"""simple docstring"""







from typing import List, Union

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


_SCREAMING_SNAKE_CASE	:		Dict   =   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:		Tuple   =   {
    '''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      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Optional[int]    =    "led"
   __UpperCAmelCase	:							List[str]    =    {
       "num_attention_heads": "encoder_attention_heads",
       "hidden_size": "d_model",
       "attention_probs_dropout_prob": "attention_dropout",
       "initializer_range": "init_std",
   }







   def __init__(	self ,				_SCREAMING_SNAKE_CASE=50265 ,				_SCREAMING_SNAKE_CASE=16384 ,				_SCREAMING_SNAKE_CASE=1024 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE=4096 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE=4096 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=1024 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=1 ,				_SCREAMING_SNAKE_CASE=0 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE = 512 ,				**_SCREAMING_SNAKE_CASE ,				):


      '''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=_SCREAMING_SNAKE_CASE ,				bos_token_id=_SCREAMING_SNAKE_CASE ,				eos_token_id=_SCREAMING_SNAKE_CASE ,				is_encoder_decoder=_SCREAMING_SNAKE_CASE ,				decoder_start_token_id=_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE ,				)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowerCamelCase							=      {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase							=      [
        '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CvtForImageClassification''',
        '''CvtModel''',
        '''CvtPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase							=      [
        '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFCvtForImageClassification''',
        '''TFCvtModel''',
        '''TFCvtPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cvt import (
            CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CvtForImageClassification,
            CvtModel,
            CvtPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_cvt import (
            TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCvtForImageClassification,
            TFCvtModel,
            TFCvtPreTrainedModel,
        )


else:
    import sys

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