





'''simple docstring'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class lowerCamelCase						(a__					):
  _lowercase  :	Tuple       					=				["""image_processor""", """tokenizer"""]
  _lowercase  :	List[Any]       					=				"""BlipImageProcessor"""
  _lowercase  :	str       					=				("""BertTokenizer""", """BertTokenizerFast""")







  def __init__(		self     ,     lowercase__     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Tuple					     =     False
        super().__init__(__A     ,     __A		)
        _snake_case		:	Tuple					     =     self.image_processor







  def __call__(		self     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = True     ,     lowercase__ = False     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = 0     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = False     ,     lowercase__ = False     ,     lowercase__ = False     ,     lowercase__ = False     ,     lowercase__ = False     ,     lowercase__ = True     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						BatchEncoding:






        """simple docstring"""





        if images is None and text is None:
              raise ValueError('''You have to specify either images or text.'''		)

        # Get only text
        if images is None:
              _snake_case		:	int					     =     self.tokenizer
              _snake_case		:	Dict					     =     self.tokenizer(
                  text=__A     ,     add_special_tokens=__A     ,     padding=__A     ,     truncation=__A     ,     max_length=__A     ,     stride=__A     ,     pad_to_multiple_of=__A     ,     return_attention_mask=__A     ,     return_overflowing_tokens=__A     ,     return_special_tokens_mask=__A     ,     return_offsets_mapping=__A     ,     return_token_type_ids=__A     ,     return_length=__A     ,     verbose=__A     ,     return_tensors=__A     ,     **__A     ,     )
              return text_encoding

        # add pixel_values
        _snake_case		:	Optional[Any]					     =     self.image_processor(__A     ,     return_tensors=__A		)

        if text is not None:
              _snake_case		:	List[Any]					     =     self.tokenizer(
                  text=__A     ,     add_special_tokens=__A     ,     padding=__A     ,     truncation=__A     ,     max_length=__A     ,     stride=__A     ,     pad_to_multiple_of=__A     ,     return_attention_mask=__A     ,     return_overflowing_tokens=__A     ,     return_special_tokens_mask=__A     ,     return_offsets_mapping=__A     ,     return_token_type_ids=__A     ,     return_length=__A     ,     verbose=__A     ,     return_tensors=__A     ,     **__A     ,     )
        else:
              _snake_case		:	Dict					     =     None

        if text_encoding is not None:
              encoding_image_processor.update(__A		)

        return encoding_image_processor







  def 				UpperCAmelCase_				(		self     ,     *lowercase__     ,     **lowercase__		)		->						int:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     *lowercase__     ,     **lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





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







  @property
  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Any					     =     self.tokenizer.model_input_names
        _snake_case		:	Dict					     =     self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)









'''simple docstring'''

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


UpperCAmelCase					:		Optional[int]		=							logging.get_logger(__name__)

UpperCAmelCase					:		List[Any]		=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase					:		Any		=							{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase					:		Optional[Any]		=							{
    'gpt-neox-20b': 2_0_4_8,
}





class lowerCamelCase						(a__					):
  _lowercase  :	Optional[int]       					=				VOCAB_FILES_NAMES
  _lowercase  :	str       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Dict       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	Optional[int]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__=False     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





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

        _snake_case		:	Optional[int]					     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get('''add_prefix_space'''     ,     lowercase__		) != add_prefix_space:
              _snake_case		:	int					     =     getattr(lowercase__     ,     pre_tok_state.pop('''type'''		)		)
              _snake_case		:	int					     =     add_prefix_space
              _snake_case		:	Optional[Any]					     =     pre_tok_class(**lowercase__		)

        _snake_case		:	List[str]					     =     add_prefix_space







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self._tokenizer.model.save(lowercase__     ,     name=lowercase__		)
        return tuple(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[int]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase__     ,     add_special_tokens=lowercase__		) + [self.eos_token_id]		)

        if len(lowercase__		) > self.model_max_length:
              _snake_case		:	Dict					     =     input_ids[-self.model_max_length :]
        return input_ids


