

from typing import TYPE_CHECKING

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


_UpperCamelCase			       =			{
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase			       =			['''GPT2TokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase			       =			[
     '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GPT2DoubleHeadsModel''',
     '''GPT2ForQuestionAnswering''',
     '''GPT2ForSequenceClassification''',
     '''GPT2ForTokenClassification''',
     '''GPT2LMHeadModel''',
     '''GPT2Model''',
     '''GPT2PreTrainedModel''',
     '''load_tf_weights_in_gpt2''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase			       =			[
     '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFGPT2DoubleHeadsModel''',
     '''TFGPT2ForSequenceClassification''',
     '''TFGPT2LMHeadModel''',
     '''TFGPT2MainLayer''',
     '''TFGPT2Model''',
     '''TFGPT2PreTrainedModel''',
 ]

try:
 if not is_keras_nlp_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase			       =			['''TFGPT2Tokenizer''']

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase			       =			['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
 from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
 from .tokenization_gpta import GPTaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_fast import GPTaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpta import (
      GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTaDoubleHeadsModel,
      GPTaForQuestionAnswering,
      GPTaForSequenceClassification,
      GPTaForTokenClassification,
      GPTaLMHeadModel,
      GPTaModel,
      GPTaPreTrainedModel,
      load_tf_weights_in_gpta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gpta import (
      TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGPTaDoubleHeadsModel,
      TFGPTaForSequenceClassification,
      TFGPTaLMHeadModel,
      TFGPTaMainLayer,
      TFGPTaModel,
      TFGPTaPreTrainedModel,
  )

 try:
  if not is_keras_nlp_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_tf import TFGPTaTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
 import sys

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






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						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""rembert"""




 def __init__(self       ,       __a=250300       ,       __a=1152       ,       __a=32       ,       __a=18       ,       __a=256       ,       __a=1664       ,       __a=4608       ,       __a="gelu"       ,       __a=0.0       ,       __a=0.0       ,       __a=0.1       ,       __a=512       ,       __a=2       ,       __a=0.02       ,       __a=1E-1_2       ,       __a=True       ,       __a=0       ,       __a=312       ,       __a=313       ,       **__a       ,       ) ->					str:






  """simple docstring"""





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

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







class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


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






  """simple docstring"""





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




 @property
 def   UpperCamelCase__							(self ) ->					float:






  """simple docstring"""





  return 1E-4



