
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  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					torch.FloatTensor




class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""

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






      """simple docstring"""



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

      UpperCamelCase						    =					in_channels

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

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

      UpperCamelCase						    =					nn.Linear(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			List[str]=None					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=None					, SCREAMING_SNAKE_CASE__			:			Any=None					, SCREAMING_SNAKE_CASE__			:			Dict=1					, SCREAMING_SNAKE_CASE__			:			Any=None					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



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

      UpperCamelCase						    =					hidden_states

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

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

      UpperCamelCase						    =					self.proj_in(SCREAMING_SNAKE_CASE__   )

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

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

      UpperCamelCase						    =					hidden_states + residual

      if not return_dict:
           return (output,)

      return TransformerTemporalModelOutput(sample=SCREAMING_SNAKE_CASE__   )




from decimal import Decimal, getcontext
from math import ceil, factorial

def  __lowerCamelCase					( _lowercase			)       ->      str:

     if not isinstance(_lowercase						,     _lowercase			):
          raise TypeError('Undefined for non-integers'			)
     elif precision < 1:
          raise ValueError('Undefined for non-natural numbers'			)

     UpperCamelCase						    =					precision
     UpperCamelCase						    =					ceil(precision / 14			)
     UpperCamelCase						    =					426880 * Decimal(10005			).sqrt()
     UpperCamelCase						    =					1
     UpperCamelCase						    =					13591409
     UpperCamelCase						    =					Decimal(_lowercase			)
     for k in range(1						,     _lowercase			):
          UpperCamelCase						    =					factorial(6 * k			) // (factorial(3 * k			) * factorial(_lowercase			) ** 3)
          linear_term += 545140134
          exponential_term *= -262537412640768000
          partial_sum += Decimal(multinomial_term * linear_term			) / exponential_term
     return str(constant_term / partial_sum			)[:-1]


if __name__ == "__main__":
 _snake_case      = 50
 print(F"The first {n} digits of pi is: {pi(n)}")


