





"""simple docstring"""





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()




class 							snake_case							(			__UpperCAmelCase      ,  __UpperCAmelCase      ,  __UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       List[Any]         =       StableDiffusionControlNetImgaImgPipeline
      _A     :       List[Any]         =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
      _A     :       Tuple         =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      _A     :       Optional[Any]         =       IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'} )
      _A     :       int         =       IMAGE_TO_IMAGE_IMAGE_PARAMS

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        torch.manual_seed(0 )
        __UpperCAmelCase    :						Union[str, Any]       =						UNetaDConditionModel(
            block_out_channels=(32, 64)						,				layers_per_block=2						,				sample_size=32						,				in_channels=4						,				out_channels=4						,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,				cross_attention_dim=32						,				)
        torch.manual_seed(0 )
        __UpperCAmelCase    :						Dict       =						ControlNetModel(
            block_out_channels=(32, 64)						,				layers_per_block=2						,				in_channels=4						,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,				cross_attention_dim=32						,				conditioning_embedding_out_channels=(16, 32)						,				)
        torch.manual_seed(0 )
        __UpperCAmelCase    :						int       =						DDIMScheduler(
            beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				beta_schedule='''scaled_linear'''						,				clip_sample=__lowercase						,				set_alpha_to_one=__lowercase						,				)
        torch.manual_seed(0 )
        __UpperCAmelCase    :						Dict       =						AutoencoderKL(
            block_out_channels=[32, 64]						,				in_channels=3						,				out_channels=3						,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,				latent_channels=4						,				)
        torch.manual_seed(0 )
        __UpperCAmelCase    :						Dict       =						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=1_000						,				)
        __UpperCAmelCase    :						List[Any]       =						CLIPTextModel(__lowercase )
        __UpperCAmelCase    :						Dict       =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

        __UpperCAmelCase    :						List[Any]       =						{
            '''unet''': unet,
            '''controlnet''': controlnet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      Union[str, Any]						,				__lowercase       :      Tuple=0 ):


        '''simple docstring'''




        if str(__lowercase ).startswith('''mps''' ):
          __UpperCAmelCase    :						List[str]       =						torch.manual_seed(__lowercase )
        else:
          __UpperCAmelCase    :						List[str]       =						torch.Generator(device=__lowercase ).manual_seed(__lowercase )

        __UpperCAmelCase    :						Dict       =						2
        __UpperCAmelCase    :						int       =						randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,				generator=__lowercase						,				device=torch.device(__lowercase )						,				)
        __UpperCAmelCase    :						Union[str, Any]       =						floats_tensor(control_image.shape						,				rng=random.Random(__lowercase ) ).to(__lowercase )
        __UpperCAmelCase    :						Dict       =						image.cpu().permute(0						,				2						,				3						,				1 )[0]
        __UpperCAmelCase    :						Tuple       =						Image.fromarray(np.uinta(__lowercase ) ).convert('''RGB''' ).resize((64, 64) )
        __UpperCAmelCase    :						Optional[int]       =						{
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
            '''image''': image,
            '''control_image''': control_image,
        }

        return inputs

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3 )

      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()						,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''						,				)
      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3 )







      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        self._test_inference_batch_single_identical(expected_max_diff=2e-3 )




class 							snake_case							(			__UpperCAmelCase      ,  __UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       str         =       StableDiffusionControlNetImgaImgPipeline
      _A     :       List[Any]         =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
      _A     :       Optional[int]         =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      _A     :       List[str]         =       frozenset([] )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess

      def       A_					(							self       :      int ):


        '''simple docstring'''




        torch.manual_seed(0 )
        __UpperCAmelCase    :						Tuple       =						UNetaDConditionModel(
            block_out_channels=(32, 64)						,				layers_per_block=2						,				sample_size=32						,				in_channels=4						,				out_channels=4						,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,				cross_attention_dim=32						,				)
        torch.manual_seed(0 )

        def init_weights(__lowercase       :      Any ):
          if isinstance(__lowercase						,				torch.nn.Convad ):
            torch.nn.init.normal(m.weight )
            m.bias.data.fill_(1.0 )

