code: import argparse
  import os
  
  import align
  import numpy as np
  import requests
  import tensorflow as tf
  import torch
  from PIL import Image
  from tokenizer import Tokenizer
  
  from transformers import (
      AlignConfig,
      AlignModel,
      AlignProcessor,
      BertConfig,
      BertTokenizer,
      EfficientNetConfig,
      EfficientNetImageProcessor,
  )
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  _SCREAMING_SNAKE_CASE   						=    logging.get_logger(__name__)
  def 				__lowercase				(						lowerCamelCase__					)				->str:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     tf.image.resize(lowerCamelCase__ ,				(3_46, 3_46)					)
    a__     		=     tf.image.crop_to_bounding_box(lowerCamelCase__ ,				(3_46 - 2_89) // 2 ,				(3_46 - 2_89) // 2 ,				2_89 ,				2_89					)
    return image
  def 				__lowercase				(						)				->Union[str, Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     EfficientNetConfig.from_pretrained("google/efficientnet-b7"					)
    a__     		=     2_89
    a__     		=     6_40
    a__     		=     {"0": "LABEL_0", "1": "LABEL_1"}
    a__     		=     {"LABEL_0": 0, "LABEL_1": 1}
    a__     		=     []
  
    a__     		=     BertConfig()
    a__     		=     AlignConfig.from_text_vision_configs(
        text_config=lowerCamelCase__ ,				vision_config=lowerCamelCase__ ,				projection_dim=6_40					)
    return config
  def 				__lowercase				(						)				->Optional[Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     "http://images.cocodataset.org/val2017/000000039769.jpg"
    a__     		=     Image.open(requests.get(lowerCamelCase__ ,				stream=lowerCamelCase__					).raw					)
    return im
  def 				__lowercase				(						)				->List[Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     EfficientNetImageProcessor(
        do_center_crop=lowerCamelCase__ ,				rescale_factor=1 / 127.5 ,				rescale_offset=lowerCamelCase__ ,				do_normalize=lowerCamelCase__ ,				include_top=lowerCamelCase__ ,				resample=Image.BILINEAR ,				)
    a__     		=     BertTokenizer.from_pretrained("bert-base-uncased"					)
    a__     		=     64
    a__     		=     AlignProcessor(image_processor=lowerCamelCase__ ,				tokenizer=lowerCamelCase__					)
    return processor
  def 				__lowercase				(						lowerCamelCase__					)				->Union[str, Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     [v.split("_"					)[0].split("block"					)[1] for v in original_param_names if v.startswith("block"					)]
    a__     		=     list(set(lowerCamelCase__					)					)
    a__     		=     sorted(lowerCamelCase__					)
    a__     		=     len(lowerCamelCase__					)
    a__     		=     {b: str(lowerCamelCase__					) for b, i in zip(lowerCamelCase__ ,				range(lowerCamelCase__					)					)}
  
    a__     		=     []
    rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")					)
    rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")					)
    rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")					)
    rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")					)
    rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")					)
  
    for b in block_names:
      a__     		=     block_name_mapping[b]
      rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight")					)
      rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight")					)
      rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias")					)
      rename_keys.append(
          (f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")					)
      rename_keys.append(
          (f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")					)
      rename_keys.append(
          (f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")					)
      rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight")					)
      rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias")					)
      rename_keys.append(
          (f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")					)
      rename_keys.append(
          (f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")					)
  
      rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight")					)
      rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias")					)
      rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight")					)
      rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias")					)
      rename_keys.append(
          (f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight")					)
      rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight")					)
      rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias")					)
      rename_keys.append(
          (f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean")					)
      rename_keys.append(
          (f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var")					)
  
    a__     		=     {}
    for item in rename_keys:
      if item[0] in original_param_names:
        a__     		=     "vision_model." + item[1]
  
