



"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase  			=		logging.get_logger(__name__)
def   a__      ( lowerCAmelCase__ ):
      # initialize config
      if "resnet-50" in model_name:
            UpperCAmelCase_     					=				ResNetConfig.from_pretrained("microsoft/resnet-50" )
      elif "resnet-101" in model_name:
            UpperCAmelCase_     					=				ResNetConfig.from_pretrained("microsoft/resnet-101" )
      else:
            raise ValueError("Model name should include either resnet50 or resnet101" )

      UpperCAmelCase_     					=				DetrConfig(use_timm_backbone=lowerCAmelCase__      ,     backbone_config=lowerCAmelCase__ )

      # set label attributes
      UpperCAmelCase_     					=				"panoptic" in model_name
      if is_panoptic:
            UpperCAmelCase_     					=				250
      else:
            UpperCAmelCase_     					=				91
            UpperCAmelCase_     					=				"huggingface/label-files"
            UpperCAmelCase_     					=				"coco-detection-id2label.json"
            UpperCAmelCase_     					=				json.load(open(hf_hub_download(lowerCAmelCase__      ,     lowerCAmelCase__      ,     repo_type="dataset" )      ,     "r" ) )
            UpperCAmelCase_     					=				{int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
            UpperCAmelCase_     					=				idalabel
            UpperCAmelCase_     					=				{v: k for k, v in idalabel.items()}

      return config, is_panoptic
def   a__      ( lowerCAmelCase__ ):
      # here we list all keys to be renamed (original name on the left, our name on the right)
      UpperCAmelCase_     					=				[]

      # stem
      # fmt: off
      rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight") )
      rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight") )
      rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias") )
      rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean") )
      rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var") )
      # stages
      for stage_idx in range(len(config.backbone_config.depths ) ):
            for layer_idx in range(config.backbone_config.depths[stage_idx] ):
                  # shortcut
                  if layer_idx == 0:
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                            ) )
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                            ) )
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                            ) )
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                            ) )
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                            ) )
                  # 3 convs
                  for i in range(3 ):
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                            ) )
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                            ) )
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                            ) )
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                            ) )
                        rename_keys.append(
                            (
                                f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                                f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                            ) )
    # fmt: on

      for i in range(config.encoder_layers ):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append(
                (
                    f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
                    f"""encoder.layers.{i}.self_attn.out_proj.weight""",
                ) )
            rename_keys.append(
                (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias""") )
            rename_keys.append(
                (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""") )
            rename_keys.append(
                (f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias""") )
            rename_keys.append(
                (f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias""") )
            # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
            rename_keys.append(
                (
                    f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
                    f"""decoder.layers.{i}.self_attn.out_proj.weight""",
                ) )
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""") )
            rename_keys.append(
                (
                    f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
                    f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
                ) )
            rename_keys.append(
                (
                    f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
                    f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
                ) )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias""") )
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""") )
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias""") )
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""") )
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""") )
            rename_keys.append(
                (f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias""") )

      # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
      rename_keys.extend(
          [
              ("input_proj.weight", "input_projection.weight"),
              ("input_proj.bias", "input_projection.bias"),
              ("query_embed.weight", "query_position_embeddings.weight"),
              ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
              ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
              ("class_embed.weight", "class_labels_classifier.weight"),
              ("class_embed.bias", "class_labels_classifier.bias"),
              ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
              ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
              ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
              ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
              ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
              ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
          ] )

      return rename_keys
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				state_dict.pop(lowerCAmelCase__ )
      UpperCAmelCase_     					=				val
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__=False ):
      UpperCAmelCase_     					=				""
      if is_panoptic:
            UpperCAmelCase_     					=				"detr."

      # first: transformer encoder
      for i in range(6 ):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            UpperCAmelCase_     					=				state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
            UpperCAmelCase_     					=				state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_     					=				in_proj_weight[:256, :]
            UpperCAmelCase_     					=				in_proj_bias[:256]
            UpperCAmelCase_     					=				in_proj_weight[256:512, :]
            UpperCAmelCase_     					=				in_proj_bias[256:512]
            UpperCAmelCase_     					=				in_proj_weight[-256:, :]
            UpperCAmelCase_     					=				in_proj_bias[-256:]
      # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
      for i in range(6 ):
            # read in weights + bias of input projection layer of self-attention
            UpperCAmelCase_     					=				state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
            UpperCAmelCase_     					=				state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_     					=				in_proj_weight[:256, :]
            UpperCAmelCase_     					=				in_proj_bias[:256]
            UpperCAmelCase_     					=				in_proj_weight[256:512, :]
            UpperCAmelCase_     					=				in_proj_bias[256:512]
            UpperCAmelCase_     					=				in_proj_weight[-256:, :]
            UpperCAmelCase_     					=				in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            UpperCAmelCase_     					=				state_dict.pop(
                f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
            UpperCAmelCase_     					=				state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
            # next, add query, keys and values (in that order) of cross-attention to the state dict
            UpperCAmelCase_     					=				in_proj_weight_cross_attn[:256, :]
            UpperCAmelCase_     					=				in_proj_bias_cross_attn[:256]
            UpperCAmelCase_     					=				in_proj_weight_cross_attn[256:512, :]
            UpperCAmelCase_     					=				in_proj_bias_cross_attn[256:512]
            UpperCAmelCase_     					=				in_proj_weight_cross_attn[-256:, :]
            UpperCAmelCase_     					=				in_proj_bias_cross_attn[-256:]
def   a__      ( ):
      UpperCAmelCase_     					=				"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_     					=				Image.open(requests.get(lowerCAmelCase__      ,     stream=lowerCAmelCase__ ).raw )