        __UpperCAmelCase    :						str       =						ControlNetModel(
            block_out_channels=(32, 64)						,				layers_per_block=2						,				in_channels=4						,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,				cross_attention_dim=32						,				conditioning_embedding_out_channels=(16, 32)						,				)
        controlneta.controlnet_down_blocks.apply(__lowercase )

        torch.manual_seed(0 )
        __UpperCAmelCase    :						str       =						ControlNetModel(
            block_out_channels=(32, 64)						,				layers_per_block=2						,				in_channels=4						,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,				cross_attention_dim=32						,				conditioning_embedding_out_channels=(16, 32)						,				)
        controlneta.controlnet_down_blocks.apply(__lowercase )

        torch.manual_seed(0 )
        __UpperCAmelCase    :						List[Any]       =						DDIMScheduler(
            beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				beta_schedule='''scaled_linear'''						,				clip_sample=__lowercase						,				set_alpha_to_one=__lowercase						,				)
        torch.manual_seed(0 )
        __UpperCAmelCase    :						Optional[int]       =						AutoencoderKL(
            block_out_channels=[32, 64]						,				in_channels=3						,				out_channels=3						,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,				latent_channels=4						,				)
        torch.manual_seed(0 )
        __UpperCAmelCase    :						Union[str, Any]       =						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=1_000						,				)
        __UpperCAmelCase    :						int       =						CLIPTextModel(__lowercase )
        __UpperCAmelCase    :						int       =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

        __UpperCAmelCase    :						List[Any]       =						MultiControlNetModel([controlneta, controlneta] )

        __UpperCAmelCase    :						str       =						{
            '''unet''': unet,
            '''controlnet''': controlnet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components

      def       A_					(							self       :      Any						,				__lowercase       :      int						,				__lowercase       :      Optional[int]=0 ):


        '''simple docstring'''




        if str(__lowercase ).startswith('''mps''' ):
          __UpperCAmelCase    :						Any       =						torch.manual_seed(__lowercase )
        else:
          __UpperCAmelCase    :						Union[str, Any]       =						torch.Generator(device=__lowercase ).manual_seed(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						2

        __UpperCAmelCase    :						List[Any]       =						[
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,				generator=__lowercase						,				device=torch.device(__lowercase )						,				),
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,				generator=__lowercase						,				device=torch.device(__lowercase )						,				),
        ]

        __UpperCAmelCase    :						Optional[Any]       =						floats_tensor(control_image[0].shape						,				rng=random.Random(__lowercase ) ).to(__lowercase )
        __UpperCAmelCase    :						List[str]       =						image.cpu().permute(0						,				2						,				3						,				1 )[0]
        __UpperCAmelCase    :						Tuple       =						Image.fromarray(np.uinta(__lowercase ) ).convert('''RGB''' ).resize((64, 64) )
        __UpperCAmelCase    :						Dict       =						{
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
            '''image''': image,
            '''control_image''': control_image,
        }

        return inputs

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.get_dummy_components()
        __UpperCAmelCase    :						Any       =						self.pipeline_class(**__lowercase )
        pipe.to(__lowercase )

        __UpperCAmelCase    :						Dict       =						10.0
        __UpperCAmelCase    :						int       =						4

        __UpperCAmelCase    :						Dict       =						self.get_dummy_inputs(__lowercase )
        __UpperCAmelCase    :						Union[str, Any]       =						steps
        __UpperCAmelCase    :						str       =						scale
        __UpperCAmelCase    :						List[Any]       =						pipe(**__lowercase )[0]

        __UpperCAmelCase    :						Optional[Any]       =						self.get_dummy_inputs(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						steps
        __UpperCAmelCase    :						Any       =						scale
        __UpperCAmelCase    :						int       =						pipe(**__lowercase						,				control_guidance_start=0.1						,				control_guidance_end=0.2 )[0]

        __UpperCAmelCase    :						Optional[int]       =						self.get_dummy_inputs(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						steps
        __UpperCAmelCase    :						str       =						scale
        __UpperCAmelCase    :						Optional[int]       =						pipe(**__lowercase						,				control_guidance_start=[0.1, 0.3]						,				control_guidance_end=[0.2, 0.7] )[0]

        __UpperCAmelCase    :						int       =						self.get_dummy_inputs(__lowercase )
        __UpperCAmelCase    :						Dict       =						steps
        __UpperCAmelCase    :						Tuple       =						scale
        __UpperCAmelCase    :						Any       =						pipe(**__lowercase						,				control_guidance_start=0.4						,				control_guidance_end=[0.5, 0.8] )[0]

