



"""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 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					['''image_processor''', '''tokenizer''']
       UpperCamelCase						=					'''BlipImageProcessor'''
       UpperCamelCase						=					('''BertTokenizer''', '''BertTokenizerFast''')



       def __init__(							self      :  Dict		,    _UpperCAmelCase      :  Dict		,    _UpperCAmelCase      :  Tuple   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							False
             super().__init__(_UpperCAmelCase		,    _UpperCAmelCase   )
             UpperCAmelCase_      			=							self.image_processor



       def __call__(							self      :  Optional[int]		,    _UpperCAmelCase      :  ImageInput = None		,    _UpperCAmelCase      :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  Union[bool, str, PaddingStrategy] = False		,    _UpperCAmelCase      :  Union[bool, str, TruncationStrategy] = None		,    _UpperCAmelCase      :  Optional[int] = None		,    _UpperCAmelCase      :  int = 0		,    _UpperCAmelCase      :  Optional[int] = None		,    _UpperCAmelCase      :  Optional[bool] = None		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  Optional[Union[str, TensorType]] = None		,    **_UpperCAmelCase      :  Dict		,    )			->						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:
                   UpperCAmelCase_      			=							self.tokenizer
                   UpperCAmelCase_      			=							self.tokenizer(
                       text=_UpperCAmelCase		,    add_special_tokens=_UpperCAmelCase		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=_UpperCAmelCase		,    stride=_UpperCAmelCase		,    pad_to_multiple_of=_UpperCAmelCase		,    return_attention_mask=_UpperCAmelCase		,    return_overflowing_tokens=_UpperCAmelCase		,    return_special_tokens_mask=_UpperCAmelCase		,    return_offsets_mapping=_UpperCAmelCase		,    return_token_type_ids=_UpperCAmelCase		,    return_length=_UpperCAmelCase		,    verbose=_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase		,    )
                   return text_encoding

             # add pixel_values
             UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase   )

             if text is not None:
                   UpperCAmelCase_      			=							self.tokenizer(
                       text=_UpperCAmelCase		,    add_special_tokens=_UpperCAmelCase		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=_UpperCAmelCase		,    stride=_UpperCAmelCase		,    pad_to_multiple_of=_UpperCAmelCase		,    return_attention_mask=_UpperCAmelCase		,    return_overflowing_tokens=_UpperCAmelCase		,    return_special_tokens_mask=_UpperCAmelCase		,    return_offsets_mapping=_UpperCAmelCase		,    return_token_type_ids=_UpperCAmelCase		,    return_length=_UpperCAmelCase		,    verbose=_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase		,    )
             else:
                   UpperCAmelCase_      			=							None

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

             return encoding_image_processor



       def 					lowercase__    (							self      :  Optional[int]		,    *_UpperCAmelCase      :  List[str]		,    **_UpperCAmelCase      :  str   )			->						Dict:


             '''simple docstring'''




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



       def 					lowercase__    (							self      :  Any		,    *_UpperCAmelCase      :  List[Any]		,    **_UpperCAmelCase      :  Tuple   )			->						str:


             '''simple docstring'''




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







       @property
       def 					lowercase__    (							self      :  int   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer.model_input_names
             UpperCAmelCase_      			=							self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )









"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''roberta'''



       def __init__(							self      :  int		,    _UpperCAmelCase      :  List[Any]=50265		,    _UpperCAmelCase      :  str=768		,    _UpperCAmelCase      :  List[str]=12		,    _UpperCAmelCase      :  int=12		,    _UpperCAmelCase      :  Tuple=3072		,    _UpperCAmelCase      :  Dict="gelu"		,    _UpperCAmelCase      :  int=0.1		,    _UpperCAmelCase      :  Optional[int]=0.1		,    _UpperCAmelCase      :  Optional[int]=512		,    _UpperCAmelCase      :  Optional[Any]=2		,    _UpperCAmelCase      :  Optional[int]=0.02		,    _UpperCAmelCase      :  Optional[Any]=1e-12		,    _UpperCAmelCase      :  Dict=1		,    _UpperCAmelCase      :  str=0		,    _UpperCAmelCase      :  int=2		,    _UpperCAmelCase      :  Tuple="absolute"		,    _UpperCAmelCase      :  Any=True		,    _UpperCAmelCase      :  int=None		,    **_UpperCAmelCase      :  List[str]		,    )			->						Any:


             '''simple docstring'''




             super().__init__(pad_token_id=_UpperCAmelCase		,    bos_token_id=_UpperCAmelCase		,    eos_token_id=_UpperCAmelCase		,    **_UpperCAmelCase   )

             UpperCAmelCase_      			=							vocab_size
             UpperCAmelCase_      			=							hidden_size
             UpperCAmelCase_      			=							num_hidden_layers
             UpperCAmelCase_      			=							num_attention_heads
             UpperCAmelCase_      			=							hidden_act
             UpperCAmelCase_      			=							intermediate_size
             UpperCAmelCase_      			=							hidden_dropout_prob
             UpperCAmelCase_      			=							attention_probs_dropout_prob
             UpperCAmelCase_      			=							max_position_embeddings
             UpperCAmelCase_      			=							type_vocab_size
             UpperCAmelCase_      			=							initializer_range
             UpperCAmelCase_      			=							layer_norm_eps
             UpperCAmelCase_      			=							position_embedding_type
             UpperCAmelCase_      			=							use_cache
             UpperCAmelCase_      			=							classifier_dropout



class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  List[Any]   )			->						Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             if self.task == "multiple-choice":
                   UpperCAmelCase_      			=							{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   UpperCAmelCase_      			=							{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ]   )




