
'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase          =						{
    """configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Config"""],
    """feature_extraction_wav2vec2""": ["""Wav2Vec2FeatureExtractor"""],
    """processing_wav2vec2""": ["""Wav2Vec2Processor"""],
    """tokenization_wav2vec2""": ["""Wav2Vec2CTCTokenizer""", """Wav2Vec2Tokenizer"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """Wav2Vec2ForAudioFrameClassification""",
						    """Wav2Vec2ForCTC""",
						    """Wav2Vec2ForMaskedLM""",
						    """Wav2Vec2ForPreTraining""",
						    """Wav2Vec2ForSequenceClassification""",
						    """Wav2Vec2ForXVector""",
						    """Wav2Vec2Model""",
						    """Wav2Vec2PreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFWav2Vec2ForCTC""",
						    """TFWav2Vec2Model""",
						    """TFWav2Vec2PreTrainedModel""",
						    """TFWav2Vec2ForSequenceClassification""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """FlaxWav2Vec2ForCTC""",
						    """FlaxWav2Vec2ForPreTraining""",
						    """FlaxWav2Vec2Model""",
						    """FlaxWav2Vec2PreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
						from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
						from .processing_wavaveca import WavaVecaProcessor
						from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavaveca import (
												    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavaVecaForAudioFrameClassification,
												    WavaVecaForCTC,
												    WavaVecaForMaskedLM,
												    WavaVecaForPreTraining,
												    WavaVecaForSequenceClassification,
												    WavaVecaForXVector,
												    WavaVecaModel,
												    WavaVecaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_wavaveca import (
												    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFWavaVecaForCTC,
												    TFWavaVecaForSequenceClassification,
												    TFWavaVecaModel,
												    TFWavaVecaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_wavaveca import (
												    FlaxWavaVecaForCTC,
												    FlaxWavaVecaForPreTraining,
												    FlaxWavaVecaModel,
												    FlaxWavaVecaPreTrainedModel,
												)


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
from ...utils import logging


_A     :			str  						=			logging.get_logger(__name__)

_A     :			Tuple  						=			{
    """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 						a__		( a_		):
   __lowerCAmelCase				=							"""big_bird"""
   def __init__(     self      ,       _a=50_358      ,       _a=768      ,       _a=12      ,       _a=12      ,       _a=3_072      ,       _a="gelu_new"      ,       _a=0.1      ,       _a=0.1      ,       _a=4_096      ,       _a=2      ,       _a=0.0_2      ,       _a=1E-12      ,       _a=True      ,       _a=0      ,       _a=1      ,       _a=2      ,       _a=66      ,       _a="block_sparse"      ,       _a=True      ,       _a=False      ,       _a=64      ,       _a=3      ,       _a=None      ,       **_a      ,       ):
          super().__init__(
              pad_token_id=_a      ,       bos_token_id=_a      ,       eos_token_id=_a      ,       sep_token_id=_a      ,       **_a      ,       )

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

          lowercase       :							Any									=				rescale_embeddings
          lowercase       :							List[str]									=				attention_type
          lowercase       :							int									=				use_bias
          lowercase       :							Dict									=				block_size
          lowercase       :							List[str]									=				num_random_blocks
          lowercase       :							int									=				classifier_dropout


class 						a__		( a_		):
   @property
   def      __magic_name__     (     self							):
          if self.task == "multiple-choice":
                 lowercase       :							Union[str, Any]									=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase       :							Dict									=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]							)


