

# 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 				A_					(						snake_case__				,      snake_case__				,      snake_case__				,      unittest.TestCase   ):
     _lowercase     :	Any        =      StableDiffusionControlNetInpaintPipeline
     _lowercase     :	Any        =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     _lowercase     :	Optional[Any]        =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     _lowercase     :	Any        =      frozenset({'control_image'}   )  # skip `image` and `mask` for now, only test for control_image
     _lowercase     :	Any        =      TEXT_TO_IMAGE_IMAGE_PARAMS


     def     UpperCAmelCase    (				self     :						int   )				->      Dict:
      torch.manual_seed(0   )
      __lowerCAmelCase:  Any        = UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)     ,    layers_per_block=2     ,    sample_size=3_2     ,    in_channels=9     ,    out_channels=4     ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,    cross_attention_dim=3_2     ,    )
      torch.manual_seed(0   )
      __lowerCAmelCase:  Union[str, Any]        = ControlNetModel(
          block_out_channels=(3_2, 6_4)     ,    layers_per_block=2     ,    in_channels=4     ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,    cross_attention_dim=3_2     ,    conditioning_embedding_out_channels=(1_6, 3_2)     ,    )
      torch.manual_seed(0   )
      __lowerCAmelCase:  Union[str, Any]        = DDIMScheduler(
          beta_start=0.00085     ,    beta_end=0.012     ,    beta_schedule='scaled_linear'     ,    clip_sample=UpperCAmelCase     ,    set_alpha_to_one=UpperCAmelCase     ,    )
      torch.manual_seed(0   )
      __lowerCAmelCase:  Union[str, Any]        = AutoencoderKL(
          block_out_channels=[3_2, 6_4]     ,    in_channels=3     ,    out_channels=3     ,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,    latent_channels=4     ,    )
      torch.manual_seed(0   )
      __lowerCAmelCase:  Union[str, Any]        = CLIPTextConfig(
          bos_token_id=0     ,    eos_token_id=2     ,    hidden_size=3_2     ,    intermediate_size=3_7     ,    layer_norm_eps=1E-05     ,    num_attention_heads=4     ,    num_hidden_layers=5     ,    pad_token_id=1     ,    vocab_size=1_0_0_0     ,    )
      __lowerCAmelCase:  str        = CLIPTextModel(UpperCAmelCase   )
      __lowerCAmelCase:  Optional[Any]        = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

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


     def     UpperCAmelCase    (				self     :						List[str]     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						List[str]=0   )				->      List[str]:
      if str(UpperCAmelCase   ).startswith('mps'   ):
       __lowerCAmelCase:  Dict        = torch.manual_seed(UpperCAmelCase   )
      else:
       __lowerCAmelCase:  Any        = torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )

      __lowerCAmelCase:  Any        = 2
      __lowerCAmelCase:  Any        = randn_tensor(
          (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)     ,    generator=UpperCAmelCase     ,    device=torch.device(UpperCAmelCase   )     ,    )
      __lowerCAmelCase:  List[Any]        = floats_tensor((1, 3, 3_2, 3_2)     ,    rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
      __lowerCAmelCase:  Union[str, Any]        = init_image.cpu().permute(0     ,    2     ,    3     ,    1   )[0]

      __lowerCAmelCase:  Union[str, Any]        = Image.fromarray(np.uinta(UpperCAmelCase   )   ).convert('RGB'   ).resize((6_4, 6_4)   )
      __lowerCAmelCase:  str        = Image.fromarray(np.uinta(init_image + 4   )   ).convert('RGB'   ).resize((6_4, 6_4)   )

      __lowerCAmelCase:  Union[str, Any]        = {
          '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     UpperCAmelCase    (				self     :						Union[str, Any]   )				->      Union[str, 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     UpperCAmelCase    (				self     :						Tuple   )				->      int:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3   )


     def     UpperCAmelCase    (				self     :						Optional[Any]   )				->      Tuple:
      self._test_inference_batch_single_identical(expected_max_diff=2E-3   )







class 				A_					(						snake_case__   ):
     _lowercase     :	Optional[int]        =      StableDiffusionControlNetInpaintPipeline
     _lowercase     :	List[str]        =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     _lowercase     :	Dict        =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     _lowercase     :	List[Any]        =      frozenset([]   )


