




"""simple docstring"""






from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
			import timm


if is_torch_available():
			from torch import Tensor






class lowerCAmelCase__	(     lowerCAmelCase__,	lowerCAmelCase__						):






			'''simple docstring'''



			lowerCamelCase__      =			"""pixel_values"""
			lowerCamelCase__      =			False
			lowerCamelCase__      =			TimmBackboneConfig
			def __init__(  self  ,      lowercase  ,      **lowercase  ):
							requires_backends(self  ,      'timm'  )
							super().__init__(__a  )
							_lowerCamelCase     :			List[str]    =    config

							if config.backbone is None:
											raise ValueError('backbone is not set in the config. Please set it to a timm model name.'  )

							if config.backbone not in timm.list_models():
											raise ValueError(F'''backbone {config.backbone} is not supported by timm.'''  )

							if hasattr(__a  ,      'out_features'  ) and config.out_features is not None:
											raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'  )

							_lowerCamelCase     :			List[str]    =    getattr(__a  ,      'use_pretrained_backbone'  ,      __a  )
							if pretrained is None:
											raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.'  )

							# We just take the final layer by default. This matches the default for the transformers models.
							_lowerCamelCase     :			Any    =    config.out_indices if getattr(__a  ,      'out_indices'  ,      __a  ) is not None else (-1,)

							_lowerCamelCase     :			List[Any]    =    timm.create_model(
							    config.backbone  ,      pretrained=__a  ,      features_only=config.features_only  ,      in_chans=config.num_channels  ,      out_indices=__a  ,      **__a  ,      )
							# These are used to control the output of the model when called. If output_hidden_states is True, then
							# return_layers is modified to include all layers.
							_lowerCamelCase     :			List[str]    =    self._backbone.return_layers
							_lowerCamelCase     :			List[str]    =    {layer['module']: str(__a  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
							super()._init_backbone(__a  )
			@classmethod
			def 					A_    (  cls  ,      lowercase  ,      *lowercase  ,      **lowercase  ):
							requires_backends(cls  ,      ['vision', 'timm']  )
							from ...models.timm_backbone import TimmBackboneConfig

							_lowerCamelCase     :			Dict    =    kwargs.pop('config'  ,      TimmBackboneConfig()  )

							_lowerCamelCase     :			Dict    =    kwargs.pop('use_timm_backbone'  ,      __a  )
							if not use_timm:
											raise ValueError('use_timm_backbone must be True for timm backbones'  )

							_lowerCamelCase     :			Dict    =    kwargs.pop('num_channels'  ,      config.num_channels  )
							_lowerCamelCase     :			str    =    kwargs.pop('features_only'  ,      config.features_only  )
							_lowerCamelCase     :			Optional[int]    =    kwargs.pop('use_pretrained_backbone'  ,      config.use_pretrained_backbone  )
							_lowerCamelCase     :			Dict    =    kwargs.pop('out_indices'  ,      config.out_indices  )
							_lowerCamelCase     :			Optional[int]    =    TimmBackboneConfig(
							    backbone=__a  ,      num_channels=__a  ,      features_only=__a  ,      use_pretrained_backbone=__a  ,      out_indices=__a  ,      )
							return super()._from_config(__a  ,      **__a  )
			def 					A_    (  self  ,      lowercase  ):
							pass






			def 					A_    (  self  ,      lowercase  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      **lowercase  ):
							_lowerCamelCase     :			Dict    =    return_dict if return_dict is not None else self.config.use_return_dict
							_lowerCamelCase     :			int    =    (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							_lowerCamelCase     :			List[str]    =    output_attentions if output_attentions is not None else self.config.output_attentions

							if output_attentions:
											raise ValueError('Cannot output attentions for timm backbones at the moment'  )

							if output_hidden_states:
											# We modify the return layers to include all the stages of the backbone
											_lowerCamelCase     :			Optional[Any]    =    self._all_layers
											_lowerCamelCase     :			Dict    =    self._backbone(__a  ,      **__a  )
											_lowerCamelCase     :			List[str]    =    self._return_layers
											_lowerCamelCase     :			Any    =    tuple(hidden_states[i] for i in self.out_indices  )
							else:
											_lowerCamelCase     :			Union[str, Any]    =    self._backbone(__a  ,      **__a  )
											_lowerCamelCase     :			List[Any]    =    None

							_lowerCamelCase     :			Tuple    =    tuple(__a  )
							_lowerCamelCase     :			Union[str, Any]    =    tuple(__a  ) if hidden_states is not None else None

							if not return_dict:
											_lowerCamelCase     :			Tuple    =    (feature_maps,)
											if output_hidden_states:
															_lowerCamelCase     :			List[str]    =    output + (hidden_states,)
											return output

							return BackboneOutput(feature_maps=__a  ,      hidden_states=__a  ,      attentions=__a  )



"""simple docstring"""


class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							pass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							pass
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self	):
										__lowerCAmelCase				    =			[
										    [],
										    [],
										    [],
										]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										try:
													if len(self.queues[priority]	) >= 1_00:
																raise OverflowError("Maximum queue size is 100"	)
													self.queues[priority].append(__a	)
										except IndexError:
													raise ValueError("Valid priorities are 0, 1, and 2"	)

							def 			snake_case				(     self	):
										for queue in self.queues:
													if queue:
																return queue.pop(0	)
										raise UnderFlowError("All queues are empty"	)



							def __str__(     self	):
										return "\n".join(f"Priority {i}: {q}" for i, q in enumerate(self.queues	)	)
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self	):
										__lowerCAmelCase				    =			[]

							def 			snake_case				(     self    ,   __a	):
										if len(self.queue	) == 1_00:
													raise OverFlowError("Maximum queue size is 100"	)
										self.queue.append(__a	)

							def 			snake_case				(     self	):
										if not self.queue:
													raise UnderFlowError("The queue is empty"	)
										else:
													__lowerCAmelCase				    =			min(self.queue	)
													self.queue.remove(__a	)
													return data



							def __str__(     self	):
										return str(self.queue	)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			FixedPriorityQueue()
			fpq.enqueue(0    , 10      )
			fpq.enqueue(1    , 70      )
			fpq.enqueue(0    , 100      )
			fpq.enqueue(2    , 1      )
			fpq.enqueue(2    , 5      )
			fpq.enqueue(1    , 7      )
			fpq.enqueue(2    , 4      )
			fpq.enqueue(1    , 64      )
			fpq.enqueue(0    , 128      )
			print(_UpperCamelCase      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(_UpperCamelCase      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )
			print(fpq.dequeue()      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			ElementPriorityQueue()
			epq.enqueue(10      )
			epq.enqueue(70      )
			epq.enqueue(100      )
			epq.enqueue(1      )
			epq.enqueue(5      )
			epq.enqueue(7      )
			epq.enqueue(4      )
			epq.enqueue(64      )
			epq.enqueue(128      )
			print(_UpperCamelCase      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(_UpperCamelCase      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )
			print(epq.dequeue()      )


if __name__ == "__main__":
	fixed_priority_queue()
	element_priority_queue()




