self.update_post_processor()

 @add_bos_token.setter
 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							Union[str, Any]    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      value
  self.update_post_processor()

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							str     ,   snake_case			:							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(snake_case    ):
   logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
   return
  UpperCamelCase_      :		Optional[int]				=      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 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							"Conversation"    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, 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...)'    )

  UpperCamelCase_      :		Optional[Any]				=      []
  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]:
    UpperCamelCase_      :		Tuple				=      (
        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]:
   UpperCamelCase_      :		int				=      (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




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,
)


a_					      = {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

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

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

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

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['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

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



