import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest





@require_torchsde
class 					lowercase__						(   _lowerCamelCase):
						UpperCamelCase_     =		(DPMSolverSDEScheduler,)
						UpperCamelCase_     =		10


						def        __A       (       self	:				Optional[Any]    ,		**UpperCamelCase__	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							{
												    '''num_train_timesteps''': 1100,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''noise_sampler_seed''': 0,
												}

												config.update(**lowerCAmelCase_						)
												return config


						def        __A       (       self	:				str						):





												'''simple docstring'''





												for timesteps in [10, 50, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=lowerCAmelCase_						)


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]    ,		[0.0002, 0.002, 0.02]						):
																		self.check_over_configs(beta_start=lowerCAmelCase_    ,		beta_end=lowerCAmelCase_						)


						def        __A       (       self	:				str						):





												'''simple docstring'''





												for schedule in ["linear", "scaled_linear"]:
																		self.check_over_configs(beta_schedule=lowerCAmelCase_						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=lowerCAmelCase_						)


						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_scheduler_config()
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							scheduler_class(**lowerCAmelCase_						)

												scheduler.set_timesteps(self.num_inference_steps						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.dummy_model()
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE							:    List[str]							=							sample.to(lowerCAmelCase_						)

												for i, t in enumerate(scheduler.timesteps						):
																		SCREAMING_SNAKE_CASE							:    Dict							=							scheduler.scale_model_input(lowerCAmelCase_    ,		lowerCAmelCase_						)

																		SCREAMING_SNAKE_CASE							:    List[str]							=							model(lowerCAmelCase_    ,		lowerCAmelCase_						)

																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							scheduler.step(lowerCAmelCase_    ,		lowerCAmelCase_    ,		lowerCAmelCase_						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							output.prev_sample

												SCREAMING_SNAKE_CASE							:    str							=							torch.sum(torch.abs(lowerCAmelCase_						)						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.mean(torch.abs(lowerCAmelCase_						)						)

												if torch_device in ["mps"]:
																		assert abs(result_sum.item() - 167.47_8210_4492_1875						) < 1E-2
																		assert abs(result_mean.item() - 0.2178_7059_6456_5277						) < 1E-3
												elif torch_device in ["cuda"]:
																		assert abs(result_sum.item() - 171.59_3521_1181_6406						) < 1E-2
																		assert abs(result_mean.item() - 0.2_2342_9068_9229_9652						) < 1E-3
												else:
																		assert abs(result_sum.item() - 162.52_3834_2285_1562						) < 1E-2
																		assert abs(result_mean.item() - 0.211_6195_7085_1326						) < 1E-3


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:    int							=							self.get_scheduler_config(prediction_type='''v_prediction'''						)
												SCREAMING_SNAKE_CASE							:    str							=							scheduler_class(**lowerCAmelCase_						)

												scheduler.set_timesteps(self.num_inference_steps						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							self.dummy_model()
												SCREAMING_SNAKE_CASE							:    Dict							=							self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE							:    Dict							=							sample.to(lowerCAmelCase_						)

												for i, t in enumerate(scheduler.timesteps						):
																		SCREAMING_SNAKE_CASE							:    str							=							scheduler.scale_model_input(lowerCAmelCase_    ,		lowerCAmelCase_						)

																		SCREAMING_SNAKE_CASE							:    str							=							model(lowerCAmelCase_    ,		lowerCAmelCase_						)

																		SCREAMING_SNAKE_CASE							:    str							=							scheduler.step(lowerCAmelCase_    ,		lowerCAmelCase_    ,		lowerCAmelCase_						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							output.prev_sample

												SCREAMING_SNAKE_CASE							:    Tuple							=							torch.sum(torch.abs(lowerCAmelCase_						)						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							torch.mean(torch.abs(lowerCAmelCase_						)						)

												if torch_device in ["mps"]:
																		assert abs(result_sum.item() - 124.77_1492_0043_9453						) < 1E-2
																		assert abs(result_mean.item() - 0.1_6226_2890_1481_6284						) < 1E-3
												elif torch_device in ["cuda"]:
																		assert abs(result_sum.item() - 128.1_6633_6059_5703						) < 1E-2
																		assert abs(result_mean.item() - 0.1_6688_3260_0116_7297						) < 1E-3
												else:
																		assert abs(result_sum.item() - 119.8_4875_4882_8125						) < 1E-2
																		assert abs(result_mean.item() - 0.1560_5306_6253_6621						) < 1E-3


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:    int							=							self.get_scheduler_config()
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							scheduler_class(**lowerCAmelCase_						)

												scheduler.set_timesteps(self.num_inference_steps    ,		device=lowerCAmelCase_						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.dummy_model()
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.dummy_sample_deter.to(lowerCAmelCase_						) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																		SCREAMING_SNAKE_CASE							:    Any							=							scheduler.scale_model_input(lowerCAmelCase_    ,		lowerCAmelCase_						)

