code: 
  
  
  # Copyright 2023 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowercase_								=    {
      'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
      'feature_extraction_tvlt': ['TvltFeatureExtractor'],
      'processing_tvlt': ['TvltProcessor'],
  }
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    [
  		    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
  		    'TvltModel',
  		    'TvltForPreTraining',
  		    'TvltForAudioVisualClassification',
  		    'TvltPreTrainedModel',
  		]
  
  try:
  		if not is_vision_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    ['TvltImageProcessor']
  
  
  if TYPE_CHECKING:
  		from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  		from .processing_tvlt import TvltProcessor
  		from .feature_extraction_tvlt import TvltFeatureExtractor
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tvlt import (
  				    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    TvltForAudioVisualClassification,
  				    TvltForPreTraining,
  				    TvltModel,
  				    TvltPreTrainedModel,
  				)
  
  		try:
  				if not is_vision_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .image_processing_tvlt import TvltImageProcessor
  
  
  else:
  		import sys
  
  		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  
  
  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 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  "led"
  							_A  				=  {
  							    "num_attention_heads": "encoder_attention_heads",
  							    "hidden_size": "d_model",
  							    "attention_probs_dropout_prob": "attention_dropout",
  							    "initializer_range": "init_std",
  							}
  
  
  
  
  
  
  
  							def __init__(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]=5_0_2_6_5		,				SCREAMING_SNAKE_CASE_  :				str=1_6_3_8_4		,				SCREAMING_SNAKE_CASE_  :				List[Any]=1_0_2_4		,				SCREAMING_SNAKE_CASE_  :				List[str]=1_2		,				SCREAMING_SNAKE_CASE_  :				Any=4_0_9_6		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=1_6		,				SCREAMING_SNAKE_CASE_  :				str=1_2		,				SCREAMING_SNAKE_CASE_  :				List[Any]=4_0_9_6		,				SCREAMING_SNAKE_CASE_  :				List[str]=1_6		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=0.0		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=0.0		,				SCREAMING_SNAKE_CASE_  :				List[Any]=True		,				SCREAMING_SNAKE_CASE_  :				str=True		,				SCREAMING_SNAKE_CASE_  :				List[str]="gelu"		,				SCREAMING_SNAKE_CASE_  :				List[Any]=1_0_2_4		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.1		,				SCREAMING_SNAKE_CASE_  :				Dict=0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=0.0		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.02		,				SCREAMING_SNAKE_CASE_  :				str=2		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=1		,				SCREAMING_SNAKE_CASE_  :				str=0		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=2		,				SCREAMING_SNAKE_CASE_  :				Union[List[int], int] = 5_1_2		,				**SCREAMING_SNAKE_CASE_  :				Dict		,				):
  													_a     =			vocab_size
  													_a     =			max_encoder_position_embeddings
  													_a     =			max_decoder_position_embeddings
  													_a     =			d_model
  													_a     =			encoder_ffn_dim
  													_a     =			encoder_layers
  													_a     =			encoder_attention_heads
  													_a     =			decoder_ffn_dim
  													_a     =			decoder_layers
  													_a     =			decoder_attention_heads
  													_a     =			dropout
  													_a     =			attention_dropout
  													_a     =			activation_dropout
  													_a     =			activation_function
  													_a     =			init_std
  													_a     =			encoder_layerdrop
  													_a     =			decoder_layerdrop
  													_a     =			classifier_dropout
  													_a     =			use_cache
  													_a     =			encoder_layers
  													_a     =			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_		,				)
  
  
  