





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 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      42







class 			__A       (      lowerCamelCase__ ,lowerCamelCase__  ):




      """simple docstring"""







      @register_to_config
      def __init__(		self      ,							a__ = 6_5536      ,							a__ = None      ,							a__ = 2      ,							a__ = 2      ,							a__ = 0      ,							a__ = "fourier"      ,							a__ = True      ,							a__ = False      ,							a__ = 0.0      ,							a__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      ,							a__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      ,							a__ = "UNetMidBlock1D"      ,							a__ = None      ,							a__ = (32, 32, 64)      ,							a__ = None      ,							a__ = 8      ,							a__ = 1      ,							a__ = False      ,							):



             """simple docstring"""





             super().__init__()
             _lowerCamelCase							:     Any	=		sample_size

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

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

             _lowerCamelCase							:     str	=		nn.ModuleList([])
             _lowerCamelCase							:     Optional[Any]	=		None
             _lowerCamelCase							:     Dict	=		nn.ModuleList([])
             _lowerCamelCase							:     str	=		None

             # down
             _lowerCamelCase							:     Any	=		in_channels
             for i, down_block_type in enumerate(a__):
                    _lowerCamelCase							:     Any	=		output_channel
                    _lowerCamelCase							:     str	=		block_out_channels[i]

                    if i == 0:
                           input_channel += extra_in_channels

                    _lowerCamelCase							:     str	=		i == len(a__) - 1

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

             # mid
             _lowerCamelCase							:     List[Any]	=		get_mid_block(
                 a__      ,							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=a__      ,							add_downsample=a__      ,							)

             # up
             _lowerCamelCase							:     str	=		list(reversed(a__))
             _lowerCamelCase							:     Optional[Any]	=		reversed_block_out_channels[0]
             if out_block_type is None:
                    _lowerCamelCase							:     Any	=		out_channels
             else:
                    _lowerCamelCase							:     Dict	=		block_out_channels[0]

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

                    _lowerCamelCase							:     int	=		i == len(a__) - 1

                    _lowerCamelCase							:     Tuple	=		get_up_block(
                        a__      ,							num_layers=a__      ,							in_channels=a__      ,							out_channels=a__      ,							temb_channels=block_out_channels[0]      ,							add_upsample=not is_final_block      ,							)
                    self.up_blocks.append(a__)
                    _lowerCamelCase							:     Union[str, Any]	=		output_channel

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



      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__ = True      ,							):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		timestep
             if not torch.is_tensor(a__):
                    _lowerCamelCase							:     int	=		torch.tensor([timesteps]      ,							dtype=torch.long      ,							device=sample.device)
             elif torch.is_tensor(a__) and len(timesteps.shape) == 0:
                    _lowerCamelCase							:     Any	=		timesteps[None].to(sample.device)

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

             # 2. down
             _lowerCamelCase							:     Dict	=		()
             for downsample_block in self.down_blocks:
                    _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		downsample_block(hidden_states=a__      ,							temb=a__)
                    down_block_res_samples += res_samples

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

             # 4. up
             for i, upsample_block in enumerate(self.up_blocks):
                    _lowerCamelCase							:     str	=		down_block_res_samples[-1:]
                    _lowerCamelCase							:     int	=		down_block_res_samples[:-1]
                    _lowerCamelCase							:     Tuple	=		upsample_block(a__      ,							res_hidden_states_tuple=a__      ,							temb=a__)

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

             if not return_dict:
                    return (sample,)

             return UNetaDOutput(sample=a__)








import pytest

import datasets


# Import fixture modules as plugins
_lowerCamelCase			       =      ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
       for item in items:
              if any(marker in item.keywords for marker in ['''integration''', '''unit''']      ):
                     continue
              item.add_marker(pytest.mark.unit      )




def    __UpperCAmelCase(      lowercase_      ):
       config.addinivalue_line('''markers''' ,    '''torchaudio_latest: mark test to run with torchaudio>=0.12'''      )




@pytest.fixture(autouse=lowercase_      )
def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
       _lowerCamelCase							:     Optional[Any]	=		tmp_path_factory.getbasetemp() / '''cache'''
       _lowerCamelCase							:     Optional[Any]	=		test_hf_cache_home / '''datasets'''
       _lowerCamelCase							:     Union[str, Any]	=		test_hf_cache_home / '''metrics'''
       _lowerCamelCase							:     Dict	=		test_hf_cache_home / '''modules'''
       monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' ,    str(lowercase_      )      )
       monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' ,    str(lowercase_      )      )
       monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' ,    str(lowercase_      )      )
       _lowerCamelCase							:     str	=		test_hf_datasets_cache / '''downloads'''
       monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' ,    str(lowercase_      )      )
       _lowerCamelCase							:     Optional[int]	=		test_hf_datasets_cache / '''downloads''' / '''extracted'''
       monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' ,    str(lowercase_      )      )




@pytest.fixture(autouse=lowercase_ ,    scope='''session'''      )
def    __UpperCAmelCase(      ):
       datasets.disable_progress_bar()




@pytest.fixture(autouse=lowercase_      )
def    __UpperCAmelCase(      lowercase_      ):
       # don't take tests into account when counting downloads
       monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' ,    lowercase_      )




@pytest.fixture
def    __UpperCAmelCase(      lowercase_      ):
       # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
       # To be removed once SQLAlchemy 2.0 supported
       monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' ,    lowercase_      )

