





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


a__					:     int							=				logging.get_logger(__name__)

a__					:     Dict							=				{
    """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							):
       snake_case__				:							Dict											=     """rembert"""

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=25_0300   ,					__SCREAMING_SNAKE_CASE=1152   ,					__SCREAMING_SNAKE_CASE=32   ,					__SCREAMING_SNAKE_CASE=18   ,					__SCREAMING_SNAKE_CASE=256   ,					__SCREAMING_SNAKE_CASE=1664   ,					__SCREAMING_SNAKE_CASE=4608   ,					__SCREAMING_SNAKE_CASE="gelu"   ,					__SCREAMING_SNAKE_CASE=0.0   ,					__SCREAMING_SNAKE_CASE=0.0   ,					__SCREAMING_SNAKE_CASE=0.1   ,					__SCREAMING_SNAKE_CASE=512   ,					__SCREAMING_SNAKE_CASE=2   ,					__SCREAMING_SNAKE_CASE=0.02   ,					__SCREAMING_SNAKE_CASE=1e-12   ,					__SCREAMING_SNAKE_CASE=True   ,					__SCREAMING_SNAKE_CASE=0   ,					__SCREAMING_SNAKE_CASE=312   ,					__SCREAMING_SNAKE_CASE=313   ,					**__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

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

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



class      lowercase_				(     __lowerCAmelCase							):

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        return 1e-4








"""simple docstring"""


from typing import TYPE_CHECKING

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


a__					:     List[Any]							=				{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				["""BartTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[Any]							=				[
       """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BartForCausalLM""",
       """BartForConditionalGeneration""",
       """BartForQuestionAnswering""",
       """BartForSequenceClassification""",
       """BartModel""",
       """BartPretrainedModel""",
       """PretrainedBartModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[str]							=				[
       """TFBartForConditionalGeneration""",
       """TFBartForSequenceClassification""",
       """TFBartModel""",
       """TFBartPretrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				[
       """FlaxBartDecoderPreTrainedModel""",
       """FlaxBartForCausalLM""",
       """FlaxBartForConditionalGeneration""",
       """FlaxBartForQuestionAnswering""",
       """FlaxBartForSequenceClassification""",
       """FlaxBartModel""",
       """FlaxBartPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
   from .tokenization_bart import BartTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bart_fast import BartTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bart import (
          BART_PRETRAINED_MODEL_ARCHIVE_LIST,
          BartForCausalLM,
          BartForConditionalGeneration,
          BartForQuestionAnswering,
          BartForSequenceClassification,
          BartModel,
          BartPretrainedModel,
          PretrainedBartModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bart import (
          TFBartForConditionalGeneration,
          TFBartForSequenceClassification,
          TFBartModel,
          TFBartPretrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bart import (
          FlaxBartDecoderPreTrainedModel,
          FlaxBartForCausalLM,
          FlaxBartForConditionalGeneration,
          FlaxBartForQuestionAnswering,
          FlaxBartForSequenceClassification,
          FlaxBartModel,
          FlaxBartPreTrainedModel,
      )

else:
   import sys

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

