'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A							=   {
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A							=   [
     'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'ResNetForImageClassification',
     'ResNetModel',
     'ResNetPreTrainedModel',
     'ResNetBackbone',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A							=   [
     'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFResNetForImageClassification',
     'TFResNetModel',
     'TFResNetPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A							=   [
     'FlaxResNetForImageClassification',
     'FlaxResNetModel',
     'FlaxResNetPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_resnet import (
      RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      ResNetBackbone,
      ResNetForImageClassification,
      ResNetModel,
      ResNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_resnet import (
      TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFResNetForImageClassification,
      TFResNetModel,
      TFResNetPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
 import sys

 _A							=   _LazyModule(__name__, globals()['__file__'], _import_structure)





'''simple docstring'''




class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   pass



class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   pass



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self				)    ->     List[str]:
        lowercase_					:       Optional[int]     				=					[
            [],
            [],
            [],
        ]
   def 		_lowerCamelCase (self       ,				_a       ,				_a				)    ->     None:
        try:
             if len(self.queues[priority]				) >= 100:
                  raise OverflowError('Maximum queue size is 100'				)
             self.queues[priority].append(_a				)
        except IndexError:
             raise ValueError('Valid priorities are 0, 1, and 2'				)
   def 		_lowerCamelCase (self				)    ->     int:
        for queue in self.queues:
             if queue:
                  return queue.pop(0				)
        raise UnderFlowError('All queues are empty'				)





   def __str__(self				)    ->     str:
        return "\n".join(f'''Priority {i}: {q}''' for i, q in enumerate(self.queues				)				)



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self				)    ->     str:
        lowercase_					:       List[Any]     				=					[]
   def 		_lowerCamelCase (self       ,				_a				)    ->     None:
        if len(self.queue				) == 100:
             raise OverFlowError('Maximum queue size is 100'				)
        self.queue.append(_a				)
   def 		_lowerCamelCase (self				)    ->     int:
        if not self.queue:
             raise UnderFlowError('The queue is empty'				)
        else:
             lowercase_					:       Tuple     				=					min(self.queue				)
             self.queue.remove(_a				)
             return data





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



def _UpperCamelCase   (       ):
     lowercase_					:       Any     				=					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(SCREAMING_SNAKE_CASE_							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(SCREAMING_SNAKE_CASE_							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)

def _UpperCamelCase   (       ):
     lowercase_					:       Optional[int]     				=					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(SCREAMING_SNAKE_CASE_							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(SCREAMING_SNAKE_CASE_							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)


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




