

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
snake_case_         =					logging.get_logger('''transformers.models.speecht5''')

def       snake_case__     (       SCREAMING_SNAKE_CASE_						:       Optional[int]      ,	SCREAMING_SNAKE_CASE_						:       Union[str, Any]      ,	SCREAMING_SNAKE_CASE_						:       List[Any]     ):



     '''simple docstring'''





     hf_model.apply_weight_norm()

     lowercase__       :  Dict =			checkpoint['input_conv.weight_g']
     lowercase__       :  Any =			checkpoint['input_conv.weight_v']
     lowercase__       :  Dict =			checkpoint['input_conv.bias']

     for i in range(len(config.upsample_rates     )     ):
          lowercase__       :  Tuple =			checkpoint[f"""upsamples.{i}.1.weight_g"""]
          lowercase__       :  int =			checkpoint[f"""upsamples.{i}.1.weight_v"""]
          lowercase__       :  Union[str, Any] =			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     )     ):
               lowercase__       :  List[str] =			checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
               lowercase__       :  Optional[Any] =			checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
               lowercase__       :  Optional[Any] =			checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

               lowercase__       :  Optional[Any] =			checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
               lowercase__       :  Optional[Any] =			checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
               lowercase__       :  str =			checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

     lowercase__       :  Optional[int] =			checkpoint['output_conv.1.weight_g']
     lowercase__       :  Union[str, Any] =			checkpoint['output_conv.1.weight_v']
     lowercase__       :  Dict =			checkpoint['output_conv.1.bias']

     hf_model.remove_weight_norm()

@torch.no_grad()
def       snake_case__     (       SCREAMING_SNAKE_CASE_						:       Union[str, Any]      ,	SCREAMING_SNAKE_CASE_						:       Dict      ,	SCREAMING_SNAKE_CASE_						:       Optional[int]      ,	SCREAMING_SNAKE_CASE_						:       Tuple=None      ,	SCREAMING_SNAKE_CASE_						:       Optional[int]=None      ,	):



     '''simple docstring'''





     if config_path is not None:
          lowercase__       :  Any =			SpeechTaHifiGanConfig.from_pretrained(UpperCamelCase__     )
     else:
          lowercase__       :  Union[str, Any] =			SpeechTaHifiGanConfig()

     lowercase__       :  Union[str, Any] =			SpeechTaHifiGan(UpperCamelCase__     )

     lowercase__       :  Union[str, Any] =			torch.load(UpperCamelCase__     )
     load_weights(orig_checkpoint['model']['generator']      ,	UpperCamelCase__      ,	UpperCamelCase__     )

     lowercase__       :  Union[str, Any] =			np.load(UpperCamelCase__     )
     lowercase__       :  Optional[Any] =			stats[0].reshape(-1     )
     lowercase__       :  List[str] =			stats[1].reshape(-1     )
     lowercase__       :  int =			torch.from_numpy(UpperCamelCase__     ).float()
     lowercase__       :  List[str] =			torch.from_numpy(UpperCamelCase__     ).float()

     model.save_pretrained(UpperCamelCase__     )

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


if __name__ == "__main__":
       snake_case_         =					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.'''
       )

       snake_case_         =					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 tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			(IPNDMScheduler,)
				lowerCAmelCase					=			(('num_inference_steps', 5_0),)

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[str]:
					A             =					{"""num_train_timesteps""": 1000}
					config.update(**a__ )
					return config

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					Tuple:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config(**a__ )
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )
						# copy over dummy past residuals
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(a__ )
							A             =					scheduler_class.from_pretrained(a__ )
							new_scheduler.set_timesteps(a__ )
							# copy over dummy past residuals
							A             =					dummy_past_residuals[:]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

				def   _UpperCAmelCase       (    self )						->					Any:
					pass

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					int:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )

						# copy over dummy past residuals (must be after setting timesteps)
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(a__ )
							A             =					scheduler_class.from_pretrained(a__ )
							# copy over dummy past residuals
							new_scheduler.set_timesteps(a__ )

							# copy over dummy past residual (must be after setting timesteps)
							A             =					dummy_past_residuals[:]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[Any]:
					A             =					self.scheduler_classes[0]
					A             =					self.get_scheduler_config(**a__ )
					A             =					scheduler_class(**a__ )

					A             =					10
					A             =					self.dummy_model()
					A             =					self.dummy_sample_deter
					scheduler.set_timesteps(a__ )

					for i, t in enumerate(scheduler.timesteps ):
						A             =					model(a__					, a__ )
						A             =					scheduler.step(a__					, a__					, a__ ).prev_sample

					for i, t in enumerate(scheduler.timesteps ):
						A             =					model(a__					, a__ )
						A             =					scheduler.step(a__					, a__					, a__ ).prev_sample

					return sample

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					dict(self.forward_default_kwargs )

					A             =					kwargs.pop("""num_inference_steps"""					, a__ )

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )

						A             =					self.dummy_sample
						A             =					0.1 * sample

						if num_inference_steps is not None and hasattr(a__					, """set_timesteps""" ):
							scheduler.set_timesteps(a__ )
						elif num_inference_steps is not None and not hasattr(a__					, """set_timesteps""" ):
							A             =					num_inference_steps

						# copy over dummy past residuals (must be done after set_timesteps)
						A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
						A             =					dummy_past_residuals[:]

						A             =					scheduler.timesteps[5]
						A             =					scheduler.timesteps[6]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						self.assertEqual(output_a.shape					, sample.shape )
						self.assertEqual(output_a.shape					, output_a.shape )

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						self.assertEqual(output_a.shape					, sample.shape )
						self.assertEqual(output_a.shape					, output_a.shape )

				def   _UpperCAmelCase       (    self )						->					Tuple:
					for timesteps in [100, 1000]:
						self.check_over_configs(num_train_timesteps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					Any:
					for t, num_inference_steps in zip([1, 5, 10]					, [10, 50, 100] ):
						self.check_over_forward(num_inference_steps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					self.full_loop()
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 254_0529 ) < 10