     def     UpperCAmelCase    (				self     :						int   )				->      Tuple:
      torch.manual_seed(0   )
      __lowerCAmelCase:  List[str]        = UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)     ,    layers_per_block=2     ,    sample_size=3_2     ,    in_channels=4     ,    out_channels=4     ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,    cross_attention_dim=3_2     ,    )
      torch.manual_seed(0   )
      __lowerCAmelCase:  Optional[Any]        = ControlNetModel(
          block_out_channels=(3_2, 6_4)     ,    layers_per_block=2     ,    in_channels=4     ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,    cross_attention_dim=3_2     ,    conditioning_embedding_out_channels=(1_6, 3_2)     ,    )
      torch.manual_seed(0   )
      __lowerCAmelCase:  Optional[Any]        = DDIMScheduler(
          beta_start=0.00085     ,    beta_end=0.012     ,    beta_schedule='scaled_linear'     ,    clip_sample=UpperCAmelCase     ,    set_alpha_to_one=UpperCAmelCase     ,    )
      torch.manual_seed(0   )
      __lowerCAmelCase:  int        = AutoencoderKL(
          block_out_channels=[3_2, 6_4]     ,    in_channels=3     ,    out_channels=3     ,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,    latent_channels=4     ,    )
      torch.manual_seed(0   )
      __lowerCAmelCase:  Dict        = CLIPTextConfig(
          bos_token_id=0     ,    eos_token_id=2     ,    hidden_size=3_2     ,    intermediate_size=3_7     ,    layer_norm_eps=1E-05     ,    num_attention_heads=4     ,    num_hidden_layers=5     ,    pad_token_id=1     ,    vocab_size=1_0_0_0     ,    )
      __lowerCAmelCase:  int        = CLIPTextModel(UpperCAmelCase   )
      __lowerCAmelCase:  List[Any]        = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

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







class 				A_					(						snake_case__				,      snake_case__				,      unittest.TestCase   ):
     _lowercase     :	List[Any]        =      StableDiffusionControlNetInpaintPipeline
     _lowercase     :	Union[str, Any]        =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     _lowercase     :	str        =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS


     def     UpperCAmelCase    (				self     :						Any   )				->      List[Any]:
      torch.manual_seed(0   )
      __lowerCAmelCase:  str        = UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)     ,    layers_per_block=2     ,    sample_size=3_2     ,    in_channels=9     ,    out_channels=4     ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,    cross_attention_dim=3_2     ,    )
      torch.manual_seed(0   )

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

      __lowerCAmelCase:  Union[str, Any]        = ControlNetModel(
          block_out_channels=(3_2, 6_4)     ,    layers_per_block=2     ,    in_channels=4     ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,    cross_attention_dim=3_2     ,    conditioning_embedding_out_channels=(1_6, 3_2)     ,    )
      controlneta.controlnet_down_blocks.apply(UpperCAmelCase   )

      torch.manual_seed(0   )
      __lowerCAmelCase:  int        = ControlNetModel(
          block_out_channels=(3_2, 6_4)     ,    layers_per_block=2     ,    in_channels=4     ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,    cross_attention_dim=3_2     ,    conditioning_embedding_out_channels=(1_6, 3_2)     ,    )
      controlneta.controlnet_down_blocks.apply(UpperCAmelCase   )

      torch.manual_seed(0   )
      __lowerCAmelCase:  int        = DDIMScheduler(
          beta_start=0.00085     ,    beta_end=0.012     ,    beta_schedule='scaled_linear'     ,    clip_sample=UpperCAmelCase     ,    set_alpha_to_one=UpperCAmelCase     ,    )
      torch.manual_seed(0   )
      __lowerCAmelCase:  int        = AutoencoderKL(
          block_out_channels=[3_2, 6_4]     ,    in_channels=3     ,    out_channels=3     ,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,    latent_channels=4     ,    )
      torch.manual_seed(0   )
      __lowerCAmelCase:  str        = CLIPTextConfig(
          bos_token_id=0     ,    eos_token_id=2     ,    hidden_size=3_2     ,    intermediate_size=3_7     ,    layer_norm_eps=1E-05     ,    num_attention_heads=4     ,    num_hidden_layers=5     ,    pad_token_id=1     ,    vocab_size=1_0_0_0     ,    )
      __lowerCAmelCase:  Union[str, Any]        = CLIPTextModel(UpperCAmelCase   )
      __lowerCAmelCase:  int        = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

