



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_   :Dict        =      {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""data2vec-text"""






     def __init__(       self  :  str, _snake_case  :  Optional[int]=3_0_5_2_2, _snake_case  :  Optional[Any]=7_6_8, _snake_case  :  Any=1_2, _snake_case  :  str=1_2, _snake_case  :  str=3_0_7_2, _snake_case  :  List[Any]="gelu", _snake_case  :  Optional[int]=0.1, _snake_case  :  Tuple=0.1, _snake_case  :  Dict=5_1_2, _snake_case  :  Union[str, Any]=2, _snake_case  :  Optional[Any]=0.0_2, _snake_case  :  List[str]=1e-12, _snake_case  :  Dict=1, _snake_case  :  str=0, _snake_case  :  Optional[int]=2, _snake_case  :  int="absolute", _snake_case  :  Any=True, _snake_case  :  Any=None, **_snake_case  :  Union[str, Any], )   ->int:
            super().__init__(pad_token_id=_snake_case, bos_token_id=_snake_case, eos_token_id=_snake_case, **_snake_case					)

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




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


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











from typing import TYPE_CHECKING

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


a_   :int        =      {
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongT5EncoderModel",
     "LongT5ForConditionalGeneration",
     "LongT5Model",
     "LongT5PreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      [
     "FlaxLongT5ForConditionalGeneration",
     "FlaxLongT5Model",
     "FlaxLongT5PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longta import (
      LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongTaEncoderModel,
      LongTaForConditionalGeneration,
      LongTaModel,
      LongTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_longta import (
      FlaxLongTaForConditionalGeneration,
      FlaxLongTaModel,
      FlaxLongTaPreTrainedModel,
  )


else:
 import sys

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






