"""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        __snake_case	(					__lowerCAmelCase    ):
      a__				=		42







class        __snake_case	(					__lowerCAmelCase	,				__lowerCAmelCase    ):



      @register_to_config
      def __init__(						self   ,      lowercase = 6_55_36   ,      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   ,      )     ->      List[Any]:





        '''simple docstring'''





        super().__init__()
        a__:					int      							=     sample_size

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

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

        a__:					Optional[int]      							=     nn.ModuleList([])
        a__:					Union[str, Any]      							=     None
        a__:					List[Any]      							=     nn.ModuleList([])
        a__:					Dict      							=     None

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

          if i == 0:
            input_channel += extra_in_channels

          a__:					Optional[int]      							=     i == len(lowercase) - 1

          a__:					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
        a__:					int      							=     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
        a__:					Tuple      							=     list(reversed(lowercase))
        a__:					Optional[int]      							=     reversed_block_out_channels[0]
        if out_block_type is None:
          a__:					List[Any]      							=     out_channels
        else:
          a__:					Optional[Any]      							=     block_out_channels[0]

        for i, up_block_type in enumerate(lowercase):
          a__:					str      							=     output_channel
          a__:					Union[str, Any]      							=     (
              reversed_block_out_channels[i + 1] if i < len(lowercase) - 1 else final_upsample_channels
          )

          a__:					Any      							=     i == len(lowercase) - 1

          a__:					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)
          a__:					Tuple      							=     output_channel

        # out
        a__:					Union[str, Any]      							=     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4   ,      32)
        a__:					Union[str, 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   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = True   ,      )     ->      Union[UNetaDOutput, Tuple]:





        '''simple docstring'''





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

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

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

        # 3. mid
        if self.mid_block:
          a__:					str      							=     self.mid_block(lowercase   ,      lowercase)

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

        # 5. post-process
        if self.out_block:
          a__:					Union[str, Any]      							=     self.out_block(lowercase   ,      lowercase)

        if not return_dict:
          return (sample,)

        return UNetaDOutput(sample=lowercase)



"""simple docstring"""
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class        __snake_case	(					unittest.TestCase    ):



      @property
      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					str      							=     UNetaDModel(
            block_out_channels=(32, 64)   ,      layers_per_block=2   ,      sample_size=32   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,      )
        return model



      @property
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					List[Any]      							=     VQModel(
            block_out_channels=[32, 64]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,      latent_channels=3   ,      )
        return model



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					Optional[int]      							=     CLIPTextConfig(
            bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=32   ,      intermediate_size=37   ,      layer_norm_eps=1e-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=10_00   ,      )
        return CLIPTextModel(lowercase)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.dummy_uncond_unet
        a__:					Optional[int]      							=     DDIMScheduler()
        a__:					Optional[int]      							=     self.dummy_vq_model

        a__:					Union[str, Any]      							=     LDMPipeline(unet=lowercase   ,      vqvae=lowercase   ,      scheduler=lowercase)
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					str      							=     torch.manual_seed(0)
        a__:					Dict      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy').images

        a__:					Union[str, Any]      							=     torch.manual_seed(0)
        a__:					int      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy'   ,      return_dict=lowercase)[0]

        a__:					Union[str, Any]      							=     image[0, -3:, -3:, -1]
        a__:					int      							=     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        a__:					int      							=     np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
        a__:					Optional[Any]      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance







@slow
@require_torch
class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256')
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					List[str]      							=     torch.manual_seed(0)
        a__:					Optional[int]      							=     ldm(generator=lowercase   ,      num_inference_steps=5   ,      output_type='numpy').images

        a__:					Optional[int]      							=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_56, 2_56, 3)
        a__:					int      							=     np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
        a__:					Any      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance


