





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


__UpperCamelCase:  Union[str, Any]   =   logging.get_logger(__name__)

__UpperCamelCase:  Union[str, 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 			__lowerCAmelCase				(					A_			):


	'''simple docstring'''





	_A       	=							"plbart"
	_A       	=							["past_key_values"]
	_A       	=							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

	def __init__(	self:	List[Any],				lowerCamelCase_:	int=50005,				lowerCamelCase_:	Tuple=1024,				lowerCamelCase_:	Optional[int]=6,				lowerCamelCase_:	Any=3072,				lowerCamelCase_:	Any=12,				lowerCamelCase_:	List[str]=6,				lowerCamelCase_:	Any=3072,				lowerCamelCase_:	Optional[int]=12,				lowerCamelCase_:	Any=0.0,				lowerCamelCase_:	Optional[Any]=0.0,				lowerCamelCase_:	str=True,				lowerCamelCase_:	List[Any]=True,				lowerCamelCase_:	List[str]="gelu",				lowerCamelCase_:	List[Any]=768,				lowerCamelCase_:	Tuple=0.1,				lowerCamelCase_:	Optional[Any]=0.1,				lowerCamelCase_:	int=0.0,				lowerCamelCase_:	Any=0.0_2,				lowerCamelCase_:	int=0.0,				lowerCamelCase_:	Dict=True,				lowerCamelCase_:	Any=1,				lowerCamelCase_:	Optional[Any]=0,				lowerCamelCase_:	List[str]=2,				lowerCamelCase_:	Optional[Any]=2,				**lowerCamelCase_:	int,				):
								lowercase__				:			Tuple            =		vocab_size
								lowercase__				:			Tuple            =		max_position_embeddings
								lowercase__				:			Union[str, Any]            =		d_model
								lowercase__				:			int            =		encoder_ffn_dim
								lowercase__				:			Tuple            =		encoder_layers
								lowercase__				:			Optional[int]            =		encoder_attention_heads
								lowercase__				:			List[str]            =		decoder_ffn_dim
								lowercase__				:			Union[str, Any]            =		decoder_layers
								lowercase__				:			str            =		decoder_attention_heads
								lowercase__				:			List[Any]            =		dropout
								lowercase__				:			Optional[Any]            =		attention_dropout
								lowercase__				:			Optional[int]            =		activation_dropout
								lowercase__				:			Dict            =		activation_function
								lowercase__				:			Tuple            =		init_std
								lowercase__				:			List[Any]            =		encoder_layerdrop
								lowercase__				:			Union[str, Any]            =		decoder_layerdrop
								lowercase__				:			Union[str, Any]            =		classifier_dropout
								lowercase__				:			List[Any]            =		use_cache
								lowercase__				:			Any            =		encoder_layers
								lowercase__				:			Optional[int]            =		scale_embedding  # scale factor will be sqrt(d_model) if True
								super().__init__(
								    pad_token_id=lowerCamelCase_,				bos_token_id=lowerCamelCase_,				eos_token_id=lowerCamelCase_,				is_encoder_decoder=lowerCamelCase_,				forced_eos_token_id=lowerCamelCase_,				**lowerCamelCase_,				)


class 			__lowerCAmelCase				(					A_			):


	'''simple docstring'''





	@property
	def       snake_case__(	self:	Optional[int] ):
								return OrderedDict(
								    [
								        ('input_ids', {0: 'batch', 1: 'sequence'}),
								        ('attention_mask', {0: 'batch', 1: 'sequence'}),
								    ] )





	@property
	def       snake_case__(	self:	Tuple ):



								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'}),
															    ] )









from typing import TYPE_CHECKING

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


__UpperCamelCase:  Any   =   {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   ["""DetaImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  List[str]   =   [
			    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DetaForObjectDetection""",
			    """DetaModel""",
			    """DetaPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_deta import DetaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deta import (
						    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DetaForObjectDetection,
						    DetaModel,
						    DetaPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:  int   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


