




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase__         =      logging.get_logger("""transformers.models.speecht5""")







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    Tuple				,				SCREAMING_SNAKE_CASE_							:    str					):
					hf_model.apply_weight_norm()

					__lowerCAmelCase           =	checkpoint["input_conv.weight_g"]
					__lowerCAmelCase           =	checkpoint["input_conv.weight_v"]
					__lowerCAmelCase           =	checkpoint["input_conv.bias"]

					for i in range(len(config.upsample_rates					)					):
										__lowerCAmelCase           =	checkpoint[F"""upsamples.{i}.1.weight_g"""]
										__lowerCAmelCase           =	checkpoint[F"""upsamples.{i}.1.weight_v"""]
										__lowerCAmelCase           =	checkpoint[F"""upsamples.{i}.1.bias"""]

					for i in range(len(config.upsample_rates					) * len(config.resblock_kernel_sizes					)					):
										for j in range(len(config.resblock_dilation_sizes					)					):
															__lowerCAmelCase           =	checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
															__lowerCAmelCase           =	checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
															__lowerCAmelCase           =	checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

															__lowerCAmelCase           =	checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
															__lowerCAmelCase           =	checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
															__lowerCAmelCase           =	checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

					__lowerCAmelCase           =	checkpoint["output_conv.1.weight_g"]
					__lowerCAmelCase           =	checkpoint["output_conv.1.weight_v"]
					__lowerCAmelCase           =	checkpoint["output_conv.1.bias"]

					hf_model.remove_weight_norm()







@torch.no_grad()
def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]=None				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]=None				,				):
					if config_path is not None:
										__lowerCAmelCase           =	SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	SpeechTaHifiGanConfig()

					__lowerCAmelCase           =	SpeechTaHifiGan(SCREAMING_SNAKE_CASE_					)

					__lowerCAmelCase           =	torch.load(SCREAMING_SNAKE_CASE_					)
					load_weights(orig_checkpoint["model"]["generator"]				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

					__lowerCAmelCase           =	np.load(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	stats[0].reshape(-1					)
					__lowerCAmelCase           =	stats[1].reshape(-1					)
					__lowerCAmelCase           =	torch.from_numpy(SCREAMING_SNAKE_CASE_					).float()
					__lowerCAmelCase           =	torch.from_numpy(SCREAMING_SNAKE_CASE_					).float()

					model.save_pretrained(SCREAMING_SNAKE_CASE_					)

					if repo_id:
										print("Pushing to the hub..."					)
										model.push_to_hub(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					UpperCamelCase__         =      argparse.ArgumentParser()
					parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
					parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
					)

					UpperCamelCase__         =      parser.parse_args()
					convert_hifigan_checkpoint(
					    args.checkpoint_path,
					    args.stats_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)










import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class       a__   ( unittest.TestCase				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											torch.manual_seed(0      )
											__lowerCAmelCase           =	UNetaDModel(
											    block_out_channels=(3_2, 6_4)		,      layers_per_block=2		,      sample_size=3_2		,      in_channels=3		,      out_channels=3		,      down_block_types=("DownBlock2D", "AttnDownBlock2D")		,      up_block_types=("AttnUpBlock2D", "UpBlock2D")		,      )
											return model






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.dummy_uncond_unet
											__lowerCAmelCase           =	ScoreSdeVeScheduler()

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A		,      return_dict=_A      )[
											    0
											]

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]
											__lowerCAmelCase           =	image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase           =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2






@slow
@require_torch
class       a__   ( unittest.TestCase				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"google/ncsnpp-church-256"
											__lowerCAmelCase           =	UNetaDModel.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVeScheduler.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=1_0		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]

											assert image.shape == (1, 2_5_6, 2_5_6, 3)

											__lowerCAmelCase           =	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




