
"""simple docstring"""


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


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE      =     {
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Dict   =			'''biogpt'''






       def __init__(  self :							Optional[int]       ,      UpperCAmelCase_ :							Any=42_384       ,      UpperCAmelCase_ :							Dict=1_024       ,      UpperCAmelCase_ :							List[Any]=24       ,      UpperCAmelCase_ :							Any=16       ,      UpperCAmelCase_ :							str=4_096       ,      UpperCAmelCase_ :							Union[str, Any]="gelu"       ,      UpperCAmelCase_ :							Any=0.1       ,      UpperCAmelCase_ :							Tuple=0.1       ,      UpperCAmelCase_ :							Optional[Any]=1_024       ,      UpperCAmelCase_ :							List[str]=0.02       ,      UpperCAmelCase_ :							Union[str, Any]=1e-12       ,      UpperCAmelCase_ :							List[str]=True       ,      UpperCAmelCase_ :							int=True       ,      UpperCAmelCase_ :							str=0.0       ,      UpperCAmelCase_ :							Optional[int]=0.0       ,      UpperCAmelCase_ :							Optional[Any]=1       ,      UpperCAmelCase_ :							Union[str, Any]=0       ,      UpperCAmelCase_ :							Optional[int]=2       ,      **UpperCAmelCase_ :							Any       ,      )->  int:







              """simple docstring"""
              UpperCamelCase					 =		vocab_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					 =		initializer_range
              UpperCamelCase					 =		layer_norm_eps
              UpperCamelCase					 =		scale_embedding
              UpperCamelCase					 =		use_cache
              UpperCamelCase					 =		layerdrop
              UpperCamelCase					 =		activation_dropout
              super().__init__(pad_token_id=UpperCAmelCase_       ,      bos_token_id=UpperCAmelCase_       ,      eos_token_id=UpperCAmelCase_       ,      **UpperCAmelCase_			)







"""simple docstring"""


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE      =     {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizer"""]

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MBartForCausalLM""",
          """MBartForConditionalGeneration""",
          """MBartForQuestionAnswering""",
          """MBartForSequenceClassification""",
          """MBartModel""",
          """MBartPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """TFMBartForConditionalGeneration""",
          """TFMBartModel""",
          """TFMBartPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """FlaxMBartForConditionalGeneration""",
          """FlaxMBartForQuestionAnswering""",
          """FlaxMBartForSequenceClassification""",
          """FlaxMBartModel""",
          """FlaxMBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

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





