


'''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    (		lowerCAmelCase_ ):
	A_           =   42





class 							__UpperCamelCase    (		lowerCAmelCase_		,				lowerCAmelCase_ ):




	@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						,    ):

						'''simple docstring'''





						super().__init__()
						__a      :	List[Any]    =  num_attention_heads
						__a      :	List[str]    =  attention_head_dim
						__a      :	Optional[Any]    =  num_attention_heads * attention_head_dim

						__a      :	Dict    =  in_channels

						__a      :	Dict    =  torch.nn.GroupNorm(num_groups=__a						,    num_channels=__a						,    eps=1E-6						,    affine=__a							)
						__a      :	Optional[int]    =  nn.Linear(__a						,    __a							)

						# 3. Define transformers blocks
						__a      :	Tuple    =  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      :	Union[str, Any]    =  nn.Linear(__a						,    __a							)





	def 				__UpperCAmelCase							(       self						,    __a						,    __a=None						,    __a=None						,    __a=None						,    __a=1						,    __a=None						,    __a = True						,    ):

						'''simple docstring'''





						__a   ,    __a   ,    __a   ,    __a      :	Tuple    =  hidden_states.shape
						__a      :	int    =  batch_frames // num_frames

						__a      :	int    =  hidden_states

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

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

						__a      :	Any    =  self.proj_in(__a							)

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

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

						__a      :	Optional[Any]    =  hidden_states + residual

						if not return_dict:
											return (output,)

						return TransformerTemporalModelOutput(sample=__a							)







'''simple docstring'''



import os






def   lowerCamelCase							():
					with open(os.path.dirname(_SCREAMING_SNAKE_CASE       ) + '/p022_names.txt'       ) as file:
										__a      :	List[Any]    =  str(file.readlines()[0]       )
										__a      :	str    =  names.replace('"'						,				''       ).split(','       )

					names.sort()

					__a      :	Union[str, Any]    =  0
					__a      :	Tuple    =  0

					for i, name in enumerate(_SCREAMING_SNAKE_CASE       ):
										for letter in name:
															name_score += ord(_SCREAMING_SNAKE_CASE       ) - 64

										total_score += (i + 1) * name_score
										__a      :	Any    =  0
					return total_score


if __name__ == "__main__":
	print(solution())