      # BERT text encoder
    a__     		=     []
    a__     		=     "tf_bert_model/bert"
    a__     		=     "text_model"
    for i in range(12					):
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/query/kernel:0",
              f"{new}.encoder.layer.{i}.attention.self.query.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/query/bias:0",
              f"{new}.encoder.layer.{i}.attention.self.query.bias",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/key/kernel:0",
              f"{new}.encoder.layer.{i}.attention.self.key.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/key/bias:0",
              f"{new}.encoder.layer.{i}.attention.self.key.bias",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/value/kernel:0",
              f"{new}.encoder.layer.{i}.attention.self.value.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/self/value/bias:0",
              f"{new}.encoder.layer.{i}.attention.self.value.bias",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/output/dense/kernel:0",
              f"{new}.encoder.layer.{i}.attention.output.dense.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/output/dense/bias:0",
              f"{new}.encoder.layer.{i}.attention.output.dense.bias",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0",
              f"{new}.encoder.layer.{i}.attention.output.LayerNorm.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0",
              f"{new}.encoder.layer.{i}.attention.output.LayerNorm.bias",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/intermediate/dense/kernel:0",
              f"{new}.encoder.layer.{i}.intermediate.dense.weight",
          )					)
      rename_keys.append(
          (
              f"{old}/encoder/layer_._{i}/intermediate/dense/bias:0",
              f"{new}.encoder.layer.{i}.intermediate.dense.bias",
          )					)
      rename_keys.append(
          (f"{old}/encoder/layer_._{i}/output/dense/kernel:0", f"{new}.encoder.layer.{i}.output.dense.weight")					)
      rename_keys.append(
          (f"{old}/encoder/layer_._{i}/output/dense/bias:0", f"{new}.encoder.layer.{i}.output.dense.bias")					)
      rename_keys.append(
          (f"{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0", f"{new}.encoder.layer.{i}.output.LayerNorm.weight")					)
      rename_keys.append(
          (f"{old}/encoder/layer_._{i}/output/LayerNorm/beta:0", f"{new}.encoder.layer.{i}.output.LayerNorm.bias")					)
  
    rename_keys.append((f"{old}/embeddings/word_embeddings/weight:0", f"{new}.embeddings.word_embeddings.weight")					)
    rename_keys.append(
        (f"{old}/embeddings/position_embeddings/embeddings:0", f"{new}.embeddings.position_embeddings.weight")					)
    rename_keys.append(
        (f"{old}/embeddings/token_type_embeddings/embeddings:0", f"{new}.embeddings.token_type_embeddings.weight")					)
    rename_keys.append((f"{old}/embeddings/LayerNorm/gamma:0", f"{new}.embeddings.LayerNorm.weight")					)
    rename_keys.append((f"{old}/embeddings/LayerNorm/beta:0", f"{new}.embeddings.LayerNorm.bias")					)
  
    rename_keys.append((f"{old}/pooler/dense/kernel:0", f"{new}.pooler.dense.weight")					)
    rename_keys.append((f"{old}/pooler/dense/bias:0", f"{new}.pooler.dense.bias")					)
    rename_keys.append(("dense/kernel:0", "text_projection.weight")					)
    rename_keys.append(("dense/bias:0", "text_projection.bias")					)
    rename_keys.append(("dense/bias:0", "text_projection.bias")					)
    rename_keys.append(("temperature:0", "temperature")					)
  
    for item in rename_keys:
      if item[0] in original_param_names:
        a__     		=     item[1]
    return key_mapping
  def 				__lowercase				(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[Any]:
  
    '''simple docstring'''
  
  
  
  
    list(hf_params.keys()					)
  
    for key, value in tf_params.items():
      if key not in key_mapping:
        continue
  
      a__     		=     key_mapping[key]
      if "_conv" in key and "kernel" in key:
        a__     		=     torch.from_numpy(lowerCamelCase__					).permute(3 ,				2 ,				0 ,				1					)
      elif "embeddings" in key:
        a__     		=     torch.from_numpy(lowerCamelCase__					)
      elif "depthwise_kernel" in key:
        a__     		=     torch.from_numpy(lowerCamelCase__					).permute(2 ,				3 ,				0 ,				1					)
      elif "kernel" in key:
        a__     		=     torch.from_numpy(np.transpose(lowerCamelCase__					)					)
      elif "temperature" in key:
        a__     		=     value
      elif "bn/gamma" or "bn/beta" in key:
        a__     		=     torch.from_numpy(np.transpose(lowerCamelCase__					)					).squeeze()
      else:
        a__     		=     torch.from_numpy(lowerCamelCase__					)
  
