


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__ (   _SCREAMING_SNAKE_CASE     ):







     """simple docstring"""

     __magic_name__        =		42






class   A__ (   _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE     ):







     """simple docstring"""

     @register_to_config
     def __init__(	self      , __snake_case = 6_5_5_3_6      , __snake_case = None      , __snake_case = 2      , __snake_case = 2      , __snake_case = 0      , __snake_case = "fourier"      , __snake_case = True      , __snake_case = False      , __snake_case = 0.0      , __snake_case = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      , __snake_case = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      , __snake_case = "UNetMidBlock1D"      , __snake_case = None      , __snake_case = (3_2, 3_2, 6_4)      , __snake_case = None      , __snake_case = 8      , __snake_case = 1      , __snake_case = False      , ):
          super().__init__()
          snake_case					=    sample_size

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

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

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

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

               if i == 0:
                    input_channel += extra_in_channels

               snake_case					=    i == len(__snake_case   ) - 1

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

          # mid
          snake_case					=    get_mid_block(
              __snake_case      , 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=__snake_case      , add_downsample=__snake_case      , )

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

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

               snake_case					=    i == len(__snake_case   ) - 1

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

          # out
          snake_case					=    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4      , 3_2   )
          snake_case					=    get_out_block(
              out_block_type=__snake_case      , num_groups_out=__snake_case      , embed_dim=block_out_channels[0]      , out_channels=__snake_case      , act_fn=__snake_case      , fc_dim=block_out_channels[-1] // 4      , )




     def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case = True      , ):
          snake_case					=    timestep
          if not torch.is_tensor(__snake_case   ):
               snake_case					=    torch.tensor([timesteps]      , dtype=torch.long      , device=sample.device   )
          elif torch.is_tensor(__snake_case   ) and len(timesteps.shape   ) == 0:
               snake_case					=    timesteps[None].to(sample.device   )

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

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

          # 3. mid
          if self.mid_block:
               snake_case					=    self.mid_block(__snake_case      , __snake_case   )

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

          # 5. post-process
          if self.out_block:
               snake_case					=    self.out_block(__snake_case      , __snake_case   )

          if not return_dict:
               return (sample,)

          return UNetaDOutput(sample=__snake_case   )








# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(UpperCamelCase_    )

def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_terminal_summary_main

     snake_case					=    terminalreporter.config.getoption('''--make-reports'''    )
     if make_reports:
          pytest_terminal_summary_main(UpperCamelCase_							,id=UpperCamelCase_    )




