


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__snake_case         =					{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case         =					[
      """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BioGptForCausalLM""",
      """BioGptForTokenClassification""",
      """BioGptForSequenceClassification""",
      """BioGptModel""",
      """BioGptPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  __snake_case         =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

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


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """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 _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Optional[Any]													=						'''big_bird'''







   def __init__(			self		,			UpperCamelCase__=5_0358		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu_new"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=4096		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=True		,			UpperCamelCase__=0		,			UpperCamelCase__=1		,			UpperCamelCase__=2		,			UpperCamelCase__=66		,			UpperCamelCase__="block_sparse"		,			UpperCamelCase__=True		,			UpperCamelCase__=False		,			UpperCamelCase__=64		,			UpperCamelCase__=3		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Tuple:

       '''simple docstring'''




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

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

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







class _lowerCAmelCase       (   snake_case_			):







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




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