      # Replace HF parameters with original TF model parameters
      hf_params[hf_key].copy_(lowerCamelCase__					)
  @torch.no_grad()
  def 				__lowercase				(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[int]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     64
    a__     		=     Tokenizer(lowerCamelCase__					)
    a__     		=     align.Align("efficientnet-b7" ,				"bert-base" ,				6_40 ,				lowerCamelCase__ ,				tok.get_vocab_size()					)
    original_model.compile()
    original_model.load_weights(lowerCamelCase__					)
  
    a__     		=     original_model.trainable_variables
    a__     		=     original_model.non_trainable_variables
    a__     		=     {param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
      a__     		=     param.numpy()
    a__     		=     list(tf_params.keys()					)
  
    # Load HuggingFace model
    a__     		=     get_align_config()
    a__     		=     AlignModel(lowerCamelCase__					).eval()
    a__     		=     hf_model.state_dict()
  
    # Create src-to-dst parameter name mapping dictionary
    print("Converting parameters..."					)
    a__     		=     rename_keys(lowerCamelCase__					)
    replace_params(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)
  
    # Initialize processor
    a__     		=     get_processor()
    a__     		=     processor(
        images=prepare_img() ,				text="A picture of a cat" ,				padding="max_length" ,				max_length=64 ,				return_tensors="pt"					)
  
    # HF model inference
    hf_model.eval()
    with torch.no_grad():
      a__     		=     hf_model(**lowerCamelCase__					)
  
    a__     		=     outputs.image_embeds.detach().numpy()
    a__     		=     outputs.text_embeds.detach().numpy()
  
    # Original model inference
    a__     		=     False
    a__     		=     EfficientNetImageProcessor(
        do_center_crop=lowerCamelCase__ ,				do_rescale=lowerCamelCase__ ,				do_normalize=lowerCamelCase__ ,				include_top=lowerCamelCase__ ,				resample=Image.BILINEAR ,				)
    a__     		=     tf_image_processor(images=prepare_img() ,				return_tensors="tf" ,				data_format="channels_last"					)["pixel_values"]
    a__     		=     tok(tf.constant(["A picture of a cat"]					)					)
  
    a__     		=     original_model.image_encoder(lowerCamelCase__ ,				training=lowerCamelCase__					)
    a__     		=     original_model.text_encoder(lowerCamelCase__ ,				training=lowerCamelCase__					)
  
    a__     		=     tf.nn.la_normalize(lowerCamelCase__ ,				axis=-1					)
    a__     		=     tf.nn.la_normalize(lowerCamelCase__ ,				axis=-1					)
  
    # Check whether original and HF model outputs match  -> np.allclose
    if not np.allclose(lowerCamelCase__ ,				lowerCamelCase__ ,				atol=1E-3					):
      raise ValueError("The predicted image features are not the same."					)
    if not np.allclose(lowerCamelCase__ ,				lowerCamelCase__ ,				atol=1E-3					):
      raise ValueError("The predicted text features are not the same."					)
    print("Model outputs match!"					)
  
    if save_model:
      # Create folder to save model
      if not os.path.isdir(lowerCamelCase__					):
        os.mkdir(lowerCamelCase__					)
      # Save converted model and image processor
      hf_model.save_pretrained(lowerCamelCase__					)
      processor.save_pretrained(lowerCamelCase__					)
  
    if push_to_hub:
      # Push model and image processor to hub
      print("Pushing converted ALIGN to the hub..."					)
      processor.push_to_hub("align-base"					)
      hf_model.push_to_hub("align-base"					)
  
  
  if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE   						=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--checkpoint_path""",
           default="""./weights/model-weights""",
           type=str,
           help="""Path to the pretrained TF ALIGN checkpoint.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""",
           default="""hf_model""",
           type=str,
           help="""Path to the output PyTorch model directory.""",
       )
       parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
       parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
  
       _SCREAMING_SNAKE_CASE   						=    parser.parse_args()
       convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
  
  
style_context: # This model implementation is heavily based on:
  
