

'''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'''






			__snake_case             =			42



class lowerCamelCase					(    lowercase_			,		lowercase_		):



			'''simple docstring'''






			@register_to_config
			def __init__( self			:		int						,     lowerCAmelCase_			:		List[str] = 16						,     lowerCAmelCase_			:		Optional[int] = 88						,     lowerCAmelCase_			:		str = None						,     lowerCAmelCase_			:		int = None						,     lowerCAmelCase_			:		Dict = 1						,     lowerCAmelCase_			:		Dict = 0.0						,     lowerCAmelCase_			:		Tuple = 32						,     lowerCAmelCase_			:		str = None						,     lowerCAmelCase_			:		Union[str, Any] = False						,     lowerCAmelCase_			:		Union[str, Any] = None						,     lowerCAmelCase_			:		Union[str, Any] = "geglu"						,     lowerCAmelCase_			:		str = True						,     lowerCAmelCase_			:		Optional[Any] = True						,     )     ->						Tuple:





									'''simple docstring'''
									super().__init__()
									A__	:  Union[str, Any]           =num_attention_heads
									A__	:  Any           =attention_head_dim
									A__	:  Optional[int]           =num_attention_heads * attention_head_dim

									A__	:  str           =in_channels

									A__	:  int           =torch.nn.GroupNorm(num_groups=a_						,     num_channels=a_						,     eps=1e-6						,     affine=a_					)
									A__	:  str           =nn.Linear(a_						,     a_					)

									# 3. Define transformers blocks
									A__	:  List[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_					)
									    ]					)

									A__	:  List[str]           =nn.Linear(a_						,     a_					)



			def lowercase__		( self			:		Optional[int]						,     lowerCAmelCase_			:		Union[str, Any]						,     lowerCAmelCase_			:		Optional[Any]=None						,     lowerCAmelCase_			:		Optional[int]=None						,     lowerCAmelCase_			:		Tuple=None						,     lowerCAmelCase_			:		List[str]=1						,     lowerCAmelCase_			:		str=None						,     lowerCAmelCase_			:		Optional[Any] = True						,     )     ->						List[str]:





									'''simple docstring'''
									A__       ,		A__       ,		A__       ,		A__	:  List[Any]           =hidden_states.shape
									A__	:  Union[str, Any]           =batch_frames // num_frames

									A__	:  Any           =hidden_states

									A__	:  Union[str, Any]           =hidden_states[None, :].reshape(a_						,     a_						,     a_						,     a_						,     a_					)
									A__	:  List[str]           =hidden_states.permute(0						,     2						,     1						,     3						,     4					)

									A__	:  int           =self.norm(a_					)
									A__	:  Dict           =hidden_states.permute(0						,     3						,     4						,     2						,     1					).reshape(batch_size * height * width						,     a_						,     a_					)

									A__	:  List[str]           =self.proj_in(a_					)

									# 2. Blocks
									for block in self.transformer_blocks:
															A__	:  List[Any]           =block(
															    a_						,     encoder_hidden_states=a_						,     timestep=a_						,     cross_attention_kwargs=a_						,     class_labels=a_						,     )

									# 3. Output
									A__	:  Optional[int]           =self.proj_out(a_					)
									A__	:  Any           =(
									    hidden_states[None, None, :]
									    .reshape(a_						,     a_						,     a_						,     a_						,     a_					)
									    .permute(0						,     3						,     4						,     1						,     2					)
									    .contiguous()
									)
									A__	:  str           =hidden_states.reshape(a_						,     a_						,     a_						,     a_					)

									A__	:  Union[str, Any]           =hidden_states + residual

									if not return_dict:
															return (output,)

									return TransformerTemporalModelOutput(sample=a_					)






from bisect import bisect
from itertools import accumulate







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  sorted(zip(snake_case__    ,			snake_case__					)    ,			key=lambda snake_case__					: x[0] / x[1]    ,			reverse=snake_case__					)
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  [i[0] for i in r], [i[1] for i in r]
				lowerCAmelCase__		  =  list(accumulate(snake_case__					)					)
				lowerCAmelCase__		  =  bisect(snake_case__    ,			snake_case__					)
				return (
				    0
				    if k == 0
				    else sum(vl[:k]					) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
				    if k != n
				    else sum(vl[:k]					)
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
