

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


a    :Any			=       logging.get_logger(__name__)

a    :str			=       {
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}

class    __a							(UpperCAmelCase__):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE  :Optional[Any]				=       """roberta-prelayernorm"""

     def __init__(  self		,			_a=50_265		,			_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-1_2		,			_a=1		,			_a=0		,			_a=2		,			_a="absolute"		,			_a=True		,			_a=None		,			**_a		,			)     ->					Optional[int]:


           """simple docstring"""


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

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





class    __a							(UpperCAmelCase__):






     '''simple docstring'''

     @property
     def     _a	(  self     )     ->					Dict:


           """simple docstring"""


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












"""simple docstring"""






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


UpperCAmelCase_   : str   			= {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Dict   			= ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : List[str]   			= [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_   : Tuple   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


