code: 
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					{
      "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
      # See all PLBART models at https://huggingface.co/models?filter=plbart
  }
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   '''plbart'''
  	A						       =   ['''past_key_values''']
  	A						       =   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE=5_0005			,    SCREAMING_SNAKE_CASE=1024			,    SCREAMING_SNAKE_CASE=6			,    SCREAMING_SNAKE_CASE=3072			,    SCREAMING_SNAKE_CASE=12			,    SCREAMING_SNAKE_CASE=6			,    SCREAMING_SNAKE_CASE=3072			,    SCREAMING_SNAKE_CASE=12			,    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=768			,    SCREAMING_SNAKE_CASE=0.1			,    SCREAMING_SNAKE_CASE=0.1			,    SCREAMING_SNAKE_CASE=0.0			,    SCREAMING_SNAKE_CASE=0.0_2			,    SCREAMING_SNAKE_CASE=0.0			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=1			,    SCREAMING_SNAKE_CASE=0			,    SCREAMING_SNAKE_CASE=2			,    SCREAMING_SNAKE_CASE=2			,    **SCREAMING_SNAKE_CASE			,    )     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	vocab_size
  				SCREAMING_SNAKE_CASE_			=	max_position_embeddings
  				SCREAMING_SNAKE_CASE_			=	d_model
  				SCREAMING_SNAKE_CASE_			=	encoder_ffn_dim
  				SCREAMING_SNAKE_CASE_			=	encoder_layers
  				SCREAMING_SNAKE_CASE_			=	encoder_attention_heads
  				SCREAMING_SNAKE_CASE_			=	decoder_ffn_dim
  				SCREAMING_SNAKE_CASE_			=	decoder_layers
  				SCREAMING_SNAKE_CASE_			=	decoder_attention_heads
  				SCREAMING_SNAKE_CASE_			=	dropout
  				SCREAMING_SNAKE_CASE_			=	attention_dropout
  				SCREAMING_SNAKE_CASE_			=	activation_dropout
  				SCREAMING_SNAKE_CASE_			=	activation_function
  				SCREAMING_SNAKE_CASE_			=	init_std
  				SCREAMING_SNAKE_CASE_			=	encoder_layerdrop
  				SCREAMING_SNAKE_CASE_			=	decoder_layerdrop
  				SCREAMING_SNAKE_CASE_			=	classifier_dropout
  				SCREAMING_SNAKE_CASE_			=	use_cache
  				SCREAMING_SNAKE_CASE_			=	encoder_layers
  				SCREAMING_SNAKE_CASE_			=	scale_embedding  # scale factor will be sqrt(d_model) if True
  				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			,    forced_eos_token_id=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			,    )
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	@property
  	def 	A_(       self			)     ->				Mapping[str, Mapping[int, str]]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return OrderedDict(
  				    [
  				        ('input_ids', {0: 'batch', 1: 'sequence'}),
  				        ('attention_mask', {0: 'batch', 1: 'sequence'}),
  				    ]			)
  
  
  
  
  
  	@property
  	def 	A_(       self			)     ->				Mapping[str, Mapping[int, str]]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if self.use_past:
  							return OrderedDict(
  							    [
  							        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
  							        ('past_keys', {0: 'batch', 2: 'sequence'}),
  							        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
  							    ]			)
  				else:
  							return OrderedDict(
  							    [
  							        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
  							        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
  							    ]			)
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
  				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  