






'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase     :   str	      =		logging.get_logger('transformers.models.speecht5')
def     A__      (					__lowerCAmelCase		:      int ,     __lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      Any    ):
						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__      (					__lowerCAmelCase		:      Any ,     __lowerCAmelCase		:      List[Any] ,     __lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      List[Any]=None ,     __lowerCAmelCase		:      Dict=None ,     ):
						if config_path is not None:
												lowerCamelCase__								=	SpeechTaHifiGanConfig.from_pretrained(__lowerCAmelCase    )
						else:
												lowerCamelCase__								=	SpeechTaHifiGanConfig()

						lowerCamelCase__								=	SpeechTaHifiGan(__lowerCAmelCase    )

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

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

						model.save_pretrained(__lowerCAmelCase    )

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


if __name__ == "__main__":
							UpperCamelCase     :   Union[str, Any]	      =		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     :   Optional[Any]	      =		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 PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						_snake_case    ( unittest.TestCase				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[Any]									=			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''')   ,    )
       return model




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''       ).images

       a		:int									=			torch.manual_seed(0       )
       a		:str									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''   ,    return_dict=_lowerCamelCase       )[0]

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:str									=			np.array([1.0, 1.0, 0.0, 1.0, 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 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

       a		:List[Any]									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

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