																		SCREAMING_SNAKE_CASE							:    Any							=							model(lowerCAmelCase_    ,		lowerCAmelCase_						)

																		SCREAMING_SNAKE_CASE							:    str							=							scheduler.step(lowerCAmelCase_    ,		lowerCAmelCase_    ,		lowerCAmelCase_						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							output.prev_sample

												SCREAMING_SNAKE_CASE							:    int							=							torch.sum(torch.abs(lowerCAmelCase_						)						)
												SCREAMING_SNAKE_CASE							:    Dict							=							torch.mean(torch.abs(lowerCAmelCase_						)						)

												if torch_device in ["mps"]:
																		assert abs(result_sum.item() - 167.46_9573_9746_0938						) < 1E-2
																		assert abs(result_mean.item() - 0.2_1805_9346_0798_2635						) < 1E-3
												elif torch_device in ["cuda"]:
																		assert abs(result_sum.item() - 171.59_3536_3769_5312						) < 1E-2
																		assert abs(result_mean.item() - 0.2_2342_9083_8241_5771						) < 1E-3
												else:
																		assert abs(result_sum.item() - 162.52_3834_2285_1562						) < 1E-2
																		assert abs(result_mean.item() - 0.211_6195_7085_1326						) < 1E-3



						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:    str							=							self.get_scheduler_config()
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							scheduler_class(**lowerCAmelCase_    ,		use_karras_sigmas=lowerCAmelCase_						)

												scheduler.set_timesteps(self.num_inference_steps    ,		device=lowerCAmelCase_						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.dummy_model()
												SCREAMING_SNAKE_CASE							:    Any							=							self.dummy_sample_deter.to(lowerCAmelCase_						) * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE							:    List[str]							=							sample.to(lowerCAmelCase_						)

												for t in scheduler.timesteps:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							scheduler.scale_model_input(lowerCAmelCase_    ,		lowerCAmelCase_						)

																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							model(lowerCAmelCase_    ,		lowerCAmelCase_						)

																		SCREAMING_SNAKE_CASE							:    Tuple							=							scheduler.step(lowerCAmelCase_    ,		lowerCAmelCase_    ,		lowerCAmelCase_						)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							output.prev_sample

												SCREAMING_SNAKE_CASE							:    Dict							=							torch.sum(torch.abs(lowerCAmelCase_						)						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							torch.mean(torch.abs(lowerCAmelCase_						)						)

												if torch_device in ["mps"]:
																		assert abs(result_sum.item() - 176.66_9741_3574_2188						) < 1E-2
																		assert abs(result_mean.item() - 0.2_3003_8727_3098_1811						) < 1E-2
												elif torch_device in ["cuda"]:
																		assert abs(result_sum.item() - 177.63_6535_6445_3125						) < 1E-2
																		assert abs(result_mean.item() - 0.2_3003_8727_3098_1811						) < 1E-2
												else:
																		assert abs(result_sum.item() - 170.3_1352_2338_8672						) < 1E-2
																		assert abs(result_mean.item() - 0.2_3003_8727_3098_1811						) < 1E-2







'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase    :							List[str]      =					logging.get_logger("transformers.models.speecht5")







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Dict:
	hf_model.apply_weight_norm()

	_snake_case   						=		checkpoint['input_conv.weight_g']
	_snake_case   						=		checkpoint['input_conv.weight_v']
	_snake_case   						=		checkpoint['input_conv.bias']

	for i in range(len(config.upsample_rates	)	):
		_snake_case   						=		checkpoint[F'upsamples.{i}.1.weight_g']
		_snake_case   						=		checkpoint[F'upsamples.{i}.1.weight_v']
		_snake_case   						=		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	)	):
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

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

	_snake_case   						=		checkpoint['output_conv.1.weight_g']
	_snake_case   						=		checkpoint['output_conv.1.weight_v']
	_snake_case   						=		checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A=None			,				__A=None			,				)       ->	List[Any]:
	if config_path is not None:
		_snake_case   						=		SpeechTaHifiGanConfig.from_pretrained(__A	)
	else:
		_snake_case   						=		SpeechTaHifiGanConfig()

	_snake_case   						=		SpeechTaHifiGan(__A	)

	_snake_case   						=		torch.load(__A	)
	load_weights(orig_checkpoint['model']['generator']			,				__A			,				__A	)

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

	model.save_pretrained(__A	)

	if repo_id:
		print('Pushing to the hub...'	)
		model.push_to_hub(__A	)


if __name__ == "__main__":
					lowercase    :							Dict      =					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."
					)

					lowercase    :							List[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,
					)