  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,
      StableDiffusionControlNetInpaintPipeline,
      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 (
      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
      TEXT_TO_IMAGE_IMAGE_PARAMS,
  )
  from ..test_pipelines_common import (
      PipelineKarrasSchedulerTesterMixin,
      PipelineLatentTesterMixin,
      PipelineTesterMixin,
  )
  
  
  enable_full_determinism()
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							_a     ,     _a     ,     _a     ,     unittest.TestCase    ):
      UpperCAmelCase_			:       Dict						 =							StableDiffusionControlNetInpaintPipeline
      UpperCAmelCase_			:       Tuple						 =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase_			:       Optional[Any]						 =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase_			:       Any						 =							frozenset({'control_image'}    )  # skip `image` and `mask` for now, only test for control_image
      UpperCAmelCase_			:       str						 =							TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> Union[str, Any]:
        torch.manual_seed(0   )
        a__     		=     UNetaDConditionModel(
            block_out_channels=(32, 64)      ,      layers_per_block=2      ,      sample_size=32      ,      in_channels=9      ,      out_channels=4      ,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,      cross_attention_dim=32      ,      )
        torch.manual_seed(0   )
        a__     		=     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   )
        a__     		=     DDIMScheduler(
            beta_start=0.00_085      ,      beta_end=0.012      ,      beta_schedule="scaled_linear"      ,      clip_sample=lowerCAmelCase      ,      set_alpha_to_one=lowerCAmelCase      ,      )
        torch.manual_seed(0   )
        a__     		=     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   )
        a__     		=     CLIPTextConfig(
            bos_token_id=0      ,      eos_token_id=2      ,      hidden_size=32      ,      intermediate_size=37      ,      layer_norm_eps=1e-0_5      ,      num_attention_heads=4      ,      num_hidden_layers=5      ,      pad_token_id=1      ,      vocab_size=1000      ,      )
        a__     		=     CLIPTextModel(lowerCAmelCase   )
        a__     		=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )
  
        a__     		=     {
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple      ,      lowerCAmelCase  :   Optional[Any]      ,      lowerCAmelCase  :   Dict=0   )	-> Optional[Any]:
        if str(lowerCAmelCase   ).startswith("mps"   ):
          a__     		=     torch.manual_seed(lowerCAmelCase   )
        else:
          a__     		=     torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
  
        a__     		=     2
        a__     		=     randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,      generator=lowerCAmelCase      ,      device=torch.device(lowerCAmelCase   )      ,      )
        a__     		=     floats_tensor((1, 3, 32, 32)      ,      rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        a__     		=     init_image.cpu().permute(0      ,      2      ,      3      ,      1   )[0]
  
        a__     		=     Image.fromarray(np.uinta(lowerCAmelCase   )   ).convert("RGB"   ).resize((64, 64)   )
        a__     		=     Image.fromarray(np.uinta(init_image + 4   )   ).convert("RGB"   ).resize((64, 64)   )
  
        a__     		=     {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
            "image": image,
            "mask_image": mask_image,
            "control_image": control_image,
        }
  
        return inputs
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> Tuple:
        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 						__lowerCamelCase (					self  :   List[Any]   )	-> Optional[int]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> Optional[int]:
        self._test_inference_batch_single_identical(expected_max_diff=2e-3   )
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							_a    ):
      UpperCAmelCase_			:       Tuple						 =							StableDiffusionControlNetInpaintPipeline
      UpperCAmelCase_			:       List[Any]						 =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase_			:       Dict						 =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase_			:       Tuple						 =							frozenset([]    )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int   )	-> Dict:
        torch.manual_seed(0   )
        a__     		=     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   )
        a__     		=     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   )
        a__     		=     DDIMScheduler(
            beta_start=0.00_085      ,      beta_end=0.012      ,      beta_schedule="scaled_linear"      ,      clip_sample=lowerCAmelCase      ,      set_alpha_to_one=lowerCAmelCase      ,      )
        torch.manual_seed(0   )
        a__     		=     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   )
        a__     		=     CLIPTextConfig(
            bos_token_id=0      ,      eos_token_id=2      ,      hidden_size=32      ,      intermediate_size=37      ,      layer_norm_eps=1e-0_5      ,      num_attention_heads=4      ,      num_hidden_layers=5      ,      pad_token_id=1      ,      vocab_size=1000      ,      )
        a__     		=     CLIPTextModel(lowerCAmelCase   )
        a__     		=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )
  
        a__     		=     {
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							_a     ,     _a     ,     unittest.TestCase    ):
      UpperCAmelCase_			:       Union[str, Any]						 =							StableDiffusionControlNetInpaintPipeline
      UpperCAmelCase_			:       Any						 =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase_			:       Optional[int]						 =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  
  
