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_	(  _UpperCamelCase					):






    '''simple docstring'''






    a__      :		torch.FloatTensor


class       lowerCamelCase_	(  _UpperCamelCase		,      _UpperCamelCase					):






    '''simple docstring'''






    @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					,					)			->      str:
        super().__init__()
        __UpperCamelCase	:int         =						sample_size

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

        if use_timestep_embedding:
            __UpperCamelCase	:List[Any]         =						block_out_channels[0] * 4
            __UpperCamelCase	:List[str]         =						TimestepEmbedding(
                in_channels=lowercase__					,					time_embed_dim=lowercase__					,					act_fn=lowercase__					,					out_dim=block_out_channels[0]					,					)

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

        # down
        __UpperCamelCase	:Union[str, Any]         =						in_channels
        for i, down_block_type in enumerate(lowercase__):
            __UpperCamelCase	:List[Any]         =						output_channel
            __UpperCamelCase	:Optional[int]         =						block_out_channels[i]

            if i == 0:
                input_channel += extra_in_channels

            __UpperCamelCase	:Optional[int]         =						i == len(lowercase__) - 1

            __UpperCamelCase	:Optional[Any]         =						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
        __UpperCamelCase	:Optional[Any]         =						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
        __UpperCamelCase	:Optional[Any]         =						list(reversed(lowercase__))
        __UpperCamelCase	:List[str]         =						reversed_block_out_channels[0]
        if out_block_type is None:
            __UpperCamelCase	:Optional[int]         =						out_channels
        else:
            __UpperCamelCase	:Optional[int]         =						block_out_channels[0]

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

            __UpperCamelCase	:Tuple         =						i == len(lowercase__) - 1

            __UpperCamelCase	:Tuple         =						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__)
            __UpperCamelCase	:str         =						output_channel

        # out
        __UpperCamelCase	:int         =						norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4					,					32)
        __UpperCamelCase	:str         =						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					,					)			->      Dict:
        __UpperCamelCase	:List[Any]         =						timestep
        if not torch.is_tensor(lowercase__):
            __UpperCamelCase	:Dict         =						torch.tensor([timesteps]					,					dtype=torch.long					,					device=sample.device)
        elif torch.is_tensor(lowercase__) and len(timesteps.shape) == 0:
            __UpperCamelCase	:Union[str, Any]         =						timesteps[None].to(sample.device)

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

        # 2. down
        __UpperCamelCase	:Optional[int]         =						()
        for downsample_block in self.down_blocks:
            __UpperCamelCase	:Optional[int]         =						downsample_block(hidden_states=lowercase__					,					temb=lowercase__)
            down_block_res_samples += res_samples

        # 3. mid
        if self.mid_block:
            __UpperCamelCase	:Union[str, Any]         =						self.mid_block(lowercase__					,					lowercase__)

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

        # 5. post-process
        if self.out_block:
            __UpperCamelCase	:Optional[int]         =						self.out_block(lowercase__					,					lowercase__)

        if not return_dict:
            return (sample,)

        return UNetaDOutput(sample=lowercase__)









import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	inspect.getfile(accelerate.test_utils)
							__UpperCAmelCase			:							Dict	    =	os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_script.py'''])
							__UpperCAmelCase			:							Union[str, Any]	    =	os.path.sep.join(inspect.getfile(self.__class__).split(os.path.sep)[:-1])


	@require_tpu
	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	F"\n            {self.test_dir}/xla_spawn.py\n            --num_cores 8\n            {self.test_file_path}\n        ".split()
							__UpperCAmelCase			:							Union[str, Any]	    =	[sys.executable] + distributed_args
							execute_subprocess_async(lowercase__				,     env=os.environ.copy())

