


"""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 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      torch.FloatTensor


class 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				@register_to_config
				def __init__(						self       ,		UpperCAmelCase__ = 16       ,		UpperCAmelCase__ = 88       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = 1       ,		UpperCAmelCase__ = 0.0       ,		UpperCAmelCase__ = 32       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = False       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = "geglu"       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = True       ,		):
								super().__init__()
								SCREAMING_SNAKE_CASE__     							=				num_attention_heads
								SCREAMING_SNAKE_CASE__     							=				attention_head_dim
								SCREAMING_SNAKE_CASE__     							=				num_attention_heads * attention_head_dim

								SCREAMING_SNAKE_CASE__     							=				in_channels

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

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

								SCREAMING_SNAKE_CASE__     							=				nn.Linear(UpperCAmelCase__       ,		UpperCAmelCase__     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=1       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__ = True       ,		):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				hidden_states.shape
								SCREAMING_SNAKE_CASE__     							=				batch_frames // num_frames

								SCREAMING_SNAKE_CASE__     							=				hidden_states

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

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

								SCREAMING_SNAKE_CASE__     							=				self.proj_in(UpperCAmelCase__     )

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

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

								SCREAMING_SNAKE_CASE__     							=				hidden_states + residual

								if not return_dict:
												return (output,)

								return TransformerTemporalModelOutput(sample=UpperCAmelCase__     )




"""simple docstring"""






from __future__ import annotations


def __lowercase     (      lowerCamelCase_  :      list						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				input_list[low:mid], input_list[mid : high + 1]
				while left and right:
								result.append((left if left[0] <= right[0] else right).pop(0  )  )
				SCREAMING_SNAKE_CASE__     							=				result + left + right
				return input_list


def __lowercase     (      lowerCamelCase_  :      list  ):
				if len(lowerCamelCase_  ) <= 1:
								return input_list
				SCREAMING_SNAKE_CASE__     							=				list(lowerCamelCase_  )

				# iteration for two-way merging
				SCREAMING_SNAKE_CASE__     							=				2
				while p <= len(lowerCamelCase_  ):
								# getting low, high and middle value for merge-sort of single list
								for i in range(0						,					len(lowerCamelCase_  )						,					lowerCamelCase_  ):
												SCREAMING_SNAKE_CASE__     							=				i
												SCREAMING_SNAKE_CASE__     							=				i + p - 1
												SCREAMING_SNAKE_CASE__     							=				(low + high + 1) // 2
												SCREAMING_SNAKE_CASE__     							=				merge(lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_  )
								# final merge of last two parts
								if p * 2 >= len(lowerCamelCase_  ):
												SCREAMING_SNAKE_CASE__     							=				i
												SCREAMING_SNAKE_CASE__     							=				merge(lowerCamelCase_						,					0						,					lowerCamelCase_						,					len(lowerCamelCase_  ) - 1  )
												break
								p *= 2

				return input_list


if __name__ == "__main__":
	_lowerCamelCase     =  input('Enter numbers separated by a comma:\n').strip()
	if user_input == "":
		_lowerCamelCase     =  []
	else:
		_lowerCamelCase     =  [int(item.strip()) for item in user_input.split(',')]
	print(iter_merge_sort(unsorted))