        # make sure that all outputs are different
        assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
        assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
        assert np.sum(np.abs(output_a - output_a ) ) > 1e-3

      def       A_					(							self       :      int ):


        '''simple docstring'''




        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3 )

      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()						,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''						,				)
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3 )

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        self._test_inference_batch_single_identical(expected_max_diff=2e-3 )







      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						self.get_dummy_components()
        __UpperCAmelCase    :						Tuple       =						self.pipeline_class(**__lowercase )
        pipe.to(__lowercase )
        pipe.set_progress_bar_config(disable=__lowercase )
        with tempfile.TemporaryDirectory() as tmpdir:
          try:
            # save_pretrained is not implemented for Multi-ControlNet
            pipe.save_pretrained(__lowercase )
          except NotImplementedError:
            pass




@slow
@require_torch_gpu
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      str ):


        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny''' )

        __UpperCAmelCase    :						Tuple       =						StableDiffusionControlNetImgaImgPipeline.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''						,				safety_checker=__lowercase						,				controlnet=__lowercase )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=__lowercase )

        __UpperCAmelCase    :						Optional[Any]       =						torch.Generator(device='''cpu''' ).manual_seed(0 )
        __UpperCAmelCase    :						str       =						'''evil space-punk bird'''
        __UpperCAmelCase    :						int       =						load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png''' ).resize((512, 512) )
        __UpperCAmelCase    :						Any       =						load_image(
            '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png''' ).resize((512, 512) )

        __UpperCAmelCase    :						Optional[int]       =						pipe(
            __lowercase						,				__lowercase						,				control_image=__lowercase						,				generator=__lowercase						,				output_type='''np'''						,				num_inference_steps=50						,				strength=0.6						,				)

        __UpperCAmelCase    :						List[str]       =						output.images[0]

        assert image.shape == (512, 512, 3)

        __UpperCAmelCase    :						int       =						load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy''' )

        assert np.abs(expected_image - image ).max() < 9e-2






"""simple docstring"""





from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin




@dataclass
class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       torch.FloatTensor




class 							snake_case							(			__UpperCAmelCase      ,  __UpperCAmelCase ):

      '''simple docstring'''



      @register_to_config
      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      int = 32						,				__lowercase       :      int = 64						,				__lowercase       :      int = 20						,				__lowercase       :      int = 768						,				__lowercase       :      Optional[int]=77						,				__lowercase       :      Union[str, Any]=4						,				__lowercase       :      float = 0.0						,				__lowercase       :      str = "silu"						,				__lowercase       :      Optional[str] = None						,				__lowercase       :      Optional[str] = None						,				__lowercase       :      Optional[str] = "linear"						,				__lowercase       :      Optional[str] = "prd"						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Optional[int] = None						,				__lowercase       :      Optional[int] = None						,				):


        '''simple docstring'''




        super().__init__()
        __UpperCAmelCase    :						int       =						num_attention_heads
        __UpperCAmelCase    :						List[Any]       =						attention_head_dim
        __UpperCAmelCase    :						int       =						num_attention_heads * attention_head_dim
        __UpperCAmelCase    :						List[str]       =						additional_embeddings

        __UpperCAmelCase    :						Optional[int]       =						time_embed_dim or inner_dim
        __UpperCAmelCase    :						Tuple       =						embedding_proj_dim or embedding_dim
        __UpperCAmelCase    :						Dict       =						clip_embed_dim or embedding_dim

        __UpperCAmelCase    :						Dict       =						Timesteps(__lowercase						,				__lowercase						,				0 )
        __UpperCAmelCase    :						List[str]       =						TimestepEmbedding(__lowercase						,				__lowercase						,				out_dim=__lowercase						,				act_fn=__lowercase )

        __UpperCAmelCase    :						Any       =						nn.Linear(__lowercase						,				__lowercase )

        if embedding_proj_norm_type is None:
          __UpperCAmelCase    :						Dict       =						None
        elif embedding_proj_norm_type == "layer":
          __UpperCAmelCase    :						Any       =						nn.LayerNorm(__lowercase )
        else:
          raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}''' )

