


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase    =				{
    """configuration_mobilebert""": [
        """MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileBertConfig""",
        """MobileBertOnnxConfig""",
    ],
    """tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				["""MobileBertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """MobileBertForMaskedLM""",
     """MobileBertForMultipleChoice""",
     """MobileBertForNextSentencePrediction""",
     """MobileBertForPreTraining""",
     """MobileBertForQuestionAnswering""",
     """MobileBertForSequenceClassification""",
     """MobileBertForTokenClassification""",
     """MobileBertLayer""",
     """MobileBertModel""",
     """MobileBertPreTrainedModel""",
     """load_tf_weights_in_mobilebert""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFMobileBertForMaskedLM""",
     """TFMobileBertForMultipleChoice""",
     """TFMobileBertForNextSentencePrediction""",
     """TFMobileBertForPreTraining""",
     """TFMobileBertForQuestionAnswering""",
     """TFMobileBertForSequenceClassification""",
     """TFMobileBertForTokenClassification""",
     """TFMobileBertMainLayer""",
     """TFMobileBertModel""",
     """TFMobileBertPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_mobilebert import (
     MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileBertConfig,
     MobileBertOnnxConfig,
 )
 from .tokenization_mobilebert import MobileBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mobilebert_fast import MobileBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilebert import (
      MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileBertForMaskedLM,
      MobileBertForMultipleChoice,
      MobileBertForNextSentencePrediction,
      MobileBertForPreTraining,
      MobileBertForQuestionAnswering,
      MobileBertForSequenceClassification,
      MobileBertForTokenClassification,
      MobileBertLayer,
      MobileBertModel,
      MobileBertPreTrainedModel,
      load_tf_weights_in_mobilebert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mobilebert import (
      TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMobileBertForMaskedLM,
      TFMobileBertForMultipleChoice,
      TFMobileBertForNextSentencePrediction,
      TFMobileBertForPreTraining,
      TFMobileBertForQuestionAnswering,
      TFMobileBertForSequenceClassification,
      TFMobileBertForTokenClassification,
      TFMobileBertMainLayer,
      TFMobileBertModel,
      TFMobileBertPreTrainedModel,
  )

else:
 import sys

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










"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ...onnx.config import PatchingSpec
 from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	"""longformer"""






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[List[int], int] = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 1    ,			__UpperCAmelCase    :   int = 0    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 30522    ,			__UpperCAmelCase    :   int = 768    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 3072    ,			__UpperCAmelCase    :   str = "gelu"    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   int = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   float = 0.02    ,			__UpperCAmelCase    :   float = 1e-12    ,			__UpperCAmelCase    :   bool = False    ,			**__UpperCAmelCase    :   int    ,			):
      super().__init__(pad_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)

      a						:		int											=      attention_window
      a						:		List[str]											=      sep_token_id
      a						:		List[Any]											=      bos_token_id
      a						:		int											=      eos_token_id
      a						:		Union[str, Any]											=      vocab_size
      a						:		Dict											=      hidden_size
      a						:		int											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Optional[int]											=      hidden_act
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Tuple											=      hidden_dropout_prob
      a						:		Union[str, Any]											=      attention_probs_dropout_prob
      a						:		str											=      max_position_embeddings
      a						:		Dict											=      type_vocab_size
      a						:		Optional[Any]											=      initializer_range
      a						:		List[Any]											=      layer_norm_eps
      a						:		List[str]											=      onnx_export






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   "PretrainedConfig"    ,			__UpperCAmelCase    :   str = "default"    ,			__UpperCAmelCase    :   "List[PatchingSpec]" = None):
      super().__init__(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      a						:		Optional[Any]											=      True






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






    @property
    def      __snake_case							( self    :   Union[str, Any]):
      a						:		str											=      super().outputs
      if self.task == "default":
        a						:		Tuple											=      {0: "batch"}
      return outputs






    @property
    def      __snake_case							( self    :   Optional[int]):
      return 1e-4






    @property
    def      __snake_case							( self    :   str):
      # needs to be >= 14 to support tril operator
      return max(super().default_onnx_opset    ,			14)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   "PreTrainedTokenizerBase"    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   bool = False    ,			__UpperCAmelCase    :   Optional[TensorType] = None    ,			):
      a						:		str											=      super().generate_dummy_inputs(
          preprocessor=__UpperCAmelCase    ,			batch_size=__UpperCAmelCase    ,			seq_length=__UpperCAmelCase    ,			is_pair=__UpperCAmelCase    ,			framework=__UpperCAmelCase)
      import torch

      # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
      # makes the export fail randomly
      a						:		Union[str, Any]											=      torch.zeros_like(inputs["input_ids"])
      # make every second token global
      a						:		Dict											=      1

      return inputs






