


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      lowercase__	(       __SCREAMING_SNAKE_CASE		):
  A__=	42




class      lowercase__	(       __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE		):






  @register_to_config
  def __init__(       self   :       Any		,    _lowercase   :       List[Any] = 16		,    _lowercase   :       Optional[Any] = 88		,    _lowercase   :       str = None		,    _lowercase   :       Union[str, Any] = None		,    _lowercase   :       Tuple = 1		,    _lowercase   :       Optional[int] = 0.0		,    _lowercase   :       int = 32		,    _lowercase   :       List[str] = None		,    _lowercase   :       List[str] = False		,    _lowercase   :       Optional[Any] = None		,    _lowercase   :       int = "geglu"		,    _lowercase   :       Dict = True		,    _lowercase   :       List[str] = 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=_lowercase		,    num_channels=_lowercase		,    eps=1E-6		,    affine=_lowercase			)
         UpperCAmelCase__					    =  nn.Linear(_lowercase		,    _lowercase			)

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

         UpperCAmelCase__					    =  nn.Linear(_lowercase		,    _lowercase			)






  def       _UpperCAmelCase	(       self   :       Any		,    _lowercase   :       List[Any]		,    _lowercase   :       Dict=None		,    _lowercase   :       Union[str, Any]=None		,    _lowercase   :       int=None		,    _lowercase   :       Dict=1		,    _lowercase   :       List[Any]=None		,    _lowercase   :       str = True		,    ):




         """simple docstring"""







         UpperCAmelCase__					    =  hidden_states.shape
         UpperCAmelCase__					    =  batch_frames // num_frames

         UpperCAmelCase__					    =  hidden_states

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

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

         UpperCAmelCase__					    =  self.proj_in(_lowercase			)

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

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

         UpperCAmelCase__					    =  hidden_states + residual

         if not return_dict:
                return (output,)

         return TransformerTemporalModelOutput(sample=_lowercase			)











'''simple docstring'''



def   lowercase__(			__UpperCamelCase:					int = 1_00_00_00    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Tuple    					=						[i - 1 for i in range(limit + 1    )]

      for i in range(2      ,limit + 1    ):
            if phi[i] == i - 1:
                  for j in range(2 * i      ,limit + 1      ,__UpperCamelCase    ):
                        phi[j] -= phi[j] // i

      return sum(phi[2 : limit + 1]    )


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