      return im
@torch.no_grad()
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__=None      ,     lowerCAmelCase__=False ):
      UpperCAmelCase_					,       UpperCAmelCase_     					=				get_detr_config(lowerCAmelCase__ )

      # load original model from torch hub
      UpperCAmelCase_     					=				{
          "detr-resnet-50": "detr_resnet50",
          "detr-resnet-101": "detr_resnet101",
      }
      logger.info(f"""Converting model {model_name}...""" )
      UpperCAmelCase_     					=				torch.hub.load("facebookresearch/detr"      ,     model_name_to_original_name[model_name]      ,     pretrained=lowerCAmelCase__ ).eval()
      UpperCAmelCase_     					=				detr.state_dict()
      # rename keys
      for src, dest in create_rename_keys(lowerCAmelCase__ ):
            if is_panoptic:
                  UpperCAmelCase_     					=				"detr." + src
            rename_key(lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ )
      # query, key and value matrices need special treatment
      read_in_q_k_v(lowerCAmelCase__      ,     is_panoptic=lowerCAmelCase__ )
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      UpperCAmelCase_     					=				"detr.model." if is_panoptic else "model."
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith("detr" )
                      and not key.startswith("class_labels_classifier" )
                      and not key.startswith("bbox_predictor" )
                  ):
                        UpperCAmelCase_     					=				state_dict.pop(lowerCAmelCase__ )
                        UpperCAmelCase_     					=				val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        UpperCAmelCase_     					=				state_dict.pop(lowerCAmelCase__ )
                        UpperCAmelCase_     					=				val
                  elif key.startswith("bbox_attention" ) or key.startswith("mask_head" ):
                        continue
                  else:
                        UpperCAmelCase_     					=				state_dict.pop(lowerCAmelCase__ )
                        UpperCAmelCase_     					=				val
            else:
                  if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
                        UpperCAmelCase_     					=				state_dict.pop(lowerCAmelCase__ )
                        UpperCAmelCase_     					=				val

    # finally, create HuggingFace model and load state dict
      UpperCAmelCase_     					=				DetrForSegmentation(lowerCAmelCase__ ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__ )
      model.load_state_dict(lowerCAmelCase__ )
      model.eval()

      # verify our conversion on an image
      UpperCAmelCase_     					=				"coco_panoptic" if is_panoptic else "coco_detection"
      UpperCAmelCase_     					=				DetrImageProcessor(format=lowerCAmelCase__ )

      UpperCAmelCase_     					=				processor(images=prepare_img()      ,     return_tensors="pt" )
      UpperCAmelCase_     					=				encoding["pixel_values"]

      UpperCAmelCase_     					=				detr(lowerCAmelCase__ )
      UpperCAmelCase_     					=				model(lowerCAmelCase__ )

      assert torch.allclose(outputs.logits      ,     original_outputs["pred_logits"]      ,     atol=1e-3 )
      assert torch.allclose(outputs.pred_boxes      ,     original_outputs["pred_boxes"]      ,     atol=1e-3 )
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks      ,     original_outputs["pred_masks"]      ,     atol=1e-4 )
      print("Looks ok!" )

      if pytorch_dump_folder_path is not None:
            # Save model and image processor
            logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
            Path(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
            model.save_pretrained(lowerCAmelCase__ )
            processor.save_pretrained(lowerCAmelCase__ )

      if push_to_hub:
            # Upload model and image processor to the hub
            logger.info("Uploading PyTorch model and image processor to the hub..." )
            model.push_to_hub(f"""nielsr/{model_name}""" )
            processor.push_to_hub(f"""nielsr/{model_name}""" )


if __name__ == "__main__":
  lowerCamelCase  			=		argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      default="""detr-resnet-50""",
      type=str,
      choices=["""detr-resnet-50""", """detr-resnet-101"""],
      help="""Name of the DETR model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
  lowerCamelCase  			=		parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""




