






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'''




	lowerCAmelCase						=					42






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			,     )			->       List[str]:
								super().__init__()
								snake_case_           =				num_attention_heads
								snake_case_           =				attention_head_dim
								snake_case_           =				num_attention_heads * attention_head_dim

								snake_case_           =				in_channels

								snake_case_           =				torch.nn.GroupNorm(num_groups=a			,     num_channels=a			,     eps=1E-6			,     affine=a   )
								snake_case_           =				nn.Linear(a			,     a   )

								# 3. Define transformers blocks
								snake_case_           =				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   )
								    ]   )

								snake_case_           =				nn.Linear(a			,     a   )






	def 				_UpperCamelCase     (			self			,     a			,     a=None			,     a=None			,     a=None			,     a=1			,     a=None			,     a = True			,     )			->       List[Any]:
								snake_case_     ,						snake_case_     ,						snake_case_     ,						snake_case_           =				hidden_states.shape
								snake_case_           =				batch_frames // num_frames

								snake_case_           =				hidden_states

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

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

								snake_case_           =				self.proj_in(a   )

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

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

								snake_case_           =				hidden_states + residual

								if not return_dict:
															return (output,)

								return TransformerTemporalModelOutput(sample=a   )













from __future__ import annotations





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							snake_case_           =				list(range(len(a_)))
							snake_case_           =				[v / w for v, w in zip(a_				,	a_)]
							index.sort(key=lambda a_: ratio[i]				,	reverse=a_)

							snake_case_           =				0
							snake_case_           =				[0] * len(a_)
							for i in index:
														if weight[i] <= capacity:
																					snake_case_           =				1
																					max_value += value[i]
																					capacity -= weight[i]
														else:
																					snake_case_           =				capacity / weight[i]
																					max_value += value[i] * capacity / weight[i]
																					break

							return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()





