





from typing import List, Union

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


__lowercase				    =      logging.get_logger(__name__)

__lowercase				    =      {
    """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/config.json""",
    # See all LED models at https://huggingface.co/models?filter=led
}

class 	__snake_case					(      _a ):






    '''simple docstring'''
    _lowerCAmelCase  =						"""led"""
    _lowerCAmelCase  =						{
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
        """initializer_range""": """init_std""",
    }





    def __init__( self    :		Optional[Any]      ,    snake_case    :		Tuple=50_265      ,    snake_case    :		Any=16_384      ,    snake_case    :		List[Any]=1_024      ,    snake_case    :		List[Any]=12      ,    snake_case    :		List[str]=4_096      ,    snake_case    :		str=16      ,    snake_case    :		Any=12      ,    snake_case    :		Optional[int]=4_096      ,    snake_case    :		Optional[Any]=16      ,    snake_case    :		List[str]=0.0      ,    snake_case    :		List[str]=0.0      ,    snake_case    :		int=True      ,    snake_case    :		List[Any]=True      ,    snake_case    :		int="gelu"      ,    snake_case    :		List[str]=1_024      ,    snake_case    :		int=0.1      ,    snake_case    :		str=0.0      ,    snake_case    :		Optional[Any]=0.0      ,    snake_case    :		Optional[int]=0.02      ,    snake_case    :		Optional[int]=2      ,    snake_case    :		int=0.0      ,    snake_case    :		int=1      ,    snake_case    :		Tuple=0      ,    snake_case    :		str=2      ,    snake_case    :		Union[List[int], int] = 512      ,    **snake_case    :		Any      ,    ):

      """simple docstring"""





      __lowerCAmelCase								=					vocab_size
      __lowerCAmelCase								=					max_encoder_position_embeddings
      __lowerCAmelCase								=					max_decoder_position_embeddings
      __lowerCAmelCase								=					d_model
      __lowerCAmelCase								=					encoder_ffn_dim
      __lowerCAmelCase								=					encoder_layers
      __lowerCAmelCase								=					encoder_attention_heads
      __lowerCAmelCase								=					decoder_ffn_dim
      __lowerCAmelCase								=					decoder_layers
      __lowerCAmelCase								=					decoder_attention_heads
      __lowerCAmelCase								=					dropout
      __lowerCAmelCase								=					attention_dropout
      __lowerCAmelCase								=					activation_dropout
      __lowerCAmelCase								=					activation_function
      __lowerCAmelCase								=					init_std
      __lowerCAmelCase								=					encoder_layerdrop
      __lowerCAmelCase								=					decoder_layerdrop
      __lowerCAmelCase								=					classifier_dropout
      __lowerCAmelCase								=					use_cache
      __lowerCAmelCase								=					encoder_layers
      __lowerCAmelCase								=					attention_window

      super().__init__(
          pad_token_id=snake_case      ,    bos_token_id=snake_case      ,    eos_token_id=snake_case      ,    is_encoder_decoder=snake_case      ,    decoder_start_token_id=snake_case      ,    **snake_case      ,    )







from typing import TYPE_CHECKING

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


__lowercase				    =      {
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

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

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

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

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

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