import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 						a__		( a_,     a_,     unittest.TestCase		):
   __lowerCAmelCase				=							StableDiffusionXLImgaImgPipeline
   __lowerCAmelCase				=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
   __lowerCAmelCase				=							PipelineTesterMixin.required_optional_params - {"""latents"""}
   __lowerCAmelCase				=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   __lowerCAmelCase				=							IMAGE_TO_IMAGE_IMAGE_PARAMS
   __lowerCAmelCase				=							IMAGE_TO_IMAGE_IMAGE_PARAMS
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							str									=				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")      ,       attention_head_dim=(2, 4)      ,       use_linear_projection=_a      ,       addition_embed_type="text_time"      ,       addition_time_embed_dim=8      ,       transformer_layers_per_block=(1, 2)      ,       projection_class_embeddings_input_dim=80      ,       cross_attention_dim=64      ,       )
          lowercase       :							Optional[Any]									=				EulerDiscreteScheduler(
              beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       steps_offset=1      ,       beta_schedule="scaled_linear"      ,       timestep_spacing="leading"      ,       )
          torch.manual_seed(0							)
          lowercase       :							Any									=				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      ,       sample_size=128      ,       )
          torch.manual_seed(0							)
          lowercase       :							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      ,       hidden_act="gelu"      ,       projection_dim=32      ,       )
          lowercase       :							str									=				CLIPTextModel(_a							)
          lowercase       :							List[str]									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      ,       local_files_only=_a							)

          lowercase       :							Any									=				CLIPTextModelWithProjection(_a							)
          lowercase       :							Union[str, Any]									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      ,       local_files_only=_a							)

          lowercase       :							Any									=				{
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "text_encoder_2": text_encoder_a,
              "tokenizer_2": tokenizer_a,
              # "safety_checker": None,
              # "feature_extractor": None,
          }
          return components
   def      __magic_name__     (     self      ,       _a      ,       _a=0							):
          lowercase       :							Dict									=				floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(_a							)							).to(_a							)
          lowercase       :							List[Any]									=				image / 2 + 0.5
          if str(_a							).startswith("mps"							):
                 lowercase       :							Optional[int]									=				torch.manual_seed(_a							)
          else:
                 lowercase       :							str									=				torch.Generator(device=_a							).manual_seed(_a							)
          lowercase       :							Optional[int]									=				{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 5.0,
              "output_type": "numpy",
              "strength": 0.7_5,
          }
          return inputs
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase       :							Optional[int]									=				self.get_dummy_components()
          lowercase       :							str									=				StableDiffusionXLImgaImgPipeline(**_a							)
          lowercase       :							List[Any]									=				sd_pipe.to(_a							)
          sd_pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Optional[Any]									=				self.get_dummy_inputs(_a							)
          lowercase       :							Tuple									=				sd_pipe(**_a							).images
          lowercase       :							Dict									=				image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)

          lowercase       :							Tuple									=				np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
   def      __magic_name__     (     self							):
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3							)
   def      __magic_name__     (     self							):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3							)
   def      __magic_name__     (     self							):
          pass

   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				self.get_dummy_components()
          lowercase       :							Optional[Any]									=				StableDiffusionXLImgaImgPipeline(**_a							)
          lowercase       :							Any									=				sd_pipe.to(_a							)
          lowercase       :							Any									=				sd_pipe.to(_a							)
          sd_pipe.set_progress_bar_config(disable=_a							)

          # forward without prompt embeds
          lowercase       :							Union[str, Any]									=				self.get_dummy_inputs(_a							)
          lowercase       :							int									=				3 * ["this is a negative prompt"]
          lowercase       :							Any									=				negative_prompt
          lowercase       :							List[str]									=				3 * [inputs["prompt"]]

          lowercase       :							Optional[int]									=				sd_pipe(**_a							)
          lowercase       :							List[Any]									=				output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          lowercase       :							Any									=				self.get_dummy_inputs(_a							)
          lowercase       :							List[str]									=				3 * ["this is a negative prompt"]
          lowercase       :							Optional[Any]									=				3 * [inputs.pop("prompt"							)]

          (
              (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       
          )       :							List[Any]									=				sd_pipe.encode_prompt(_a      ,       negative_prompt=_a							)

          lowercase       :							int									=				sd_pipe(
              **_a      ,       prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       pooled_prompt_embeds=_a      ,       negative_pooled_prompt_embeds=_a      ,       )
          lowercase       :							Dict									=				output.images[0, -3:, -3:, -1]

          # make sure that it's equal
          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()							).max() < 1E-4


@slow
@require_torch_gpu
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   def      __magic_name__     (     self      ,       _a      ,       _a="cpu"      ,       _a=torch.floataa      ,       _a=0							):
          lowercase       :							Optional[int]									=				torch.Generator(device=_a							).manual_seed(_a							)
          lowercase       :							Any									=				np.random.RandomState(_a							).standard_normal((1, 4, 64, 64)							)
          lowercase       :							List[str]									=				torch.from_numpy(_a							).to(device=_a      ,       dtype=_a							)
          lowercase       :							List[Any]									=				{
              "prompt": "a photograph of an astronaut riding a horse",
              "latents": latents,
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs

   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Any									=				self.get_inputs(_a							)
          lowercase       :							int									=				pipe(**_a							).images
          lowercase       :							Any									=				image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          lowercase       :							Optional[Any]									=				np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]							)
          assert np.abs(image_slice - expected_slice							).max() < 7E-3


