
from typing import TYPE_CHECKING

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


_UpperCAmelCase      :				List[Any]          = {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase      :				Tuple          = ["LongformerTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase      :				Tuple          = [
					    "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "LongformerForMaskedLM",
					    "LongformerForMultipleChoice",
					    "LongformerForQuestionAnswering",
					    "LongformerForSequenceClassification",
					    "LongformerForTokenClassification",
					    "LongformerModel",
					    "LongformerPreTrainedModel",
					    "LongformerSelfAttention",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase      :				str          = [
					    "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFLongformerForMaskedLM",
					    "TFLongformerForMultipleChoice",
					    "TFLongformerForQuestionAnswering",
					    "TFLongformerForSequenceClassification",
					    "TFLongformerForTokenClassification",
					    "TFLongformerModel",
					    "TFLongformerPreTrainedModel",
					    "TFLongformerSelfAttention",
					]


if TYPE_CHECKING:
					from .configuration_longformer import (
					    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    LongformerConfig,
					    LongformerOnnxConfig,
					)
					from .tokenization_longformer import LongformerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_longformer_fast import LongformerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_longformer import (
										    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LongformerForMaskedLM,
										    LongformerForMultipleChoice,
										    LongformerForQuestionAnswering,
										    LongformerForSequenceClassification,
										    LongformerForTokenClassification,
										    LongformerModel,
										    LongformerPreTrainedModel,
										    LongformerSelfAttention,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_longformer import (
										    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLongformerForMaskedLM,
										    TFLongformerForMultipleChoice,
										    TFLongformerForQuestionAnswering,
										    TFLongformerForSequenceClassification,
										    TFLongformerForTokenClassification,
										    TFLongformerModel,
										    TFLongformerPreTrainedModel,
										    TFLongformerSelfAttention,
										)

else:
					import sys

					_UpperCAmelCase      :				Optional[Any]          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				Union[str, Any]          = {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       "data2vec-text"



		def __init__(				self       ,   A_=30_522       ,   A_=768       ,   A_=12       ,   A_=12       ,   A_=3_072       ,   A_="gelu"       ,   A_=0.1       ,   A_=0.1       ,   A_=512       ,   A_=2       ,   A_=0.02       ,   A_=1e-12       ,   A_=1       ,   A_=0       ,   A_=2       ,   A_="absolute"       ,   A_=True       ,   A_=None       ,   **A_       ,   )						->      Any:
				"""simple docstring"""
				super().__init__(pad_token_id=A_       ,   bos_token_id=A_       ,   eos_token_id=A_       ,   **A_ )

				UpperCamelCase						=  vocab_size
				UpperCamelCase						=  hidden_size
				UpperCamelCase						=  num_hidden_layers
				UpperCamelCase						=  num_attention_heads
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  intermediate_size
				UpperCamelCase						=  hidden_dropout_prob
				UpperCamelCase						=  attention_probs_dropout_prob
				UpperCamelCase						=  max_position_embeddings
				UpperCamelCase						=  type_vocab_size
				UpperCamelCase						=  initializer_range
				UpperCamelCase						=  layer_norm_eps
				UpperCamelCase						=  position_embedding_type
				UpperCamelCase						=  use_cache
				UpperCamelCase						=  classifier_dropout






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		@property
		def __UpperCamelCase				(				self )						->      Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task == "multiple-choice":
						UpperCamelCase						=  {0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
						UpperCamelCase						=  {0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				    ] )