      __lowerCAmelCase:  int        = MultiControlNetModel([controlneta, controlneta]   )

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


     def     UpperCAmelCase    (				self     :						Tuple     ,    UpperCAmelCase     :						Dict     ,    UpperCAmelCase     :						List[Any]=0   )				->      Optional[Any]:
      if str(UpperCAmelCase   ).startswith('mps'   ):
       __lowerCAmelCase:  Optional[int]        = torch.manual_seed(UpperCAmelCase   )
      else:
       __lowerCAmelCase:  Optional[int]        = torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )

      __lowerCAmelCase:  Optional[Any]        = 2

      __lowerCAmelCase:  Optional[Any]        = [
          randn_tensor(
              (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)     ,    generator=UpperCAmelCase     ,    device=torch.device(UpperCAmelCase   )     ,    ),
          randn_tensor(
              (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)     ,    generator=UpperCAmelCase     ,    device=torch.device(UpperCAmelCase   )     ,    ),
      ]
      __lowerCAmelCase:  Optional[int]        = floats_tensor((1, 3, 3_2, 3_2)     ,    rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
      __lowerCAmelCase:  Optional[Any]        = init_image.cpu().permute(0     ,    2     ,    3     ,    1   )[0]

      __lowerCAmelCase:  int        = Image.fromarray(np.uinta(UpperCAmelCase   )   ).convert('RGB'   ).resize((6_4, 6_4)   )
      __lowerCAmelCase:  int        = Image.fromarray(np.uinta(init_image + 4   )   ).convert('RGB'   ).resize((6_4, 6_4)   )

      __lowerCAmelCase:  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,
          'mask_image': mask_image,
          'control_image': control_image,
      }

      return inputs


     def     UpperCAmelCase    (				self     :						List[Any]   )				->      str:
      __lowerCAmelCase:  List[str]        = self.get_dummy_components()
      __lowerCAmelCase:  str        = self.pipeline_class(**UpperCAmelCase   )
      pipe.to(UpperCAmelCase   )

      __lowerCAmelCase:  Any        = 10.0
      __lowerCAmelCase:  Union[str, Any]        = 4

      __lowerCAmelCase:  Optional[int]        = self.get_dummy_inputs(UpperCAmelCase   )
      __lowerCAmelCase:  Optional[Any]        = steps
      __lowerCAmelCase:  int        = scale
      __lowerCAmelCase:  List[str]        = pipe(**UpperCAmelCase   )[0]

      __lowerCAmelCase:  Optional[int]        = self.get_dummy_inputs(UpperCAmelCase   )
      __lowerCAmelCase:  str        = steps
      __lowerCAmelCase:  Any        = scale
      __lowerCAmelCase:  Any        = pipe(**UpperCAmelCase     ,    control_guidance_start=0.1     ,    control_guidance_end=0.2   )[0]

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

      __lowerCAmelCase:  List[Any]        = self.get_dummy_inputs(UpperCAmelCase   )
      __lowerCAmelCase:  List[Any]        = steps
      __lowerCAmelCase:  Optional[int]        = scale
      __lowerCAmelCase:  List[Any]        = pipe(**UpperCAmelCase     ,    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     UpperCAmelCase    (				self     :						Optional[int]   )				->      List[str]:
      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     UpperCAmelCase    (				self     :						str   )				->      int:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3   )


     def     UpperCAmelCase    (				self     :						str   )				->      Any:
      self._test_inference_batch_single_identical(expected_max_diff=2E-3   )


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Dict:
      __lowerCAmelCase:  Optional[Any]        = self.get_dummy_components()
      __lowerCAmelCase:  str        = self.pipeline_class(**UpperCAmelCase   )
      pipe.to(UpperCAmelCase   )
      pipe.set_progress_bar_config(disable=UpperCAmelCase   )
      with tempfile.TemporaryDirectory() as tmpdir:
       try:
        # save_pretrained is not implemented for Multi-ControlNet
        pipe.save_pretrained(UpperCAmelCase   )
       except NotImplementedError:
        pass







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


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Union[str, Any]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


     def     UpperCAmelCase    (				self     :						List[str]   )				->      Dict:
      __lowerCAmelCase:  Dict        = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'   )

      __lowerCAmelCase:  Dict        = StableDiffusionControlNetInpaintPipeline.from_pretrained(
          'runwayml/stable-diffusion-inpainting'     ,    safety_checker=UpperCAmelCase     ,    controlnet=UpperCAmelCase   )
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=UpperCAmelCase   )

      __lowerCAmelCase:  List[Any]        = torch.Generator(device='cpu'   ).manual_seed(0   )
      __lowerCAmelCase:  Tuple        = load_image(
          'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'   ).resize((5_1_2, 5_1_2)   )