        __UpperCAmelCase    :						List[Any]       =						nn.Linear(__lowercase						,				__lowercase )

        if encoder_hid_proj_type is None:
          __UpperCAmelCase    :						int       =						None
        elif encoder_hid_proj_type == "linear":
          __UpperCAmelCase    :						int       =						nn.Linear(__lowercase						,				__lowercase )
        else:
          raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}''' )

        __UpperCAmelCase    :						str       =						nn.Parameter(torch.zeros(1						,				num_embeddings + additional_embeddings						,				__lowercase ) )

        if added_emb_type == "prd":
          __UpperCAmelCase    :						Optional[int]       =						nn.Parameter(torch.zeros(1						,				1						,				__lowercase ) )
        elif added_emb_type is None:
          __UpperCAmelCase    :						str       =						None
        else:
          raise ValueError(
              f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.''' )

        __UpperCAmelCase    :						Union[str, Any]       =						nn.ModuleList(
            [
                BasicTransformerBlock(
                    __lowercase						,				__lowercase						,				__lowercase						,				dropout=__lowercase						,				activation_fn='''gelu'''						,				attention_bias=__lowercase						,				)
                for d in range(__lowercase )
            ] )

        if norm_in_type == "layer":
          __UpperCAmelCase    :						Optional[Any]       =						nn.LayerNorm(__lowercase )
        elif norm_in_type is None:
          __UpperCAmelCase    :						List[Any]       =						None
        else:
          raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.''' )

        __UpperCAmelCase    :						str       =						nn.LayerNorm(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						nn.Linear(__lowercase						,				__lowercase )

        __UpperCAmelCase    :						List[Any]       =						torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]						,				-1_0_0_0_0.0 )
        causal_attention_mask.triu_(1 )
        __UpperCAmelCase    :						Optional[Any]       =						causal_attention_mask[None, ...]
        self.register_buffer('''causal_attention_mask'''						,				__lowercase						,				persistent=__lowercase )

        __UpperCAmelCase    :						Any       =						nn.Parameter(torch.zeros(1						,				__lowercase ) )
        __UpperCAmelCase    :						List[str]       =						nn.Parameter(torch.zeros(1						,				__lowercase ) )

      @property
      # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						{}

        def fn_recursive_add_processors(__lowercase       :      str						,				__lowercase       :      torch.nn.Module						,				__lowercase       :      Dict[str, AttentionProcessor] ):
          if hasattr(__lowercase						,				'''set_processor''' ):
            __UpperCAmelCase    :						Optional[Any]       =						module.processor

          for sub_name, child in module.named_children():
            fn_recursive_add_processors(f'''{name}.{sub_name}'''						,				__lowercase						,				__lowercase )

          return processors

        for name, module in self.named_children():
          fn_recursive_add_processors(__lowercase						,				__lowercase						,				__lowercase )

        return processors

      def       A_					(							self       :      Any						,				__lowercase       :      Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						len(self.attn_processors.keys() )

        if isinstance(__lowercase						,				__lowercase ) and len(__lowercase ) != count:
          raise ValueError(
              f'''A dict of processors was passed, but the number of processors {len(__lowercase )} does not match the'''
              f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )

        def fn_recursive_attn_processor(__lowercase       :      str						,				__lowercase       :      torch.nn.Module						,				__lowercase       :      int ):
          if hasattr(__lowercase						,				'''set_processor''' ):
            if not isinstance(__lowercase						,				__lowercase ):
              module.set_processor(__lowercase )
            else:
              module.set_processor(processor.pop(f'''{name}.processor''' ) )

          for sub_name, child in module.named_children():
            fn_recursive_attn_processor(f'''{name}.{sub_name}'''						,				__lowercase						,				__lowercase )

        for name, module in self.named_children():
          fn_recursive_attn_processor(__lowercase						,				__lowercase						,				__lowercase )

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        self.set_attn_processor(AttnProcessor() )

      def       A_					(							self       :      Optional[int]						,				__lowercase       :      Dict						,				__lowercase       :      Union[torch.Tensor, float, int]						,				__lowercase       :      torch.FloatTensor						,				__lowercase       :      Optional[torch.FloatTensor] = None						,				__lowercase       :      Optional[torch.BoolTensor] = None						,				__lowercase       :      bool = True						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						hidden_states.shape[0]

