




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__       				=	{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class 							__lowercase   (			lowerCamelCase__    ):
     _lowerCAmelCase		       =						"""rembert"""



     def __init__(  self   :							Any      ,		lowercase__   :							Dict=2_5_0_3_0_0      ,		lowercase__   :							Optional[int]=1_1_5_2      ,		lowercase__   :							int=3_2      ,		lowercase__   :							Tuple=1_8      ,		lowercase__   :							int=2_5_6      ,		lowercase__   :							Optional[Any]=1_6_6_4      ,		lowercase__   :							Union[str, Any]=4_6_0_8      ,		lowercase__   :							Optional[Any]="gelu"      ,		lowercase__   :							Optional[int]=0.0      ,		lowercase__   :							List[str]=0.0      ,		lowercase__   :							List[Any]=0.1      ,		lowercase__   :							Optional[Any]=5_1_2      ,		lowercase__   :							Union[str, Any]=2      ,		lowercase__   :							Optional[Any]=0.02      ,		lowercase__   :							Tuple=1e-12      ,		lowercase__   :							Optional[Any]=True      ,		lowercase__   :							Optional[int]=0      ,		lowercase__   :							Tuple=3_1_2      ,		lowercase__   :							Optional[int]=3_1_3      ,		**lowercase__   :							Optional[Any]      ,		):
       super().__init__(pad_token_id=snake_case__      ,		bos_token_id=snake_case__      ,		eos_token_id=snake_case__      ,		**snake_case__       )

       a_		    =  vocab_size
       a_		    =  input_embedding_size
       a_		    =  output_embedding_size
       a_		    =  max_position_embeddings
       a_		    =  hidden_size
       a_		    =  num_hidden_layers
       a_		    =  num_attention_heads
       a_		    =  intermediate_size
       a_		    =  hidden_act
       a_		    =  hidden_dropout_prob
       a_		    =  attention_probs_dropout_prob
       a_		    =  classifier_dropout_prob
       a_		    =  initializer_range
       a_		    =  type_vocab_size
       a_		    =  layer_norm_eps
       a_		    =  use_cache
       a_		    =  False



class 							__lowercase   (			lowerCamelCase__    ):



     @property
     def 				__magic_name__		(  self   :							str       ):





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



     @property
     def 				__magic_name__		(  self   :							List[str]       ):
       return 1e-4

from typing import TYPE_CHECKING

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


__snake_case         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTJForCausalLM''',
         '''GPTJForQuestionAnswering''',
         '''GPTJForSequenceClassification''',
         '''GPTJModel''',
         '''GPTJPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''TFGPTJForCausalLM''',
         '''TFGPTJForQuestionAnswering''',
         '''TFGPTJForSequenceClassification''',
         '''TFGPTJModel''',
         '''TFGPTJPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''FlaxGPTJForCausalLM''',
         '''FlaxGPTJModel''',
         '''FlaxGPTJPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

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