


'''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     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	torch.FloatTensor






class     __lowerCAmelCase		(lowercase_    ,	lowercase_	):





      '''simple docstring'''






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

             '''simple docstring'''







             super().__init__()
             lowercase__      =					sample_size

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

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

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

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

                    if i == 0:
                           input_channel += extra_in_channels

                    lowercase__      =					i == len(UpperCamelCase					) - 1

                    lowercase__      =					get_down_block(
                        UpperCamelCase  ,		num_layers=UpperCamelCase  ,		in_channels=UpperCamelCase  ,		out_channels=UpperCamelCase  ,		temb_channels=block_out_channels[0]  ,		add_downsample=not is_final_block or downsample_each_block  ,		)
                    self.down_blocks.append(UpperCamelCase					)

             # mid
             lowercase__      =					get_mid_block(
                 UpperCamelCase  ,		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=UpperCamelCase  ,		add_downsample=UpperCamelCase  ,		)

             # up
             lowercase__      =					list(reversed(UpperCamelCase					)					)
             lowercase__      =					reversed_block_out_channels[0]
             if out_block_type is None:
                    lowercase__      =					out_channels
             else:
                    lowercase__      =					block_out_channels[0]

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

                    lowercase__      =					i == len(UpperCamelCase					) - 1

                    lowercase__      =					get_up_block(
                        UpperCamelCase  ,		num_layers=UpperCamelCase  ,		in_channels=UpperCamelCase  ,		out_channels=UpperCamelCase  ,		temb_channels=block_out_channels[0]  ,		add_upsample=not is_final_block  ,		)
                    self.up_blocks.append(UpperCamelCase					)
                    lowercase__      =					output_channel

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






      def  UpperCamelCase__						(self       :       str  ,		UpperCamelCase       :       torch.FloatTensor  ,		UpperCamelCase       :       Union[torch.Tensor, float, int]  ,		UpperCamelCase       :       bool = True  ,		):

             '''simple docstring'''







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

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

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

             # 3. mid
             if self.mid_block:
                    lowercase__      =					self.mid_block(UpperCamelCase  ,		UpperCamelCase					)

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

             # 5. post-process
             if self.out_block:
                    lowercase__      =					self.out_block(UpperCamelCase  ,		UpperCamelCase					)

             if not return_dict:
                    return (sample,)

             return UNetaDOutput(sample=UpperCamelCase					)








'''simple docstring'''

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands

def _SCREAMING_SNAKE_CASE ()			->			Dict:






       """simple docstring"""







       lowercase__      =					ArgumentParser('''Transformers CLI tool'''    ,					usage='''transformers-cli <command> [<args>]'''			)
       lowercase__      =					parser.add_subparsers(help='''transformers-cli command helpers'''			)

       # Register commands
       ConvertCommand.register_subcommand(A			)
       DownloadCommand.register_subcommand(A			)
       EnvironmentCommand.register_subcommand(A			)
       RunCommand.register_subcommand(A			)
       ServeCommand.register_subcommand(A			)
       UserCommands.register_subcommand(A			)
       AddNewModelCommand.register_subcommand(A			)
       AddNewModelLikeCommand.register_subcommand(A			)
       LfsCommands.register_subcommand(A			)
       PTtoTFCommand.register_subcommand(A			)

       # Let's go
       lowercase__      =					parser.parse_args()

       if not hasattr(A    ,					'''func'''			):
              parser.print_help()
              exit(1			)

       # Run
       lowercase__      =					args.func(A			)
       service.run()


if __name__ == "__main__":
 main()




