


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 						__a (							UpperCAmelCase     ):
		_a      :    torch.FloatTensor



class 						__a (							UpperCAmelCase       , UpperCAmelCase     ):
		@register_to_config
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE = 16     ,       _SCREAMING_SNAKE_CASE = 88     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = 1     ,       _SCREAMING_SNAKE_CASE = 0.0     ,       _SCREAMING_SNAKE_CASE = 32     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = "geglu"     ,       _SCREAMING_SNAKE_CASE = True     ,       _SCREAMING_SNAKE_CASE = True     ,       )					->			Dict:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     attention_head_dim
									_UpperCAmelCase											=     num_attention_heads * attention_head_dim

									_UpperCAmelCase											=     in_channels

									_UpperCAmelCase											=     torch.nn.GroupNorm(num_groups=_SCREAMING_SNAKE_CASE     ,       num_channels=_SCREAMING_SNAKE_CASE     ,       eps=1e-6     ,       affine=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     nn.Linear(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

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

									_UpperCAmelCase											=     nn.Linear(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE = True     ,       )					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     hidden_states.shape
									_UpperCAmelCase											=     batch_frames // num_frames

									_UpperCAmelCase											=     hidden_states

									_UpperCAmelCase											=     hidden_states[None, :].reshape(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     hidden_states.permute(0     ,       2     ,       1     ,       3     ,       4		)

									_UpperCAmelCase											=     self.norm(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     hidden_states.permute(0     ,       3     ,       4     ,       2     ,       1		).reshape(batch_size * height * width     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     self.proj_in(_SCREAMING_SNAKE_CASE		)

									# 2. Blocks
									for block in self.transformer_blocks:
																_UpperCAmelCase											=     block(
																    _SCREAMING_SNAKE_CASE     ,       encoder_hidden_states=_SCREAMING_SNAKE_CASE     ,       timestep=_SCREAMING_SNAKE_CASE     ,       cross_attention_kwargs=_SCREAMING_SNAKE_CASE     ,       class_labels=_SCREAMING_SNAKE_CASE     ,       )

									# 3. Output
									_UpperCAmelCase											=     self.proj_out(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     (
									    hidden_states[None, None, :]
									    .reshape(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									    .permute(0     ,       3     ,       4     ,       1     ,       2		)
									    .contiguous()
									)
									_UpperCAmelCase											=     hidden_states.reshape(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     hidden_states + residual

									if not return_dict:
																return (output,)

									return TransformerTemporalModelOutput(sample=_SCREAMING_SNAKE_CASE		)




def 			lowerCAmelCase__					(     a__:						list			)    ->						list:


							'''simple docstring'''





							if len(a__			) < 2:
														return collection

							def circle_sort_util(a__:						list     ,    a__:						int     ,    a__:						int			) -> bool:
														_UpperCAmelCase											=     False

														if low == high:
																					return swapped

														_UpperCAmelCase											=     low
														_UpperCAmelCase											=     high

														while left < right:
																					if collection[left] > collection[right]:
																												_UpperCAmelCase						,			_UpperCAmelCase											=     (
																												    collection[right],
																												    collection[left],
																												)
																												_UpperCAmelCase											=     True

																					left += 1
																					right -= 1

														if left == right and collection[left] > collection[right + 1]:
																					_UpperCAmelCase						,			_UpperCAmelCase											=     (
																					    collection[right + 1],
																					    collection[left],
																					)

																					_UpperCAmelCase											=     True

														_UpperCAmelCase											=     low + int((high - low) / 2			)
														_UpperCAmelCase											=     circle_sort_util(a__     ,    a__     ,    a__			)
														_UpperCAmelCase											=     circle_sort_util(a__     ,    mid + 1     ,    a__			)

														return swapped or left_swap or right_swap

							_UpperCAmelCase											=     True

							while is_not_sorted is True:
														_UpperCAmelCase											=     circle_sort_util(a__     ,    0     ,    len(a__			) - 1			)

							return collection


if __name__ == "__main__":
		lowerCAmelCase__    :Tuple       =				input('''Enter numbers separated by a comma:\n''').strip()
		lowerCAmelCase__    :List[str]       =				[int(item) for item in user_input.split(''',''')]
		print(circle_sort(unsorted))
