
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase           =logging.get_logger(__name__)

_lowerCamelCase           ={
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}





class 							A__	(			__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					Tuple					 =    """qdqbert"""
      def __init__(   self					,						__magic_name__=3_0_5_2_2					,						__magic_name__=7_6_8					,						__magic_name__=1_2					,						__magic_name__=1_2					,						__magic_name__=3_0_7_2					,						__magic_name__="gelu"					,						__magic_name__=0.1					,						__magic_name__=0.1					,						__magic_name__=5_1_2					,						__magic_name__=2					,						__magic_name__=0.02					,						__magic_name__=1e-12					,						__magic_name__=True					,						__magic_name__=1					,						__magic_name__=0					,						__magic_name__=2					,						**__magic_name__					,						):
       super().__init__(pad_token_id=__magic_name__					,						bos_token_id=__magic_name__					,						eos_token_id=__magic_name__					,						**__magic_name__   )

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






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,
)


_lowerCamelCase           ={
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =[
          """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:
      _lowerCamelCase           =[
          """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:
      _lowerCamelCase           =["""TFGPT2Tokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =["""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

      _lowerCamelCase           =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




