from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__	:  Optional[int]						=  logging.get_logger(__name__)

lowerCAmelCase__	:  List[str]						=  {
    '''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 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  """plbart"""
					__lowerCamelCase			   =  ["""past_key_values"""]
					__lowerCamelCase			   =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




					def __init__(  self       ,			__UpperCamelCase=50005       ,			__UpperCamelCase=1024       ,			__UpperCamelCase=6       ,			__UpperCamelCase=3072       ,			__UpperCamelCase=12       ,			__UpperCamelCase=6       ,			__UpperCamelCase=3072       ,			__UpperCamelCase=12       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=True       ,			__UpperCamelCase=True       ,			__UpperCamelCase="gelu"       ,			__UpperCamelCase=768       ,			__UpperCamelCase=0.1       ,			__UpperCamelCase=0.1       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0_2       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=True       ,			__UpperCamelCase=1       ,			__UpperCamelCase=0       ,			__UpperCamelCase=2       ,			__UpperCamelCase=2       ,			**__UpperCamelCase       ,			)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= vocab_size
									snake_case__   :      Optional[Any]   				= max_position_embeddings
									snake_case__   :      Optional[int]   				= d_model
									snake_case__   :      Tuple   				= encoder_ffn_dim
									snake_case__   :      Any   				= encoder_layers
									snake_case__   :      Optional[int]   				= encoder_attention_heads
									snake_case__   :      Optional[Any]   				= decoder_ffn_dim
									snake_case__   :      int   				= decoder_layers
									snake_case__   :      str   				= decoder_attention_heads
									snake_case__   :      Optional[Any]   				= dropout
									snake_case__   :      Optional[Any]   				= attention_dropout
									snake_case__   :      Tuple   				= activation_dropout
									snake_case__   :      List[str]   				= activation_function
									snake_case__   :      Optional[Any]   				= init_std
									snake_case__   :      Dict   				= encoder_layerdrop
									snake_case__   :      Any   				= decoder_layerdrop
									snake_case__   :      List[Any]   				= classifier_dropout
									snake_case__   :      List[Any]   				= use_cache
									snake_case__   :      Tuple   				= encoder_layers
									snake_case__   :      int   				= scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__UpperCamelCase       ,			bos_token_id=__UpperCamelCase       ,			eos_token_id=__UpperCamelCase       ,			is_encoder_decoder=__UpperCamelCase       ,			forced_eos_token_id=__UpperCamelCase       ,			**__UpperCamelCase       ,			)





class 					__snake_case    (     _lowerCamelCase      ):




					@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


lowerCAmelCase__	:  int						=  {
    '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Union[str, Any]						=  ['''DetaImageProcessor''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Optional[int]						=  [
				    '''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

				lowerCAmelCase__	:  int						=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


