



'''simple docstring'''






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class 				SCREAMING_SNAKE_CASE(							lowerCAmelCase_			):







	"""simple docstring"""






	lowerCamelCase__			 =       42

class 				SCREAMING_SNAKE_CASE(							lowerCAmelCase_							,		lowerCAmelCase_			):







	"""simple docstring"""






	@register_to_config
	def __init__(						self			:     Tuple		,			__snake_case			:     int = 65536		,			__snake_case			:     Optional[int] = None		,			__snake_case			:     int = 2		,			__snake_case			:     int = 2		,			__snake_case			:     int = 0		,			__snake_case			:     str = "fourier"		,			__snake_case			:     bool = True		,			__snake_case			:     bool = False		,			__snake_case			:     float = 0.0		,			__snake_case			:     Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,			__snake_case			:     Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,			__snake_case			:     Tuple[str] = "UNetMidBlock1D"		,			__snake_case			:     str = None		,			__snake_case			:     Tuple[int] = (32, 32, 64)		,			__snake_case			:     str = None		,			__snake_case			:     int = 8		,			__snake_case			:     int = 1		,			__snake_case			:     bool = False		,			)			->		List[Any]:
								super().__init__()
								UpperCAmelCase :       List[str] 							=		sample_size

								# time
								if time_embedding_type == "fourier":
															UpperCAmelCase :       Dict 							=		GaussianFourierProjection(
															    embedding_size=8		,			set_W_to_weight=lowerCamelCase__		,			log=lowerCamelCase__		,			flip_sin_to_cos=lowerCamelCase__		)
															UpperCAmelCase :       Dict 							=		2 * block_out_channels[0]
								elif time_embedding_type == "positional":
															UpperCAmelCase :       str 							=		Timesteps(
															    block_out_channels[0]		,			flip_sin_to_cos=lowerCamelCase__		,			downscale_freq_shift=lowerCamelCase__		)
															UpperCAmelCase :       List[Any] 							=		block_out_channels[0]

								if use_timestep_embedding:
															UpperCAmelCase :       Optional[Any] 							=		block_out_channels[0] * 4
															UpperCAmelCase :       str 							=		TimestepEmbedding(
															    in_channels=lowerCamelCase__		,			time_embed_dim=lowerCamelCase__		,			act_fn=lowerCamelCase__		,			out_dim=block_out_channels[0]		,			)

								UpperCAmelCase :       List[Any] 							=		nn.ModuleList([]		)
								UpperCAmelCase :       int 							=		None
								UpperCAmelCase :       Optional[Any] 							=		nn.ModuleList([]		)
								UpperCAmelCase :       List[str] 							=		None

								# down
								UpperCAmelCase :       Optional[int] 							=		in_channels
								for i, down_block_type in enumerate(lowerCamelCase__		):
															UpperCAmelCase :       Optional[int] 							=		output_channel
															UpperCAmelCase :       Tuple 							=		block_out_channels[i]

															if i == 0:
																						input_channel += extra_in_channels

															UpperCAmelCase :       Optional[int] 							=		i == len(lowerCamelCase__		) - 1

															UpperCAmelCase :       Optional[Any] 							=		get_down_block(
															    lowerCamelCase__		,			num_layers=lowerCamelCase__		,			in_channels=lowerCamelCase__		,			out_channels=lowerCamelCase__		,			temb_channels=block_out_channels[0]		,			add_downsample=not is_final_block or downsample_each_block		,			)
															self.down_blocks.append(lowerCamelCase__		)

								# mid
								UpperCAmelCase :       List[Any] 							=		get_mid_block(
								    lowerCamelCase__		,			in_channels=block_out_channels[-1]		,			mid_channels=block_out_channels[-1]		,			out_channels=block_out_channels[-1]		,			embed_dim=block_out_channels[0]		,			num_layers=lowerCamelCase__		,			add_downsample=lowerCamelCase__		,			)