      __lowerCAmelCase:  Union[str, Any]        = load_image(
          'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
          '/stable_diffusion_inpaint/input_bench_mask.png'   ).resize((5_1_2, 5_1_2)   )

      __lowerCAmelCase:  Tuple        = 'pitch black hole'

      __lowerCAmelCase:  Optional[Any]        = load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'   ).resize((5_1_2, 5_1_2)   )

      __lowerCAmelCase:  Any        = pipe(
          UpperCAmelCase     ,    image=UpperCAmelCase     ,    mask_image=UpperCAmelCase     ,    control_image=UpperCAmelCase     ,    generator=UpperCAmelCase     ,    output_type='np'     ,    num_inference_steps=3     ,    )

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

      assert image.shape == (5_1_2, 5_1_2, 3)

      __lowerCAmelCase:  int        = 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     UpperCAmelCase    (				self     :						Optional[Any]   )				->      int:
      __lowerCAmelCase:  str        = ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'   )

      __lowerCAmelCase:  List[Any]        = StableDiffusionControlNetInpaintPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'     ,    safety_checker=UpperCAmelCase     ,    controlnet=UpperCAmelCase   )
      __lowerCAmelCase:  List[str]        = DDIMScheduler.from_config(pipe.scheduler.config   )
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=UpperCAmelCase   )

      __lowerCAmelCase:  Dict        = torch.Generator(device='cpu'   ).manual_seed(3_3   )

      __lowerCAmelCase:  Union[str, Any]        = load_image(
          'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'   )
      __lowerCAmelCase:  Union[str, Any]        = init_image.resize((5_1_2, 5_1_2)   )

      __lowerCAmelCase:  List[Any]        = load_image(
          'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'   )
      __lowerCAmelCase:  List[str]        = mask_image.resize((5_1_2, 5_1_2)   )

      __lowerCAmelCase:  List[Any]        = 'a handsome man with ray-ban sunglasses'

      def make_inpaint_condition(UpperCAmelCase     :						Dict     ,    UpperCAmelCase     :						Any   ):
       __lowerCAmelCase:  Dict        = np.array(image.convert('RGB'   )   ).astype(np.floataa   ) / 255.0
       __lowerCAmelCase:  Any        = 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"
       __lowerCAmelCase:  List[Any]        = -1.0  # set as masked pixel
       __lowerCAmelCase:  List[Any]        = np.expand_dims(UpperCAmelCase     ,    0   ).transpose(0     ,    3     ,    1     ,    2   )
       __lowerCAmelCase:  Union[str, Any]        = torch.from_numpy(UpperCAmelCase   )
       return image

      __lowerCAmelCase:  List[str]        = make_inpaint_condition(UpperCAmelCase     ,    UpperCAmelCase   )

      __lowerCAmelCase:  List[str]        = pipe(
          UpperCAmelCase     ,    image=UpperCAmelCase     ,    mask_image=UpperCAmelCase     ,    control_image=UpperCAmelCase     ,    guidance_scale=9.0     ,    eta=1.0     ,    generator=UpperCAmelCase     ,    num_inference_steps=2_0     ,    output_type='np'     ,    )
      __lowerCAmelCase:  Tuple        = output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)

      __lowerCAmelCase:  str        = 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








import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_a									=  logging.get_logger(__name__)

_a									=  {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}







def _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       str:

 """simple docstring"""






 for attribute in key.split('.'						):
  __lowerCAmelCase:  Optional[int]        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 if weight_type is not None:
  __lowerCAmelCase:  Dict        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						).shape
 else:
  __lowerCAmelCase:  str        = hf_pointer.shape

 assert hf_shape == value.shape, (
     f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
     f''' {value.shape} for {full_name}'''
 )

 if weight_type == "weight":
  __lowerCAmelCase:  str        = value
 elif weight_type == "weight_g":
  __lowerCAmelCase:  List[str]        = value
 elif weight_type == "weight_v":
  __lowerCAmelCase:  List[Any]        = value
 elif weight_type == "bias":
  __lowerCAmelCase:  Optional[int]        = value
 else:
  __lowerCAmelCase:  int        = value

 logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''						)







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Any						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = []
 __lowerCAmelCase:  Optional[int]        = fairseq_model.state_dict()

