'''simple docstring'''




from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       torch.FloatTensor



class       UpperCAmelCase__  (       _snake_case					,     _snake_case			):






   """simple docstring"""
   @register_to_config
   def __init__(self       ,				_a = 16       ,				_a = 88       ,				_a = None       ,				_a = None       ,				_a = 1       ,				_a = 0.0       ,				_a = 32       ,				_a = None       ,				_a = False       ,				_a = None       ,				_a = "geglu"       ,				_a = True       ,				_a = True       ,				)    ->     Optional[int]:
        super().__init__()
        lowercase_					:       Optional[int]     				=					num_attention_heads
        lowercase_					:       Union[str, Any]     				=					attention_head_dim
        lowercase_					:       int     				=					num_attention_heads * attention_head_dim

        lowercase_					:       Tuple     				=					in_channels

        lowercase_					:       List[Any]     				=					torch.nn.GroupNorm(num_groups=_a       ,				num_channels=_a       ,				eps=1e-6       ,				affine=_a				)
        lowercase_					:       str     				=					nn.Linear(_a       ,				_a				)

        # 3. Define transformers blocks
        lowercase_					:       Any     				=					nn.ModuleList(
            [
                BasicTransformerBlock(
                    _a       ,				_a       ,				_a       ,				dropout=_a       ,				cross_attention_dim=_a       ,				activation_fn=_a       ,				attention_bias=_a       ,				double_self_attention=_a       ,				norm_elementwise_affine=_a       ,				)
                for d in range(_a				)
            ]				)

        lowercase_					:       Optional[int]     				=					nn.Linear(_a       ,				_a				)





   def 		_lowerCamelCase (self       ,				_a       ,				_a=None       ,				_a=None       ,				_a=None       ,				_a=1       ,				_a=None       ,				_a = True       ,				)    ->     Union[str, Any]:
        lowercase_				,lowercase_				,lowercase_				,lowercase_					:       List[Any]     				=					hidden_states.shape
        lowercase_					:       List[str]     				=					batch_frames // num_frames

        lowercase_					:       Optional[int]     				=					hidden_states

        lowercase_					:       List[str]     				=					hidden_states[None, :].reshape(_a       ,				_a       ,				_a       ,				_a       ,				_a				)
        lowercase_					:       Optional[int]     				=					hidden_states.permute(0       ,				2       ,				1       ,				3       ,				4				)

        lowercase_					:       List[str]     				=					self.norm(_a				)
        lowercase_					:       List[Any]     				=					hidden_states.permute(0       ,				3       ,				4       ,				2       ,				1				).reshape(batch_size * height * width       ,				_a       ,				_a				)

        lowercase_					:       Dict     				=					self.proj_in(_a				)

        # 2. Blocks
        for block in self.transformer_blocks:
             lowercase_					:       Optional[int]     				=					block(
                 _a       ,				encoder_hidden_states=_a       ,				timestep=_a       ,				cross_attention_kwargs=_a       ,				class_labels=_a       ,				)

        # 3. Output
        lowercase_					:       List[Any]     				=					self.proj_out(_a				)
        lowercase_					:       Any     				=					(
            hidden_states[None, None, :]
            .reshape(_a       ,				_a       ,				_a       ,				_a       ,				_a				)
            .permute(0       ,				3       ,				4       ,				1       ,				2				)
            .contiguous()
        )
        lowercase_					:       int     				=					hidden_states.reshape(_a       ,				_a       ,				_a       ,				_a				)

        lowercase_					:       List[str]     				=					hidden_states + residual

        if not return_dict:
             return (output,)

        return TransformerTemporalModelOutput(sample=_a				)





'''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()




