
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase    :   Any		       =				logging.get_logger("""transformers.models.speecht5""")







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

       lowercase				:	Any			      = checkpoint['input_conv.weight_g']
       lowercase				:	str			      = checkpoint['input_conv.weight_v']
       lowercase				:	Union[str, Any]			      = checkpoint['input_conv.bias']

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

                     lowercase				:	Tuple			      = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"]
                     lowercase				:	Tuple			      = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
                     lowercase				:	Tuple			      = checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]

       lowercase				:	Any			      = checkpoint['output_conv.1.weight_g']
       lowercase				:	str			      = checkpoint['output_conv.1.weight_v']
       lowercase				:	int			      = checkpoint['output_conv.1.bias']

       hf_model.remove_weight_norm()







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

       lowercase				:	Optional[int]			      = SpeechTaHifiGan(UpperCAmelCase_							)

       lowercase				:	Union[str, Any]			      = torch.load(UpperCAmelCase_							)
       load_weights(orig_checkpoint["""model"""]["""generator"""]   ,      UpperCAmelCase_   ,      UpperCAmelCase_							)

       lowercase				:	str			      = np.load(UpperCAmelCase_							)
       lowercase				:	Tuple			      = stats[0].reshape(-1							)
       lowercase				:	Union[str, Any]			      = stats[1].reshape(-1							)
       lowercase				:	Optional[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__":
  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    :   str		       =				parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )





'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor






class 				lowercase__      (				unittest.TestCase  ):





	def __init__(					self   :							int					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Optional[Any]=13					,lowerCamelCase__   :							List[Any]=3					,lowerCamelCase__   :							Optional[Any]=224					,lowerCamelCase__   :							int=30					,lowerCamelCase__   :							str=400					,lowerCamelCase__   :							str=True					,lowerCamelCase__   :							Any=None					,lowerCamelCase__   :							Tuple=True					,lowerCamelCase__   :							Tuple=[0.5, 0.5, 0.5]					,lowerCamelCase__   :							Optional[int]=[0.5, 0.5, 0.5]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  size if size is not None else {'height': 18, 'width': 18}
							_UpperCamelCase   :						List[Any]												=  parent
							_UpperCamelCase   :						Union[str, Any]												=  batch_size
							_UpperCamelCase   :						Dict												=  num_channels
							_UpperCamelCase   :						List[str]												=  image_size
							_UpperCamelCase   :						Optional[Any]												=  min_resolution
							_UpperCamelCase   :						Dict												=  max_resolution
							_UpperCamelCase   :						Tuple												=  do_resize
							_UpperCamelCase   :						Optional[int]												=  size
							_UpperCamelCase   :						Tuple												=  do_normalize
							_UpperCamelCase   :						str												=  image_mean
							_UpperCamelCase   :						int												=  image_std





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}






@require_torch
@require_vision
class 				lowercase__      (				lowercase		,   unittest.TestCase  ):
	lowercase__    							=     ViTImageProcessor if is_vision_available() else None





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  EfficientFormerImageProcessorTester(self					)





	@property
	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							return self.image_proc_tester.prepare_image_processor_dict()





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(lowerCamelCase__					,'image_mean'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'image_std'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'do_normalize'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'do_resize'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'size'					)					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						str												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							_UpperCamelCase   :						Optional[Any]												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,Image.Image					)

							# Test not batched input
							_UpperCamelCase   :						int												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						int												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						Any												=  self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							_UpperCamelCase   :						Optional[int]												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					,numpify=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,np.ndarray					)

							# Test not batched input
							_UpperCamelCase   :						Union[str, Any]												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						Optional[Any]												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						Any												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							_UpperCamelCase   :						Any												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					,torchify=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,torch.Tensor					)

							# Test not batched input
							_UpperCamelCase   :						Dict												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						Any												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)


