





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 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	torch.FloatTensor

class 		UpperCamelCase__ (      lowerCAmelCase_							,      lowerCAmelCase_   ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						sample_size

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

           if use_timestep_embedding:
                 SCREAMING_SNAKE_CASE		=						block_out_channels[0] * 4
                 SCREAMING_SNAKE_CASE		=						TimestepEmbedding(
                     in_channels=lowerCamelCase__						,time_embed_dim=lowerCamelCase__						,act_fn=lowerCamelCase__						,out_dim=block_out_channels[0]						,)

           SCREAMING_SNAKE_CASE		=						nn.ModuleList([]       )
           SCREAMING_SNAKE_CASE		=						None
           SCREAMING_SNAKE_CASE		=						nn.ModuleList([]       )
           SCREAMING_SNAKE_CASE		=						None

           # down
           SCREAMING_SNAKE_CASE		=						in_channels
           for i, down_block_type in enumerate(lowerCamelCase__       ):
                 SCREAMING_SNAKE_CASE		=						output_channel
                 SCREAMING_SNAKE_CASE		=						block_out_channels[i]

                 if i == 0:
                       input_channel += extra_in_channels

                 SCREAMING_SNAKE_CASE		=						i == len(lowerCamelCase__       ) - 1

                 SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						list(reversed(lowerCamelCase__       )       )
           SCREAMING_SNAKE_CASE		=						reversed_block_out_channels[0]
           if out_block_type is None:
                 SCREAMING_SNAKE_CASE		=						out_channels
           else:
                 SCREAMING_SNAKE_CASE		=						block_out_channels[0]

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

                 SCREAMING_SNAKE_CASE		=						i == len(lowerCamelCase__       ) - 1

                 SCREAMING_SNAKE_CASE		=						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__       )
                 SCREAMING_SNAKE_CASE		=						output_channel

           # out
           SCREAMING_SNAKE_CASE		=						norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4						,32       )
           SCREAMING_SNAKE_CASE		=						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 				SCREAMING_SNAKE_CASE__  (							self						:    Dict						,lowerCamelCase__						:    torch.FloatTensor						,lowerCamelCase__						:    Union[torch.Tensor, float, int]						,lowerCamelCase__						:    bool = True						,)     ->		Union[UNetaDOutput, Tuple]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						timestep
           if not torch.is_tensor(lowerCamelCase__       ):
                 SCREAMING_SNAKE_CASE		=						torch.tensor([timesteps]						,dtype=torch.long						,device=sample.device       )
           elif torch.is_tensor(lowerCamelCase__       ) and len(timesteps.shape       ) == 0:
                 SCREAMING_SNAKE_CASE		=						timesteps[None].to(sample.device       )

           SCREAMING_SNAKE_CASE		=						self.time_proj(lowerCamelCase__       )
           if self.config.use_timestep_embedding:
                 SCREAMING_SNAKE_CASE		=						self.time_mlp(lowerCamelCase__       )
           else:
                 SCREAMING_SNAKE_CASE		=						timestep_embed[..., None]
                 SCREAMING_SNAKE_CASE		=						timestep_embed.repeat([1, 1, sample.shape[2]]       ).to(sample.dtype       )
                 SCREAMING_SNAKE_CASE		=						timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])       )

           # 2. down
           SCREAMING_SNAKE_CASE		=						()
           for downsample_block in self.down_blocks:
                 SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						downsample_block(hidden_states=lowerCamelCase__						,temb=lowerCamelCase__       )
                 down_block_res_samples += res_samples

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

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

           # 5. post-process
           if self.out_block:
                 SCREAMING_SNAKE_CASE		=						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)


