





"""simple docstring"""






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       List[str]:






       """simple docstring"""




       hf_model.apply_weight_norm()

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

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

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

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

       hf_model.remove_weight_norm()

@torch.no_grad()
def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=None,)		->       Any:






       """simple docstring"""




       if config_path is not None:
              _UpperCAmelCase     =     SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE							)
       else:
              _UpperCAmelCase     =     SpeechTaHifiGanConfig()

       _UpperCAmelCase     =     SpeechTaHifiGan(SCREAMING_SNAKE_CASE							)

       _UpperCAmelCase     =     torch.load(SCREAMING_SNAKE_CASE							)
       load_weights(orig_checkpoint['model']['generator'],SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

       _UpperCAmelCase     =     np.load(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     stats[0].reshape(-1							)
       _UpperCAmelCase     =     stats[1].reshape(-1							)
       _UpperCAmelCase     =     torch.from_numpy(SCREAMING_SNAKE_CASE							).float()
       _UpperCAmelCase     =     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__":
    lowerCAmelCase_			=							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.'''
    )

    lowerCAmelCase_			=							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


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ChineseCLIPImageProcessor



class 	lowerCAmelCase					(      unittest.TestCase							):






       def __init__(   self    ,  a__    ,  a__=7    ,  a__=3    ,  a__=18    ,  a__=30    ,  a__=4_00    ,  a__=True    ,  a__=None    ,  a__=True    ,  a__=None    ,  a__=True    ,  a__=[0.48_145_466, 0.4_578_275, 0.40_821_073]    ,  a__=[0.26_862_954, 0.26_130_258, 0.27_577_711]    ,  a__=True    ,  ):
              _UpperCAmelCase     =     size if size is not None else {'height': 2_24, 'width': 2_24}
              _UpperCAmelCase     =     crop_size if crop_size is not None else {'height': 18, 'width': 18}
              _UpperCAmelCase     =     parent
              _UpperCAmelCase     =     batch_size
              _UpperCAmelCase     =     num_channels
              _UpperCAmelCase     =     image_size
              _UpperCAmelCase     =     min_resolution
              _UpperCAmelCase     =     max_resolution
              _UpperCAmelCase     =     do_resize
              _UpperCAmelCase     =     size
              _UpperCAmelCase     =     do_center_crop
              _UpperCAmelCase     =     crop_size
              _UpperCAmelCase     =     do_normalize
              _UpperCAmelCase     =     image_mean
              _UpperCAmelCase     =     image_std
              _UpperCAmelCase     =     do_convert_rgb






       def 							__A (   self     ):
              return {
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "do_center_crop": self.do_center_crop,
                  "crop_size": self.crop_size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
                  "do_convert_rgb": self.do_convert_rgb,
              }






       def 							__A (   self    ,  a__=False    ,  a__=False    ,  a__=False     ):
              assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

              if equal_resolution:
                     _UpperCAmelCase     =     []
                     for i in range(self.batch_size     ):
                            image_inputs.append(
                                np.random.randint(
                                    2_55    ,  size=(self.num_channels, self.max_resolution, self.max_resolution)    ,  dtype=np.uinta     )     )
              else:
                     _UpperCAmelCase     =     []
                     for i in range(self.batch_size     ):
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     np.random.choice(np.arange(self.min_resolution    ,  self.max_resolution     )    ,  2     )
                            image_inputs.append(np.random.randint(2_55    ,  size=(self.num_channels, width, height)    ,  dtype=np.uinta     )     )

              if not numpify and not torchify:
                     # PIL expects the channel dimension as last dimension
                     _UpperCAmelCase     =     [Image.fromarray(np.moveaxis(a__    ,  0    ,  -1     )     ) for x in image_inputs]

              if torchify:
                     _UpperCAmelCase     =     [torch.from_numpy(a__     ) for x in image_inputs]

              return image_inputs



@require_torch
@require_vision
class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  ChineseCLIPImageProcessor if is_vision_available() else None






       def 							__A (   self     ):
              _UpperCAmelCase     =     ChineseCLIPImageProcessingTester(self    ,  do_center_crop=a__     )






       @property
       def 							__A (   self     ):
              return self.image_processor_tester.prepare_image_processor_dict()






