



'''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 							a__(      lowerCamelCase__			):
    lowercase__				=					42

class 							a__(      lowerCamelCase__	,				lowerCamelCase__			):







    @register_to_config
    def __init__(   self				:						Dict		,    __snake_case				:						int = 16		,    __snake_case				:						int = 88		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						int = 1		,    __snake_case				:						float = 0.0		,    __snake_case				:						int = 32		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						bool = False		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						str = "geglu"		,    __snake_case				:						bool = True		,    __snake_case				:						bool = True		,    ):
      super().__init__()
      a		:      int       =  num_attention_heads
      a		:      Union[str, Any]       =  attention_head_dim
      a		:      Union[str, Any]       =  num_attention_heads * attention_head_dim

      a		:      Any       =  in_channels

      a		:      Optional[int]       =  torch.nn.GroupNorm(num_groups=__snake_case		,    num_channels=__snake_case		,    eps=1e-6		,    affine=__snake_case			)
      a		:      Optional[int]       =  nn.Linear(__snake_case		,    __snake_case			)

      # 3. Define transformers blocks
      a		:      Optional[Any]       =  nn.ModuleList(
          [
              BasicTransformerBlock(
                  __snake_case		,    __snake_case		,    __snake_case		,    dropout=__snake_case		,    cross_attention_dim=__snake_case		,    activation_fn=__snake_case		,    attention_bias=__snake_case		,    double_self_attention=__snake_case		,    norm_elementwise_affine=__snake_case		,    )
              for d in range(__snake_case			)
          ]			)

      a		:      int       =  nn.Linear(__snake_case		,    __snake_case			)







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						Dict		,    __snake_case				:						Optional[Any]=None		,    __snake_case				:						int=None		,    __snake_case				:						List[Any]=None		,    __snake_case				:						Union[str, Any]=1		,    __snake_case				:						int=None		,    __snake_case				:						bool = True		,    ):
      a     ,			a     ,			a     ,			a		:      Any       =  hidden_states.shape
      a		:      Optional[int]       =  batch_frames // num_frames

      a		:      Union[str, Any]       =  hidden_states

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

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

      a		:      Tuple       =  self.proj_in(__snake_case			)

      # 2. Blocks
      for block in self.transformer_blocks:
        a		:      Optional[Any]       =  block(
            __snake_case		,    encoder_hidden_states=__snake_case		,    timestep=__snake_case		,    cross_attention_kwargs=__snake_case		,    class_labels=__snake_case		,    )

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

      a		:      int       =  hidden_states + residual

      if not return_dict:
        return (output,)

      return TransformerTemporalModelOutput(sample=__snake_case			)




'''simple docstring'''






from pathlib import Path

import fire
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  Path(_A       )
  a		:      Tuple       =  Path(_A       )
  dest_dir.mkdir(exist_ok=_A       )
  for path in src_dir.iterdir():
    a		:      Tuple       =  [x.rstrip() for x in list(path.open().readlines()       )][:n]
    a		:      List[str]       =  dest_dir.joinpath(path.name       )
    print(_A       )
    dest_path.open('w'       ).write('\n'.join(_A       )       )


if __name__ == "__main__":
     fire.Fire(minify)