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        lowercase_   (					snake_case_		):
       _lowerCamelCase						=		42




class        lowercase_   (					snake_case_						,			snake_case_		):



       @register_to_config
       def __init__(						self				,    lowercase_ = 65_536				,    lowercase_ = None				,    lowercase_ = 2				,    lowercase_ = 2				,    lowercase_ = 0				,    lowercase_ = "fourier"				,    lowercase_ = True				,    lowercase_ = False				,    lowercase_ = 0.0				,    lowercase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,    lowercase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,    lowercase_ = "UNetMidBlock1D"				,    lowercase_ = None				,    lowercase_ = (32, 32, 64)				,    lowercase_ = None				,    lowercase_ = 8				,    lowercase_ = 1				,    lowercase_ = False				,    ):
             super().__init__()
             _snake_case						:					List[str]    =   sample_size

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

             if use_timestep_embedding:
                   _snake_case						:					List[Any]    =   block_out_channels[0] * 4
                   _snake_case						:					int    =   TimestepEmbedding(
                       in_channels=lowercase_				,    time_embed_dim=lowercase_				,    act_fn=lowercase_				,    out_dim=block_out_channels[0]				,    )

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

             # down
             _snake_case						:					Any    =   in_channels
             for i, down_block_type in enumerate(lowercase_    ):
                   _snake_case						:					Optional[int]    =   output_channel
                   _snake_case						:					Optional[Any]    =   block_out_channels[i]

                   if i == 0:
                         input_channel += extra_in_channels

                   _snake_case						:					Dict    =   i == len(lowercase_    ) - 1

                   _snake_case						:					str    =   get_down_block(
                       lowercase_				,    num_layers=lowercase_				,    in_channels=lowercase_				,    out_channels=lowercase_				,    temb_channels=block_out_channels[0]				,    add_downsample=not is_final_block or downsample_each_block				,    )
                   self.down_blocks.append(lowercase_    )

             # mid
             _snake_case						:					Dict    =   get_mid_block(
                 lowercase_				,    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=lowercase_				,    add_downsample=lowercase_				,    )

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

             for i, up_block_type in enumerate(lowercase_    ):
                   _snake_case						:					Optional[Any]    =   output_channel
                   _snake_case						:					List[str]    =   (
                       reversed_block_out_channels[i + 1] if i < len(lowercase_    ) - 1 else final_upsample_channels
                   )

                   _snake_case						:					Dict    =   i == len(lowercase_    ) - 1

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

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



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = True				,    ):
             _snake_case						:					Optional[int]    =   timestep
             if not torch.is_tensor(lowercase_    ):
                   _snake_case						:					Any    =   torch.tensor([timesteps]				,    dtype=torch.long				,    device=sample.device    )
             elif torch.is_tensor(lowercase_    ) and len(timesteps.shape    ) == 0:
                   _snake_case						:					str    =   timesteps[None].to(sample.device    )

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

             # 2. down
             _snake_case						:					Tuple    =   ()
             for downsample_block in self.down_blocks:
                   _snake_case						:					Tuple    =   downsample_block(hidden_states=lowercase_				,    temb=lowercase_    )
                   down_block_res_samples += res_samples

             # 3. mid
             if self.mid_block:
                   _snake_case						:					Tuple    =   self.mid_block(lowercase_				,    lowercase_    )

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

             # 5. post-process
             if self.out_block:
                   _snake_case						:					Any    =   self.out_block(lowercase_				,    lowercase_    )

             if not return_dict:
                   return (sample,)

             return UNetaDOutput(sample=lowercase_    )
import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


__SCREAMING_SNAKE_CASE						:   Any         =			importlib.util.find_spec('s3fs') is not None

if _has_safs:
				from .safilesystem import SaFileSystem  # noqa: F401

__SCREAMING_SNAKE_CASE						:   List[compression.BaseCompressedFileFileSystem]         =			[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
				if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
								warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
				fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						if "://" in dataset_path:
												_snake_case						:					Tuple    =   dataset_path.split("://"						)[1]
						return dataset_path
def  snake_case						(__lowercase						)	->	bool:

						'''simple docstring'''



						if fs is not None and fs.protocol != "file":
												return True
						else:
												return False
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   not is_remote_filesystem(__lowercase						)
						if is_local:
												# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
												shutil.move(fs._strip_protocol(__lowercase						) ,      fs._strip_protocol(__lowercase						)						)
						else:
												fs.mv(__lowercase ,      __lowercase ,      recursive=__lowercase						)

def  snake_case						()	->	None:

						'''simple docstring'''



						if hasattr(fsspec.asyn ,      "reset_lock"						):
												# for future fsspec>2022.05.0
												fsspec.asyn.reset_lock()
						else:
												_snake_case						:					List[str]    =   None
												_snake_case						:					str    =   None
												_snake_case						:					List[Any]    =   threading.Lock()