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              self.assertTrue(hasattr(a__    ,  'do_resize'     )     )
              self.assertTrue(hasattr(a__    ,  'size'     )     )
              self.assertTrue(hasattr(a__    ,  'do_center_crop'     )     )
              self.assertTrue(hasattr(a__    ,  'center_crop'     )     )
              self.assertTrue(hasattr(a__    ,  'do_normalize'     )     )
              self.assertTrue(hasattr(a__    ,  'image_mean'     )     )
              self.assertTrue(hasattr(a__    ,  'image_std'     )     )
              self.assertTrue(hasattr(a__    ,  'do_convert_rgb'     )     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.image_processing_class.from_dict(self.image_processor_dict     )
              self.assertEqual(image_processor.size    ,  {'height': 2_24, 'width': 2_24}     )
              self.assertEqual(image_processor.crop_size    ,  {'height': 18, 'width': 18}     )

              _UpperCAmelCase     =     self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42    ,  crop_size=84     )
              self.assertEqual(image_processor.size    ,  {'shortest_edge': 42}     )
              self.assertEqual(image_processor.crop_size    ,  {'height': 84, 'width': 84}     )






       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random PIL images
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  Image.Image     )

              # Test not batched input
              _UpperCAmelCase     =     image_processing(image_inputs[0]    ,  return_tensors='pt'     ).pixel_values
              self.assertEqual(
                  encoded_images.shape    ,  (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  )    ,  )

              # Test batched
              _UpperCAmelCase     =     image_processing(a__    ,  return_tensors='pt'     ).pixel_values
              self.assertEqual(
                  encoded_images.shape    ,  (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  )    ,  )






       def 							__A (   self     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random numpy tensors
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__    ,  numpify=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  np.ndarray     )

              # Test not batched input
              _UpperCAmelCase     =     image_processing(image_inputs[0]    ,  return_tensors='pt'     ).pixel_values
              self.assertEqual(
                  encoded_images.shape    ,  (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  )    ,  )

              # Test batched
              _UpperCAmelCase     =     image_processing(a__    ,  return_tensors='pt'     ).pixel_values
              self.assertEqual(
                  encoded_images.shape    ,  (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  )    ,  )






       def 							__A (   self     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random PyTorch tensors
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__    ,  torchify=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  torch.Tensor     )

              # Test not batched input
              _UpperCAmelCase     =     image_processing(image_inputs[0]    ,  return_tensors='pt'     ).pixel_values
              self.assertEqual(
                  encoded_images.shape    ,  (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  )    ,  )

              # Test batched
              _UpperCAmelCase     =     image_processing(a__    ,  return_tensors='pt'     ).pixel_values
              self.assertEqual(
                  encoded_images.shape    ,  (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  )    ,  )



@require_torch
@require_vision
class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  ChineseCLIPImageProcessor if is_vision_available() else None






       def 							__A (   self     ):
              _UpperCAmelCase     =     ChineseCLIPImageProcessingTester(self    ,  num_channels=4    ,  do_center_crop=a__     )
              _UpperCAmelCase     =     3






       @property
       def 							__A (   self     ):
              return self.image_processor_tester.prepare_image_processor_dict()






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              self.assertTrue(hasattr(a__    ,  'do_resize'     )     )
              self.assertTrue(hasattr(a__    ,  'size'     )     )
              self.assertTrue(hasattr(a__    ,  'do_center_crop'     )     )
              self.assertTrue(hasattr(a__    ,  'center_crop'     )     )
              self.assertTrue(hasattr(a__    ,  'do_normalize'     )     )
              self.assertTrue(hasattr(a__    ,  'image_mean'     )     )
              self.assertTrue(hasattr(a__    ,  'image_std'     )     )
              self.assertTrue(hasattr(a__    ,  'do_convert_rgb'     )     )






       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              # Initialize image_processing
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random PIL images
              _UpperCAmelCase     =     self.image_processor_tester.prepare_inputs(equal_resolution=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  Image.Image     )

              # Test not batched input
              _UpperCAmelCase     =     image_processing(image_inputs[0]    ,  return_tensors='pt'     ).pixel_values
              self.assertEqual(
                  encoded_images.shape    ,  (
                      1,
                      self.expected_encoded_image_num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  )    ,  )

              # Test batched
              _UpperCAmelCase     =     image_processing(a__    ,  return_tensors='pt'     ).pixel_values
              self.assertEqual(
                  encoded_images.shape    ,  (
                      self.image_processor_tester.batch_size,
                      self.expected_encoded_image_num_channels,
                      self.image_processor_tester.crop_size['height'],
                      self.image_processor_tester.crop_size['width'],
                  )    ,  )






