
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 		__magic_name__   (						snake_case    ):
  _lowerCAmelCase		=	["image_processor", "tokenizer"]
  _lowerCAmelCase		=	"AutoImageProcessor"
  _lowerCAmelCase		=	"AutoTokenizer"





  def __init__(							self    :	str						,  lowerCamelCase__    :	int=None						,  lowerCamelCase__    :	List[Any]=None						,  **lowerCamelCase__    :	str				):
       lowerCAmelCase		:					Dict  	=     None
       if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''						,  lowerCamelCase__						,  )
            lowerCAmelCase		:					List[Any]  	=     kwargs.pop('''feature_extractor'''				)

       lowerCAmelCase		:					Tuple  	=     image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
            raise ValueError('''You have to specify an image_processor.'''				)
       if tokenizer is None:
            raise ValueError('''You have to specify a tokenizer.'''				)

       super().__init__(lowerCamelCase__						,  lowerCamelCase__				)
       lowerCAmelCase		:					List[str]  	=     self.image_processor





  def __call__(							self    :	Any						,  lowerCamelCase__    :	List[Any]=None						,  lowerCamelCase__    :	str=None						,  lowerCamelCase__    :	List[str]=None						,  **lowerCamelCase__    :	Optional[int]				):

       if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

       if text is not None:
            lowerCAmelCase		:					Union[str, Any]  	=     self.tokenizer(lowerCamelCase__						,  return_tensors=lowerCamelCase__						,  **lowerCamelCase__				)

       if images is not None:
            lowerCAmelCase		:					int  	=     self.image_processor(lowerCamelCase__						,  return_tensors=lowerCamelCase__						,  **lowerCamelCase__				)

       if text is not None and images is not None:
            lowerCAmelCase		:					List[Any]  	=     image_features.pixel_values
            return encoding
       elif text is not None:
            return encoding
       else:
            return BatchEncoding(data=dict(**lowerCamelCase__				)						,  tensor_type=lowerCamelCase__				)





  def       _A							(							self    :	int						,  *lowerCamelCase__    :	Tuple						,  **lowerCamelCase__    :	Union[str, Any]				):
       return self.tokenizer.batch_decode(*lowerCamelCase__						,  **lowerCamelCase__				)





  def       _A							(							self    :	int						,  *lowerCamelCase__    :	Any						,  **lowerCamelCase__    :	str				):
       return self.tokenizer.decode(*lowerCamelCase__						,  **lowerCamelCase__				)





  @property
  def       _A							(							self    :	Optional[Any]				):
       lowerCAmelCase		:					Optional[int]  	=     self.tokenizer.model_input_names
       lowerCAmelCase		:					List[str]  	=     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)





  @property
  def       _A							(							self    :	Union[str, Any]				):
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''						,  lowerCamelCase__						,  )
       return self.image_processor_class





  @property
  def       _A							(							self    :	Union[str, Any]				):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''						,  lowerCamelCase__						,  )
       return self.image_processor


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:
		__SCREAMING_SNAKE_CASE			:   int         =     None

__SCREAMING_SNAKE_CASE			:   List[Any]         =     logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE			:   Optional[int]         =     {'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Dict         =     '[INST]', '[/INST]'
__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   List[Any]         =     '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
__SCREAMING_SNAKE_CASE			:   List[Any]         =     '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 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	VOCAB_FILES_NAMES
		_lowerCAmelCase		=	LlamaTokenizer
		_lowerCAmelCase		=	"left"
		_lowerCAmelCase		=	["input_ids", "attention_mask"]





		def __init__(							self    :	List[Any]						,  lowerCamelCase__    :	int=None						,  lowerCamelCase__    :	Union[str, Any]=None						,  lowerCamelCase__    :	List[str]=False						,  lowerCamelCase__    :	int="<unk>"						,  lowerCamelCase__    :	int="<s>"						,  lowerCamelCase__    :	str="</s>"						,  lowerCamelCase__    :	Optional[Any]=True						,  lowerCamelCase__    :	int=False						,  **lowerCamelCase__    :	List[str]						,  ):
							super().__init__(
							    vocab_file=lowerCamelCase__						,  tokenizer_file=lowerCamelCase__						,  clean_up_tokenization_spaces=lowerCamelCase__						,  unk_token=lowerCamelCase__						,  bos_token=lowerCamelCase__						,  eos_token=lowerCamelCase__						,  **lowerCamelCase__						,  )
							lowerCAmelCase		:					Optional[int]  	=     add_bos_token
							lowerCAmelCase		:					int  	=     add_eos_token
							self.update_post_processor()

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





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					List[str]  	=     self.bos_token
							lowerCAmelCase		:					Optional[Any]  	=     self.bos_token_id

							lowerCAmelCase		:					Optional[Any]  	=     self.eos_token
							lowerCAmelCase		:					List[str]  	=     self.eos_token_id

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

							lowerCAmelCase		:					Any  	=     []
							if self.add_bos_token:
												special_tokens.append((bos, bos_token_id)				)
							if self.add_eos_token:
												special_tokens.append((eos, eos_token_id)				)
							lowerCAmelCase		:					Union[str, Any]  	=     processors.TemplateProcessing(
							    single=lowerCamelCase__						,  pair=lowerCamelCase__						,  special_tokens=lowerCamelCase__				)





		@property
		def       _A							(							self    :	str				):
							return self._add_eos_token





		@property
		def       _A							(							self    :	Dict				):
							return self._add_bos_token





		@add_eos_token.setter
		def       _A							(							self    :	Union[str, Any]						,  lowerCamelCase__    :	str				):
							lowerCAmelCase		:					Any  	=     value
							self.update_post_processor()





		@add_bos_token.setter
		def       _A							(							self    :	Dict						,  lowerCamelCase__    :	Optional[Any]				):
							lowerCAmelCase		:					List[Any]  	=     value
							self.update_post_processor()





		def       _A							(							self    :	int						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	Optional[str] = None				):
							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(lowerCamelCase__				):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
												return
							lowerCAmelCase		:					str  	=     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__				):
												copyfile(self.vocab_file						,  lowerCamelCase__				)

							return (out_vocab_file,)





		def       _A							(							self    :	Any						,  lowerCamelCase__    :	"Conversation"				):
							lowerCAmelCase		:					int  	=     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...)'''				)

							lowerCAmelCase		:					List[str]  	=     []
							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]:
																	lowerCAmelCase		:					Tuple  	=     (
																	    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]:
												lowerCAmelCase		:					Optional[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=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
