




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'}),
							    ]			)






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__)