        __UpperCAmelCase    :						Any       =						timestep
        if not torch.is_tensor(__lowercase ):
          __UpperCAmelCase    :						Union[str, Any]       =						torch.tensor([timesteps]						,				dtype=torch.long						,				device=hidden_states.device )
        elif torch.is_tensor(__lowercase ) and len(timesteps.shape ) == 0:
          __UpperCAmelCase    :						Union[str, Any]       =						timesteps[None].to(hidden_states.device )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        __UpperCAmelCase    :						List[str]       =						timesteps * torch.ones(__lowercase						,				dtype=timesteps.dtype						,				device=timesteps.device )

        __UpperCAmelCase    :						List[str]       =						self.time_proj(__lowercase )

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        __UpperCAmelCase    :						Any       =						timesteps_projected.to(dtype=self.dtype )
        __UpperCAmelCase    :						List[str]       =						self.time_embedding(__lowercase )

        if self.embedding_proj_norm is not None:
          __UpperCAmelCase    :						Dict       =						self.embedding_proj_norm(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						self.embedding_proj(__lowercase )
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
          __UpperCAmelCase    :						Dict       =						self.encoder_hidden_states_proj(__lowercase )
        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
          raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set''' )

        __UpperCAmelCase    :						int       =						self.proj_in(__lowercase )

        __UpperCAmelCase    :						Tuple       =						self.positional_embedding.to(hidden_states.dtype )

        __UpperCAmelCase    :						Optional[Any]       =						[]
        __UpperCAmelCase    :						str       =						0

        if encoder_hidden_states is not None:
          additional_embeds.append(__lowercase )
          additional_embeddings_len += encoder_hidden_states.shape[1]

        if len(proj_embeddings.shape ) == 2:
          __UpperCAmelCase    :						str       =						proj_embeddings[:, None, :]

        if len(hidden_states.shape ) == 2:
          __UpperCAmelCase    :						Optional[int]       =						hidden_states[:, None, :]

        __UpperCAmelCase    :						str       =						additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
          __UpperCAmelCase    :						Dict       =						self.prd_embedding.to(hidden_states.dtype ).expand(__lowercase						,				-1						,				-1 )
          additional_embeds.append(__lowercase )

        __UpperCAmelCase    :						Optional[Any]       =						torch.cat(
            __lowercase						,				dim=1						,				)

        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
        __UpperCAmelCase    :						Optional[int]       =						additional_embeddings_len + proj_embeddings.shape[1] + 1
        if positional_embeddings.shape[1] < hidden_states.shape[1]:
          __UpperCAmelCase    :						List[str]       =						F.pad(
              __lowercase						,				(
                  0,
                  0,
                  additional_embeddings_len,
                  self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
              )						,				value=0.0						,				)

        __UpperCAmelCase    :						Dict       =						hidden_states + positional_embeddings

        if attention_mask is not None:
          __UpperCAmelCase    :						Optional[Any]       =						(1 - attention_mask.to(hidden_states.dtype )) * -1_0_0_0_0.0
          __UpperCAmelCase    :						Optional[Any]       =						F.pad(__lowercase						,				(0, self.additional_embeddings)						,				value=0.0 )
          __UpperCAmelCase    :						Dict       =						(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
          __UpperCAmelCase    :						Tuple       =						attention_mask.repeat_interleave(self.config.num_attention_heads						,				dim=0 )

        if self.norm_in is not None:
          __UpperCAmelCase    :						str       =						self.norm_in(__lowercase )

        for block in self.transformer_blocks:
          __UpperCAmelCase    :						Dict       =						block(__lowercase						,				attention_mask=__lowercase )

        __UpperCAmelCase    :						Any       =						self.norm_out(__lowercase )

        if self.prd_embedding is not None:
          __UpperCAmelCase    :						List[Any]       =						hidden_states[:, -1]
        else:
          __UpperCAmelCase    :						List[str]       =						hidden_states[:, additional_embeddings_len:]

        __UpperCAmelCase    :						Optional[int]       =						self.proj_to_clip_embeddings(__lowercase )

        if not return_dict:
          return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=__lowercase )







      def       A_					(							self       :      List[Any]						,				__lowercase       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						(prior_latents * self.clip_std) + self.clip_mean
        return prior_latents