



'''simple docstring'''


from typing import TYPE_CHECKING

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


lowercase							       =			{
    '''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
    '''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
    '''tokenization_funnel''': ['''FunnelTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''FunnelTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''FunnelBaseModel''',
      '''FunnelForMaskedLM''',
      '''FunnelForMultipleChoice''',
      '''FunnelForPreTraining''',
      '''FunnelForQuestionAnswering''',
      '''FunnelForSequenceClassification''',
      '''FunnelForTokenClassification''',
      '''FunnelModel''',
      '''FunnelPreTrainedModel''',
      '''load_tf_weights_in_funnel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFFunnelBaseModel''',
      '''TFFunnelForMaskedLM''',
      '''TFFunnelForMultipleChoice''',
      '''TFFunnelForPreTraining''',
      '''TFFunnelForQuestionAnswering''',
      '''TFFunnelForSequenceClassification''',
      '''TFFunnelForTokenClassification''',
      '''TFFunnelModel''',
      '''TFFunnelPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
  from .tokenization_funnel import FunnelTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_funnel_fast import FunnelTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_funnel import (
        FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
        FunnelBaseModel,
        FunnelForMaskedLM,
        FunnelForMultipleChoice,
        FunnelForPreTraining,
        FunnelForQuestionAnswering,
        FunnelForSequenceClassification,
        FunnelForTokenClassification,
        FunnelModel,
        FunnelPreTrainedModel,
        load_tf_weights_in_funnel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_funnel import (
        TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFunnelBaseModel,
        TFFunnelForMaskedLM,
        TFFunnelForMultipleChoice,
        TFFunnelForPreTraining,
        TFFunnelForQuestionAnswering,
        TFFunnelForSequenceClassification,
        TFFunnelForTokenClassification,
        TFFunnelModel,
        TFFunnelPreTrainedModel,
    )

else:
  import sys

  lowercase							       =			_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


lowercase							       =			{
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: int  =      "albert"




 def __init__(   self							,  lowerCAmelCase_=3_0_0_0_0							,  lowerCAmelCase_=1_2_8							,  lowerCAmelCase_=4_0_9_6							,  lowerCAmelCase_=1_2							,  lowerCAmelCase_=1							,  lowerCAmelCase_=6_4							,  lowerCAmelCase_=1_6_3_8_4							,  lowerCAmelCase_=1							,  lowerCAmelCase_="gelu_new"							,  lowerCAmelCase_=0							,  lowerCAmelCase_=0							,  lowerCAmelCase_=5_1_2							,  lowerCAmelCase_=2							,  lowerCAmelCase_=0.0_2							,  lowerCAmelCase_=1e-12							,  lowerCAmelCase_=0.1							,  lowerCAmelCase_="absolute"							,  lowerCAmelCase_=0							,  lowerCAmelCase_=2							,  lowerCAmelCase_=3							,  **lowerCAmelCase_							,  ) ->      Any:



      """simple docstring"""







      super().__init__(pad_token_id=lowerCAmelCase_							,  bos_token_id=lowerCAmelCase_							,  eos_token_id=lowerCAmelCase_							,  **lowerCAmelCase_)

      a_       		=vocab_size
      a_       		=embedding_size
      a_       		=hidden_size
      a_       		=num_hidden_layers
      a_       		=num_hidden_groups
      a_       		=num_attention_heads
      a_       		=inner_group_num
      a_       		=hidden_act
      a_       		=intermediate_size
      a_       		=hidden_dropout_prob
      a_       		=attention_probs_dropout_prob
      a_       		=max_position_embeddings
      a_       		=type_vocab_size
      a_       		=initializer_range
      a_       		=layer_norm_eps
      a_       		=classifier_dropout_prob
      a_       		=position_embedding_type







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




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



      """simple docstring"""







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


