





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
snake_case__					:							Any													=     logging.get_logger('transformers.models.speecht5')



def _a				(	lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   int					,				lowerCamelCase:   Union[str, Any] )	->						Tuple:







      '''simple docstring'''






      hf_model.apply_weight_norm()

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

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

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

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

      hf_model.remove_weight_norm()



@torch.no_grad()
def _a				(	lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   List[str]=None					,				lowerCamelCase:   Union[str, Any]=None					,				)	->						Optional[Any]:







      '''simple docstring'''






      if config_path is not None:
            __A          =				SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase )
      else:
            __A          =				SpeechTaHifiGanConfig()

      __A          =				SpeechTaHifiGan(lowerCamelCase )

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

      __A          =				np.load(lowerCamelCase )
      __A          =				stats[0].reshape(-1 )
      __A          =				stats[1].reshape(-1 )
      __A          =				torch.from_numpy(lowerCamelCase ).float()
      __A          =				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__":
       snake_case__					:							List[str]													=     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__					:							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,
       )












import numpy as np
from PIL import Image



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				np.array(lowerCamelCase )
      if arr.shape[0] != arr.shape[1]:
            raise ValueError('''The input array is not a square matrix''' )
      __A          =				0
      __A          =				0
      __A          =				0
      __A          =				0

      # compute the shape of the output matrix
      __A          =				(arr.shape[0] - size) // stride + 1
      # initialize the output matrix with zeros of shape maxpool_shape
      __A          =				np.zeros((maxpool_shape, maxpool_shape) )

      while i < arr.shape[0]:
            if i + size > arr.shape[0]:
                  # if the end of the matrix is reached, break
                  break
            while j < arr.shape[1]:
                  # if the end of the matrix is reached, break
                  if j + size > arr.shape[1]:
                        break
                  # compute the maximum of the pooling matrix
                  __A          =				np.max(arr[i : i + size, j : j + size] )
                  # shift the pooling matrix by stride of column pixels
                  j += stride
                  mat_j += 1

            # shift the pooling matrix by stride of row pixels
            i += stride
            mat_i += 1

            # reset the column index to 0
            __A          =				0
            __A          =				0

      return updated_arr



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				np.array(lowerCamelCase )
      if arr.shape[0] != arr.shape[1]:
            raise ValueError('''The input array is not a square matrix''' )
      __A          =				0
      __A          =				0
      __A          =				0
      __A          =				0

      # compute the shape of the output matrix
      __A          =				(arr.shape[0] - size) // stride + 1
      # initialize the output matrix with zeros of shape avgpool_shape
      __A          =				np.zeros((avgpool_shape, avgpool_shape) )

      while i < arr.shape[0]:
            # if the end of the matrix is reached, break
            if i + size > arr.shape[0]:
                  break
            while j < arr.shape[1]:
                  # if the end of the matrix is reached, break
                  if j + size > arr.shape[1]:
                        break
                  # compute the average of the pooling matrix
                  __A          =				int(np.average(arr[i : i + size, j : j + size] ) )
                  # shift the pooling matrix by stride of column pixels
                  j += stride
                  mat_j += 1

            # shift the pooling matrix by stride of row pixels
            i += stride
            mat_i += 1
            # reset the column index to 0
            __A          =				0
            __A          =				0

      return updated_arr


# Main Function
if __name__ == "__main__":
       from doctest import testmod

       testmod(name='avgpooling', verbose=True)

       # Loading the image
       snake_case__					:							Optional[Any]													=     Image.open('path_to_image')

       # Converting the image to numpy array and maxpooling, displaying the result
       # Ensure that the image is a square matrix

       Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

       # Converting the image to numpy array and averagepooling, displaying the result
       # Ensure that the image is a square matrix

       Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()





