
"""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        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							torch.FloatTensor






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				UpperCAmelCase							):






				'''simple docstring'''



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







											"""simple docstring"""




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

											_lowerCAmelCase  	=			in_channels

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

											# 3. Define transformers blocks
											_lowerCAmelCase  	=			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_  )
											    ]  )

											_lowerCAmelCase  	=			nn.Linear(UpperCAmelCase_				,     UpperCAmelCase_  )







				def 				__lowerCamelCase	(					self	:			Optional[int]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			str=None				,     UpperCAmelCase_	:			int=None				,     UpperCAmelCase_	:			Any=None				,     UpperCAmelCase_	:			Union[str, Any]=1				,     UpperCAmelCase_	:			Any=None				,     UpperCAmelCase_	:			bool = True				,     )		->  Optional[int]:







											"""simple docstring"""




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

											_lowerCAmelCase  	=			hidden_states

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

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

											_lowerCAmelCase  	=			self.proj_in(UpperCAmelCase_  )

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

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

											_lowerCAmelCase  	=			hidden_states + residual

											if not return_dict:
																		return (output,)

											return TransformerTemporalModelOutput(sample=UpperCAmelCase_  )








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[int(SCREAMING_SNAKE_CASE						) for i in ip_va_address.split('.'						) if i.isdigit()]
							return len(SCREAMING_SNAKE_CASE						) == 4 and all(0 <= int(SCREAMING_SNAKE_CASE						) <= 254 for octet in octets						)


if __name__ == "__main__":
	_snake_case          =						input().strip()
	_snake_case          =						'''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
	print(f'{ip} is a {valid_or_invalid} IP v4 address.')