  
  
      def 						__lowerCamelCase (					self  :   Dict   )	-> str:
        torch.manual_seed(0   )
        a__     		=     UNetaDConditionModel(
            block_out_channels=(32, 64)      ,      layers_per_block=2      ,      sample_size=32      ,      in_channels=9      ,      out_channels=4      ,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,      cross_attention_dim=32      ,      )
        torch.manual_seed(0   )
  
        def init_weights(lowerCAmelCase  :   Optional[int]   ):
          if isinstance(lowerCAmelCase      ,      torch.nn.Convad   ):
            torch.nn.init.normal(m.weight   )
            m.bias.data.fill_(1.0   )
  
        a__     		=     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(lowerCAmelCase   )
  
        torch.manual_seed(0   )
        a__     		=     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(lowerCAmelCase   )
  
        torch.manual_seed(0   )
        a__     		=     DDIMScheduler(
            beta_start=0.00_085      ,      beta_end=0.012      ,      beta_schedule="scaled_linear"      ,      clip_sample=lowerCAmelCase      ,      set_alpha_to_one=lowerCAmelCase      ,      )
        torch.manual_seed(0   )
        a__     		=     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   )
        a__     		=     CLIPTextConfig(
            bos_token_id=0      ,      eos_token_id=2      ,      hidden_size=32      ,      intermediate_size=37      ,      layer_norm_eps=1e-0_5      ,      num_attention_heads=4      ,      num_hidden_layers=5      ,      pad_token_id=1      ,      vocab_size=1000      ,      )
        a__     		=     CLIPTextModel(lowerCAmelCase   )
        a__     		=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )
  
        a__     		=     MultiControlNetModel([controlneta, controlneta]   )
  
        a__     		=     {
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[int]      ,      lowerCAmelCase  :   Dict      ,      lowerCAmelCase  :   Any=0   )	-> List[str]:
        if str(lowerCAmelCase   ).startswith("mps"   ):
          a__     		=     torch.manual_seed(lowerCAmelCase   )
        else:
          a__     		=     torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
  
        a__     		=     2
  
        a__     		=     [
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,      generator=lowerCAmelCase      ,      device=torch.device(lowerCAmelCase   )      ,      ),
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,      generator=lowerCAmelCase      ,      device=torch.device(lowerCAmelCase   )      ,      ),
        ]
        a__     		=     floats_tensor((1, 3, 32, 32)      ,      rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
        a__     		=     init_image.cpu().permute(0      ,      2      ,      3      ,      1   )[0]
  
        a__     		=     Image.fromarray(np.uinta(lowerCAmelCase   )   ).convert("RGB"   ).resize((64, 64)   )
        a__     		=     Image.fromarray(np.uinta(init_image + 4   )   ).convert("RGB"   ).resize((64, 64)   )
  
        a__     		=     {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
            "image": image,
            "mask_image": mask_image,
            "control_image": control_image,
        }
  
        return inputs
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> List[Any]:
        a__     		=     self.get_dummy_components()
        a__     		=     self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
  
        a__     		=     10.0
        a__     		=     4
  
        a__     		=     self.get_dummy_inputs(lowerCAmelCase   )
        a__     		=     steps
        a__     		=     scale
        a__     		=     pipe(**lowerCAmelCase   )[0]
  
        a__     		=     self.get_dummy_inputs(lowerCAmelCase   )
        a__     		=     steps
        a__     		=     scale
        a__     		=     pipe(**lowerCAmelCase      ,      control_guidance_start=0.1      ,      control_guidance_end=0.2   )[0]
  
        a__     		=     self.get_dummy_inputs(lowerCAmelCase   )
        a__     		=     steps
        a__     		=     scale
        a__     		=     pipe(**lowerCAmelCase      ,      control_guidance_start=[0.1, 0.3]      ,      control_guidance_end=[0.2, 0.7]   )[0]
  