 __lowerCAmelCase:  int        = hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __lowerCAmelCase:  List[str]        = False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   hf_model.config.feat_extract_norm == 'group'       ,   )
   __lowerCAmelCase:  List[str]        = True
  else:
   for key, mapped_key in MAPPING.items():
    __lowerCAmelCase:  List[Any]        = mapped_key

    if key in name:
     __lowerCAmelCase:  List[Any]        = True
     if "*" in mapped_key:
      __lowerCAmelCase:  Any        = name.split(SCREAMING_SNAKE_CASE						)[0].split('.'						)[-2]
      __lowerCAmelCase:  Dict        = mapped_key.replace('*'       ,   SCREAMING_SNAKE_CASE						)
     if "weight_g" in name:
      __lowerCAmelCase:  str        = 'weight_g'
     elif "weight_v" in name:
      __lowerCAmelCase:  Tuple        = 'weight_v'
     elif "weight" in name:
      __lowerCAmelCase:  Tuple        = 'weight'
     elif "bias" in name:
      __lowerCAmelCase:  Optional[Any]        = 'bias'
     else:
      __lowerCAmelCase:  Optional[Any]        = None
     set_recursively(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
    continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE						)

 logger.warning(f'''Unused weights: {unused_weights}'''						)







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     List[str]						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  Dict        = full_name.split('conv_layers.'						)[-1]
 __lowerCAmelCase:  Optional[Any]        = name.split('.'						)
 __lowerCAmelCase:  List[str]        = int(items[0]						)
 __lowerCAmelCase:  str        = int(items[1]						)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       f'''{full_name} has size {value.shape}, but'''
       f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
   )
   __lowerCAmelCase:  Optional[int]        = value
   logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       f'''{full_name} has size {value.shape}, but'''
       f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
   )
   __lowerCAmelCase:  Tuple        = value
   logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
       " found."
   )
   __lowerCAmelCase:  List[Any]        = value
   logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       f'''{full_name} has size {value.shape}, but'''
       f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
   )
   __lowerCAmelCase:  Any        = value
   logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
 else:
  unused_weights.append(SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  str        = HubertConfig()
 __lowerCAmelCase:  Any        = model.config

 __lowerCAmelCase:  str        = fs_config.activation_dropout
 __lowerCAmelCase:  Optional[Any]        = False
 __lowerCAmelCase:  Tuple        = fs_config.attention_dropout
 __lowerCAmelCase:  Tuple        = False
 __lowerCAmelCase:  Tuple        = eval(fs_config.extractor_conv_feature_layers						)
 __lowerCAmelCase:  Optional[int]        = [x[0] for x in conv_layers]
 __lowerCAmelCase:  Dict        = [x[1] for x in conv_layers]
 __lowerCAmelCase:  List[str]        = [x[2] for x in conv_layers]
 __lowerCAmelCase:  Dict        = 'gelu'
 __lowerCAmelCase:  List[str]        = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
 __lowerCAmelCase:  int        = False
 __lowerCAmelCase:  Dict        = 0.0
 __lowerCAmelCase:  str        = 0.0
 __lowerCAmelCase:  List[Any]        = fs_config.activation_fn
 __lowerCAmelCase:  str        = fs_config.dropout
 __lowerCAmelCase:  List[str]        = fs_config.encoder_embed_dim
 __lowerCAmelCase:  str        = 0.0_2
 __lowerCAmelCase:  List[Any]        = fs_config.encoder_ffn_embed_dim
 __lowerCAmelCase:  List[str]        = 1E-5
 __lowerCAmelCase:  Optional[Any]        = 0.0
 __lowerCAmelCase:  int        = fs_config.encoder_attention_heads
 __lowerCAmelCase:  Any        = fs_config.conv_pos_groups
 __lowerCAmelCase:  str        = fs_config.conv_pos
 __lowerCAmelCase:  Optional[int]        = len(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Dict        = fs_config.encoder_layers

 return config







@torch.no_grad()
def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]=None						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = distilhubert().model.model

 if config_path is not None:
  __lowerCAmelCase:  Dict        = HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
 else:
  __lowerCAmelCase:  Union[str, Any]        = convert_config(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Any        = model.eval()

 __lowerCAmelCase:  Tuple        = WavaVecaFeatureExtractor(
     feature_size=1       ,   sampling_rate=1_60_00       ,   padding_value=0       ,   do_normalize=SCREAMING_SNAKE_CASE       ,   return_attention_mask=SCREAMING_SNAKE_CASE       ,   )
 __lowerCAmelCase:  Tuple        = HubertModel(SCREAMING_SNAKE_CASE						)

 recursively_load_weights(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE						)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
  _a									=  argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  _a									=  parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





