



"""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       	=						{
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class 				lowercase__       (   UpperCamelCase_				):







       '''simple docstring'''


       UpperCamelCase						=					"""big_bird"""



       def __init__(							self      :  Tuple		,    _UpperCAmelCase      :  Optional[int]=50358		,    _UpperCAmelCase      :  Union[str, Any]=768		,    _UpperCAmelCase      :  List[str]=12		,    _UpperCAmelCase      :  Union[str, Any]=12		,    _UpperCAmelCase      :  Dict=3072		,    _UpperCAmelCase      :  str="gelu_new"		,    _UpperCAmelCase      :  Dict=0.1		,    _UpperCAmelCase      :  Union[str, Any]=0.1		,    _UpperCAmelCase      :  Any=4096		,    _UpperCAmelCase      :  Any=2		,    _UpperCAmelCase      :  Optional[int]=0.02		,    _UpperCAmelCase      :  Any=1e-12		,    _UpperCAmelCase      :  str=True		,    _UpperCAmelCase      :  List[Any]=0		,    _UpperCAmelCase      :  Union[str, Any]=1		,    _UpperCAmelCase      :  Dict=2		,    _UpperCAmelCase      :  Union[str, Any]=66		,    _UpperCAmelCase      :  Optional[int]="block_sparse"		,    _UpperCAmelCase      :  List[str]=True		,    _UpperCAmelCase      :  Tuple=False		,    _UpperCAmelCase      :  str=64		,    _UpperCAmelCase      :  Any=3		,    _UpperCAmelCase      :  List[Any]=None		,    **_UpperCAmelCase      :  Union[str, Any]		,    )			->						Union[str, Any]:


             '''simple docstring'''




             super().__init__(
                 pad_token_id=_a		,    bos_token_id=_a		,    eos_token_id=_a		,    sep_token_id=_a		,    **_a		,    )

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

             UpperCAmelCase_      			=							rescale_embeddings
             UpperCAmelCase_      			=							attention_type
             UpperCAmelCase_      			=							use_bias
             UpperCAmelCase_      			=							block_size
             UpperCAmelCase_      			=							num_random_blocks
             UpperCAmelCase_      			=							classifier_dropout



class 				lowercase__       (   UpperCamelCase_				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  Dict   )			->						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),
                 ]   )









"""simple docstring"""





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					['''image_processor''', '''tokenizer''']
       UpperCamelCase						=					'''ViTImageProcessor'''
       UpperCamelCase						=					('''CLIPTokenizer''', '''CLIPTokenizerFast''')



       def __init__(							self      :  List[Any]		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  Optional[int]=None		,    **_UpperCAmelCase      :  List[str]   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead."		,    _UpperCAmelCase		,    )
                   UpperCAmelCase_      			=							kwargs.pop("feature_extractor"   )

             UpperCAmelCase_      			=							image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError("You need to specify an `image_processor`."   )
             if tokenizer is None:
                   raise ValueError("You need to specify a `tokenizer`."   )

             super().__init__(_UpperCAmelCase		,    _UpperCAmelCase   )



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


             '''simple docstring'''




             if text is None and visual_prompt is None and images is None:
                   raise ValueError("You have to specify either text, visual prompt or images."   )

             if text is not None and visual_prompt is not None:
                   raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."   )

             if text is not None:
                   UpperCAmelCase_      			=							self.tokenizer(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if visual_prompt is not None:
                   UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if images is not None:
                   UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if visual_prompt is not None and images is not None:
                   UpperCAmelCase_      			=							{
                       "pixel_values": image_features.pixel_values,
                       "conditional_pixel_values": prompt_features.pixel_values,
                   }
                   return encoding
             elif text is not None and images is not None:
                   UpperCAmelCase_      			=							image_features.pixel_values
                   return encoding
             elif text is not None:
                   return encoding
             elif visual_prompt is not None:
                   UpperCAmelCase_      			=							{
                       "conditional_pixel_values": prompt_features.pixel_values,
                   }
                   return encoding
             else:
                   return BatchEncoding(data=dict(**_UpperCAmelCase   )		,    tensor_type=_UpperCAmelCase   )



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


             '''simple docstring'''




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



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


             '''simple docstring'''




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



       @property
       def 					lowercase__    (							self      :  int   )			->						Optional[int]:


             '''simple docstring'''




             warnings.warn(
                 "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,    _UpperCAmelCase		,    )
             return self.image_processor_class







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


             '''simple docstring'''




             warnings.warn(
                 "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,    _UpperCAmelCase		,    )
             return self.image_processor