        a__     		=     self.get_dummy_inputs(lowerCAmelCase   )
        a__     		=     steps
        a__     		=     scale
        a__     		=     pipe(**lowerCAmelCase      ,      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 						__lowerCamelCase (					self  :   Tuple   )	-> Any:
        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 						__lowerCamelCase (					self  :   Any   )	-> Dict:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> List[str]:
        self._test_inference_batch_single_identical(expected_max_diff=2e-3   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]   )	-> Any:
        a__     		=     self.get_dummy_components()
        a__     		=     self.pipeline_class(**lowerCAmelCase   )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )
        with tempfile.TemporaryDirectory() as tmpdir:
          try:
            # save_pretrained is not implemented for Multi-ControlNet
            pipe.save_pretrained(lowerCAmelCase   )
          except NotImplementedError:
            pass
  
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 					UpperCAmelCase_				(							unittest.TestCase    ):
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]   )	-> Tuple:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
  
  
  
  
      def 						__lowerCamelCase (					self  :   Union[str, Any]   )	-> List[Any]:
        a__     		=     ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"   )
  
        a__     		=     StableDiffusionControlNetInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting"      ,      safety_checker=lowerCAmelCase      ,      controlnet=lowerCAmelCase   )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )
  
        a__     		=     torch.Generator(device="cpu"   ).manual_seed(0   )
        a__     		=     load_image(
            "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"   ).resize((512, 512)   )
  
        a__     		=     load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/input_bench_mask.png"   ).resize((512, 512)   )
  
        a__     		=     "pitch black hole"
  
        a__     		=     load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"   ).resize((512, 512)   )
  
        a__     		=     pipe(
            lowerCAmelCase      ,      image=lowerCAmelCase      ,      mask_image=lowerCAmelCase      ,      control_image=lowerCAmelCase      ,      generator=lowerCAmelCase      ,      output_type="np"      ,      num_inference_steps=3      ,      )
  
        a__     		=     output.images[0]
  
        assert image.shape == (512, 512, 3)
  
        a__     		=     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"   )
  
        assert np.abs(expected_image - image   ).max() < 9e-2
  
  
  
  
      def 						__lowerCamelCase (					self  :   Dict   )	-> List[str]:
        a__     		=     ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"   )
  
        a__     		=     StableDiffusionControlNetInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5"      ,      safety_checker=lowerCAmelCase      ,      controlnet=lowerCAmelCase   )
        a__     		=     DDIMScheduler.from_config(pipe.scheduler.config   )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )
  
        a__     		=     torch.Generator(device="cpu"   ).manual_seed(33   )
  
        a__     		=     load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"   )
        a__     		=     init_image.resize((512, 512)   )
  
        a__     		=     load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"   )
        a__     		=     mask_image.resize((512, 512)   )
  
        a__     		=     "a handsome man with ray-ban sunglasses"
  
        def make_inpaint_condition(lowerCAmelCase  :   Any      ,      lowerCAmelCase  :   Optional[int]   ):
          a__     		=     np.array(image.convert("RGB"   )   ).astype(np.floataa   ) / 255.0
          a__     		=     np.array(image_mask.convert("L"   )   ).astype(np.floataa   ) / 255.0
  
          assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
          a__     		=     -1.0  # set as masked pixel
          a__     		=     np.expand_dims(lowerCAmelCase      ,      0   ).transpose(0      ,      3      ,      1      ,      2   )
          a__     		=     torch.from_numpy(lowerCAmelCase   )
          return image
  
        a__     		=     make_inpaint_condition(lowerCAmelCase      ,      lowerCAmelCase   )
  
        a__     		=     pipe(
            lowerCAmelCase      ,      image=lowerCAmelCase      ,      mask_image=lowerCAmelCase      ,      control_image=lowerCAmelCase      ,      guidance_scale=9.0      ,      eta=1.0      ,      generator=lowerCAmelCase      ,      num_inference_steps=20      ,      output_type="np"      ,      )
        a__     		=     output.images[0]
  
        assert image.shape == (512, 512, 3)
  
        a__     		=     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"   )
  
        assert np.abs(expected_image - image   ).max() < 9e-2
  
  