

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


a_     :    Union[str, Any]     		=   logging.get_logger(__name__)

a_     :    Optional[int]     		=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_     :    Union[str, Any]     		=   {
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

a_     :    Optional[Any]     		=   {
    """gpt-neox-20b""": 2048,
}




class   UpperCamelCase			(  UpperCAmelCase_ ):
    __UpperCamelCase    =VOCAB_FILES_NAMES
    __UpperCamelCase    =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase    =['input_ids', 'attention_mask']

    def __init__(      self			:   Optional[int]				,     snake_case__			:   List[Any]=None				,     snake_case__			:   List[Any]=None				,     snake_case__			:   Any=None				,     snake_case__			:   str="<|endoftext|>"				,     snake_case__			:   List[Any]="<|endoftext|>"				,     snake_case__			:   int="<|endoftext|>"				,     snake_case__			:   str=False				,     **snake_case__			:   List[str]				,     ):

     """simple docstring"""






     super().__init__(
         SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     tokenizer_file=SCREAMING_SNAKE_CASE_				,     unk_token=SCREAMING_SNAKE_CASE_				,     bos_token=SCREAMING_SNAKE_CASE_				,     eos_token=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_				,     )

     SCREAMING_SNAKE_CASE		    =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
     if pre_tok_state.get('add_prefix_space'				,     SCREAMING_SNAKE_CASE_     ) != add_prefix_space:
      SCREAMING_SNAKE_CASE		    =      getattr(SCREAMING_SNAKE_CASE_				,     pre_tok_state.pop('type'     )     )
      SCREAMING_SNAKE_CASE		    =      add_prefix_space
      SCREAMING_SNAKE_CASE		    =      pre_tok_class(**SCREAMING_SNAKE_CASE_     )

     SCREAMING_SNAKE_CASE		    =      add_prefix_space

    def UpperCamelCase      (      self			:   str				,     snake_case__			:   Dict				,     snake_case__			:   Tuple = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self._tokenizer.model.save(SCREAMING_SNAKE_CASE_				,     name=SCREAMING_SNAKE_CASE_     )
     return tuple(SCREAMING_SNAKE_CASE_     )




    def UpperCamelCase      (      self			:   Optional[int]				,     snake_case__			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      []
     for is_user, text in conversation.iter_texts():
      input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_     ) + [self.eos_token_id]     )

     if len(SCREAMING_SNAKE_CASE_     ) > self.model_max_length:
      SCREAMING_SNAKE_CASE		    =      input_ids[-self.model_max_length :]
     return input_ids



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



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['image_processor', 'tokenizer']
 __lowerCAmelCase	:  Union[str, Any]	   =      'AutoImageProcessor'
 __lowerCAmelCase	:  int	   =      'AutoTokenizer'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Union[str, Any]     		=   self.image_processor


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   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:
     lowercase__	: List[str]     		=   self.tokenizer(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if images is not None:
     lowercase__	: Optional[int]     		=   self.image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if text is not None and images is not None:
     lowercase__	: Union[str, Any]     		=   image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_)       ,	tensor_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)



 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return ["input_ids", "attention_mask", "pixel_values"]






