





from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}

class        snake_case_    (		__snake_case						):
  A_      							=   'data2vec-text'






  def __init__(							self			:					Tuple       ,  _snake_case			:					List[Any]=30522       ,  _snake_case			:					Any=768       ,  _snake_case			:					Optional[Any]=12       ,  _snake_case			:					Union[str, Any]=12       ,  _snake_case			:					Optional[int]=3072       ,  _snake_case			:					Tuple="gelu"       ,  _snake_case			:					Tuple=0.1       ,  _snake_case			:					int=0.1       ,  _snake_case			:					Any=512       ,  _snake_case			:					List[str]=2       ,  _snake_case			:					int=0.02       ,  _snake_case			:					Tuple=1E-12       ,  _snake_case			:					Optional[Any]=1       ,  _snake_case			:					int=0       ,  _snake_case			:					int=2       ,  _snake_case			:					Any="absolute"       ,  _snake_case			:					Optional[Any]=True       ,  _snake_case			:					List[Any]=None       ,  **_snake_case			:					Any       ,  )->str:






   '''simple docstring'''

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

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



class        snake_case_    (		__snake_case						):






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






   '''simple docstring'''

   if self.task == "multiple-choice":
    __lowerCAmelCase					:	str      =       {0: """batch""", 1: """choice""", 2: """sequence"""}
   else:
    __lowerCAmelCase					:	Tuple      =       {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_torch_available,
)


_UpperCAmelCase             =							{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCAmelCase             =							[
          'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
          'GPTBigCodeForSequenceClassification',
          'GPTBigCodeForTokenClassification',
          'GPTBigCodeForCausalLM',
          'GPTBigCodeModel',
          'GPTBigCodePreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_bigcode import (
                GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTBigCodeForCausalLM,
                GPTBigCodeForSequenceClassification,
                GPTBigCodeForTokenClassification,
                GPTBigCodeModel,
                GPTBigCodePreTrainedModel,
            )


else:
      import sys

      _UpperCAmelCase             =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)