								# up
								UpperCAmelCase :       Optional[int] 							=		list(reversed(lowerCamelCase__		)		)
								UpperCAmelCase :       Any 							=		reversed_block_out_channels[0]
								if out_block_type is None:
															UpperCAmelCase :       int 							=		out_channels
								else:
															UpperCAmelCase :       int 							=		block_out_channels[0]

								for i, up_block_type in enumerate(lowerCamelCase__		):
															UpperCAmelCase :       Optional[Any] 							=		output_channel
															UpperCAmelCase :       int 							=		(
															    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase__		) - 1 else final_upsample_channels
															)

															UpperCAmelCase :       str 							=		i == len(lowerCamelCase__		) - 1

															UpperCAmelCase :       List[str] 							=		get_up_block(
															    lowerCamelCase__		,			num_layers=lowerCamelCase__		,			in_channels=lowerCamelCase__		,			out_channels=lowerCamelCase__		,			temb_channels=block_out_channels[0]		,			add_upsample=not is_final_block		,			)
															self.up_blocks.append(lowerCamelCase__		)
															UpperCAmelCase :       Optional[Any] 							=		output_channel

								# out
								UpperCAmelCase :       Optional[Any] 							=		norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,			32		)
								UpperCAmelCase :       Any 							=		get_out_block(
								    out_block_type=lowerCamelCase__		,			num_groups_out=lowerCamelCase__		,			embed_dim=block_out_channels[0]		,			out_channels=lowerCamelCase__		,			act_fn=lowerCamelCase__		,			fc_dim=block_out_channels[-1] // 4		,			)







	def      A	(						self			:     Dict		,			__snake_case			:     torch.FloatTensor		,			__snake_case			:     Union[torch.Tensor, float, int]		,			__snake_case			:     bool = True		,			)			->		Union[UNetaDOutput, Tuple]:
								UpperCAmelCase :       int 							=		timestep
								if not torch.is_tensor(lowerCamelCase__		):
															UpperCAmelCase :       Dict 							=		torch.tensor([timesteps]		,			dtype=torch.long		,			device=sample.device		)
								elif torch.is_tensor(lowerCamelCase__		) and len(timesteps.shape		) == 0:
															UpperCAmelCase :       List[Any] 							=		timesteps[None].to(sample.device		)

								UpperCAmelCase :       Optional[Any] 							=		self.time_proj(lowerCamelCase__		)
								if self.config.use_timestep_embedding:
															UpperCAmelCase :       Optional[int] 							=		self.time_mlp(lowerCamelCase__		)
								else:
															UpperCAmelCase :       Any 							=		timestep_embed[..., None]
															UpperCAmelCase :       List[Any] 							=		timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
															UpperCAmelCase :       str 							=		timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

								# 2. down
								UpperCAmelCase :       Any 							=		()
								for downsample_block in self.down_blocks:
															UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		downsample_block(hidden_states=lowerCamelCase__		,			temb=lowerCamelCase__		)
															down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
															UpperCAmelCase :       str 							=		self.mid_block(lowerCamelCase__		,			lowerCamelCase__		)

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks		):
															UpperCAmelCase :       Optional[Any] 							=		down_block_res_samples[-1:]
															UpperCAmelCase :       Tuple 							=		down_block_res_samples[:-1]
															UpperCAmelCase :       Tuple 							=		upsample_block(lowerCamelCase__		,			res_hidden_states_tuple=lowerCamelCase__		,			temb=lowerCamelCase__		)

								# 5. post-process
								if self.out_block:
															UpperCAmelCase :       Any 							=		self.out_block(lowerCamelCase__		,			lowerCamelCase__		)

								if not return_dict:
															return (sample,)

								return UNetaDOutput(sample=lowerCamelCase__		)












from pathlib import Path

import fire



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)
      for path in src_dir.iterdir():
            SCREAMING_SNAKE_CASE		=						[x.rstrip() for x in list(path.open().readlines()	)][:n]
            SCREAMING_SNAKE_CASE		=						dest_dir.joinpath(path.name	)
            print(_SCREAMING_SNAKE_CASE	)
            dest_path.open("""w"""	).write("""\n""".join(_SCREAMING_SNAKE_CASE	)	)


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


