





from collections import OrderedDict
from typing import Mapping

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


_A   :       Any    					=    logging.get_logger(__name__)

_A   :       Optional[Any]    					=    {
    """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   __SCREAMING_SNAKE_CASE     (					lowerCAmelCase_   ):
       _UpperCAmelCase :    List[Any]					       =       "big_bird"







       def __init__( self	:      List[Any]						,   A	:      int=5_0_3_5_8						,   A	:      Dict=7_6_8						,   A	:      Any=1_2						,   A	:      List[Any]=1_2						,   A	:      Tuple=3_0_7_2						,   A	:      List[Any]="gelu_new"						,   A	:      Any=0.1						,   A	:      List[str]=0.1						,   A	:      Union[str, Any]=4_0_9_6						,   A	:      Union[str, Any]=2						,   A	:      Dict=0.02						,   A	:      List[Any]=1e-12						,   A	:      Optional[Any]=True						,   A	:      List[str]=0						,   A	:      Dict=1						,   A	:      List[str]=2						,   A	:      str=6_6						,   A	:      Optional[Any]="block_sparse"						,   A	:      Tuple=True						,   A	:      Any=False						,   A	:      int=6_4						,   A	:      int=3						,   A	:      int=None						,   **A	:      str						,   )  ->Optional[Any]:
          super().__init__(
              pad_token_id=__lowerCAmelCase						,   bos_token_id=__lowerCAmelCase						,   eos_token_id=__lowerCAmelCase						,   sep_token_id=__lowerCAmelCase						,   **__lowerCAmelCase						,   )

          lowerCamelCase__  :		Union[str, Any]     =				vocab_size
          lowerCamelCase__  :		List[str]     =				max_position_embeddings
          lowerCamelCase__  :		Dict     =				hidden_size
          lowerCamelCase__  :		Union[str, Any]     =				num_hidden_layers
          lowerCamelCase__  :		str     =				num_attention_heads
          lowerCamelCase__  :		Any     =				intermediate_size
          lowerCamelCase__  :		List[Any]     =				hidden_act
          lowerCamelCase__  :		Any     =				hidden_dropout_prob
          lowerCamelCase__  :		str     =				attention_probs_dropout_prob
          lowerCamelCase__  :		Union[str, Any]     =				initializer_range
          lowerCamelCase__  :		Optional[int]     =				type_vocab_size
          lowerCamelCase__  :		Optional[Any]     =				layer_norm_eps
          lowerCamelCase__  :		List[str]     =				use_cache

          lowerCamelCase__  :		Optional[Any]     =				rescale_embeddings
          lowerCamelCase__  :		Optional[Any]     =				attention_type
          lowerCamelCase__  :		Optional[Any]     =				use_bias
          lowerCamelCase__  :		Optional[int]     =				block_size
          lowerCamelCase__  :		List[Any]     =				num_random_blocks
          lowerCamelCase__  :		List[str]     =				classifier_dropout


class   __SCREAMING_SNAKE_CASE     (					lowerCAmelCase_   ):







       @property
       def 		__lowerCamelCase       ( self	:      int     )  ->Any:
          if self.task == "multiple-choice":
             lowerCamelCase__  :		int     =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
             lowerCamelCase__  :		Dict     =				{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ]     )











import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''ChineseCLIPImageProcessor'''
				a__       							=	('''BertTokenizer''', '''BertTokenizerFast''')







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   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							,       )
												__magic_name__		:Optional[Any]   =   kwargs.pop('''feature_extractor'''  )

								__magic_name__		:Tuple   =   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__(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   self.image_processor







				def __call__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""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:
												__magic_name__		:int   =   self.tokenizer(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if images is not None:
												__magic_name__		:Dict   =   self.image_processor(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if text is not None and images is not None:
												__magic_name__		:Union[str, 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							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.tokenizer.model_input_names
								__magic_name__		:Any   =   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       __lowerCAmelCase							,       )
								return self.image_processor_class






