





'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 				__UpperCAmelCase	(							A__		,       unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     RePaintPipeline
		__lowerCAmelCase								=     IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
		__lowerCAmelCase								=     PipelineTesterMixin.required_optional_params - {
		    '''latents''',
		    '''num_images_per_prompt''',
		    '''callback''',
		    '''callback_steps''',
		}
		__lowerCAmelCase								=     IMAGE_INPAINTING_BATCH_PARAMS
		__lowerCAmelCase								=     False







		def 							A  (self    :   Tuple   ):
			torch.manual_seed(0   )
			torch.manual_seed(0   )
			A					=   UNetaDModel(
			    block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,   up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,   )
			A					=   RePaintScheduler()
			A					=   {"""unet""": unet, """scheduler""": scheduler}
			return components







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Tuple=0   ):
			if str(_lowerCAmelCase   ).startswith("""mps"""   ):
				A					=   torch.manual_seed(_lowerCAmelCase   )
			else:
				A					=   torch.Generator(device=_lowerCAmelCase   ).manual_seed(_lowerCAmelCase   )
			A					=   np.random.RandomState(_lowerCAmelCase   ).standard_normal((1, 3, 32, 32)   )
			A					=   torch.from_numpy(_lowerCAmelCase   ).to(device=_lowerCAmelCase   ,   dtype=torch.floataa   )
			A					=   (image > 0).to(device=_lowerCAmelCase   ,   dtype=torch.floataa   )
			A					=   {
			    """image""": image,
			    """mask_image""": mask,
			    """generator""": generator,
			    """num_inference_steps""": 5,
			    """eta""": 0.0,
			    """jump_length""": 2,
			    """jump_n_sample""": 2,
			    """output_type""": """numpy""",
			}
			return inputs







		def 							A  (self    :   List[Any]   ):
			A					=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
			A					=   self.get_dummy_components()
			A					=   RePaintPipeline(**_lowerCAmelCase   )
			A					=   sd_pipe.to(_lowerCAmelCase   )
			sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase   )

			A					=   self.get_dummy_inputs(_lowerCAmelCase   )
			A					=   sd_pipe(**_lowerCAmelCase   ).images
			A					=   image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			A					=   np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]   )

			assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







		@skip_mps
		def 							A  (self    :   str   ):
			return super().test_save_load_local()







		@unittest.skip("""non-deterministic pipeline"""   )
		def 							A  (self    :   List[Any]   ):
			return super().test_inference_batch_single_identical()







		@skip_mps
		def 							A  (self    :   str   ):
			return super().test_dict_tuple_outputs_equivalent()







		@skip_mps
		def 							A  (self    :   List[Any]   ):
			return super().test_save_load_optional_components()







		@skip_mps
		def 							A  (self    :   Dict   ):
			return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class 				__UpperCAmelCase	(							unittest.TestCase    ):







		'''simple docstring'''







		def 							A  (self    :   List[Any]   ):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def 							A  (self    :   Optional[Any]   ):
			A					=   load_image(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
			    """repaint/celeba_hq_256.png"""   )
			A					=   load_image(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""   )
			A					=   load_numpy(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
			    """repaint/celeba_hq_256_result.npy"""   )

			A					=   """google/ddpm-ema-celebahq-256"""
			A					=   UNetaDModel.from_pretrained(_lowerCAmelCase   )
			A					=   RePaintScheduler.from_pretrained(_lowerCAmelCase   )

			A					=   RePaintPipeline(unet=_lowerCAmelCase   ,   scheduler=_lowerCAmelCase   ).to(_lowerCAmelCase   )
			repaint.set_progress_bar_config(disable=_lowerCAmelCase   )
			repaint.enable_attention_slicing()

			A					=   torch.manual_seed(0   )
			A					=   repaint(
			    _lowerCAmelCase   ,   _lowerCAmelCase   ,   num_inference_steps=250   ,   eta=0.0   ,   jump_length=10   ,   jump_n_sample=10   ,   generator=_lowerCAmelCase   ,   output_type="""np"""   ,   )
			A					=   output.images[0]

			assert image.shape == (256, 256, 3)
			assert np.abs(expected_image - image   ).mean() < 1e-2








'''simple docstring'''


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)

_lowerCamelCase					:						List[Any]					     =							['UtteranceLevel']




@torch.no_grad()
def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   torch.load(UpperCAmelCase							,      map_location="""cpu"""     )
	if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
		raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""     )

	A					=   checkpoint["""Downstream"""]

	A					=   HubertConfig.from_pretrained(UpperCAmelCase     )
	A					=   HubertForSequenceClassification.from_pretrained(UpperCAmelCase							,      config=UpperCAmelCase     )
	A					=   WavaVecaFeatureExtractor.from_pretrained(
	    UpperCAmelCase							,      return_attention_mask=UpperCAmelCase							,      do_normalize=UpperCAmelCase     )

	if hf_congfig.use_weighted_layer_sum:
		A					=   checkpoint["""Featurizer"""]["""weights"""]

	A					=   downstream_dict["""projector.weight"""]
	A					=   downstream_dict["""projector.bias"""]
	A					=   downstream_dict["""model.post_net.linear.weight"""]
	A					=   downstream_dict["""model.post_net.linear.bias"""]

	hf_feature_extractor.save_pretrained(UpperCAmelCase     )
	hf_model.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
				_lowerCamelCase					:						List[str]					     =							argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				_lowerCamelCase					:						List[Any]					     =							parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

