


'''simple docstring'''







from typing import TYPE_CHECKING

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


a__  :					Optional[Any]        =       {
    'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  :					Dict        =       [
      'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ErnieForCausalLM',
      'ErnieForMaskedLM',
      'ErnieForMultipleChoice',
      'ErnieForNextSentencePrediction',
      'ErnieForPreTraining',
      'ErnieForQuestionAnswering',
      'ErnieForSequenceClassification',
      'ErnieForTokenClassification',
      'ErnieModel',
      'ErniePreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ernie import (
        ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
        ErnieForCausalLM,
        ErnieForMaskedLM,
        ErnieForMultipleChoice,
        ErnieForNextSentencePrediction,
        ErnieForPreTraining,
        ErnieForQuestionAnswering,
        ErnieForSequenceClassification,
        ErnieForTokenClassification,
        ErnieModel,
        ErniePreTrainedModel,
    )

else:
  import sys

  a__  :					List[Any]        =       _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


UpperCAmelCase_		:   Union[str, Any]							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   Optional[int]							   =		{
    '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__  (						_snake_case					):





							'''simple docstring'''


							A_							:      str      	=  """big_bird"""






							def __init__(    self    ,					__snake_case=5_0358    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case="gelu_new"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=4096    ,					__snake_case=2    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=True    ,					__snake_case=0    ,					__snake_case=1    ,					__snake_case=2    ,					__snake_case=66    ,					__snake_case="block_sparse"    ,					__snake_case=True    ,					__snake_case=False    ,					__snake_case=64    ,					__snake_case=3    ,					__snake_case=None    ,					**__snake_case    ,					):
											super().__init__(
											    pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					sep_token_id=__snake_case    ,					**__snake_case    ,					)

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

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      rescale_embeddings
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      attention_type
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      use_bias
											_SCREAMING_SNAKE_CASE :       int     		=      block_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_random_blocks
											_SCREAMING_SNAKE_CASE :       List[str]     		=      classifier_dropout





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


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




