

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal


def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  str	)		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			list(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			list(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			0
 for i in range(len(_UpperCamelCase	)	):
  if lista[i] != lista[i]:
   count += 1
   SCREAMING_SNAKE_CASE								=			'_'
 if count > 1:
  return False
 else:
  return "".join(_UpperCamelCase	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  list[str]	)		->							List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[]
 while True:
  SCREAMING_SNAKE_CASE								=			['$'] * len(_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			[]
  for i in range(len(_UpperCamelCase	)	):
   for j in range(i + 1		,							len(_UpperCamelCase	)	):
    SCREAMING_SNAKE_CASE								=			compare_string(binary[i]		,							binary[j]	)
    if k is False:
     SCREAMING_SNAKE_CASE								=			'*'
     SCREAMING_SNAKE_CASE								=			'*'
     temp.append('X'	)
  for i in range(len(_UpperCamelCase	)	):
   if checka[i] == "$":
    pi.append(binary[i]	)
  if len(_UpperCamelCase	) == 0:
   return pi
  SCREAMING_SNAKE_CASE								=			list(set(_UpperCamelCase	)	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  Sequence[float]	)		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[]
 for minterm in minterms:
  SCREAMING_SNAKE_CASE								=			''
  for _ in range(_UpperCamelCase	):
   SCREAMING_SNAKE_CASE								=			str(minterm % 2	) + string
   minterm //= 2
  temp.append(_UpperCamelCase	)
 return temp


def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  int	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			list(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			list(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			0
 for i in range(len(_UpperCamelCase	)	):
  if lista[i] != lista[i]:
   count_n += 1
 return count_n == count


def        __lowerCAmelCase    (       _UpperCamelCase		:  list[list[int]]		,							_UpperCamelCase		:  list[str]	)		->							Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[]
 SCREAMING_SNAKE_CASE								=			[0] * len(_UpperCamelCase	)
 for i in range(len(chart[0]	)	):
  SCREAMING_SNAKE_CASE								=			0
  SCREAMING_SNAKE_CASE								=			-1
  for j in range(len(_UpperCamelCase	)	):
   if chart[j][i] == 1:
    count += 1
    SCREAMING_SNAKE_CASE								=			j
  if count == 1:
   SCREAMING_SNAKE_CASE								=			1
 for i in range(len(_UpperCamelCase	)	):
  if select[i] == 1:
   for j in range(len(chart[0]	)	):
    if chart[i][j] == 1:
     for k in range(len(_UpperCamelCase	)	):
      SCREAMING_SNAKE_CASE								=			0
   temp.append(prime_implicants[i]	)
 while True:
  SCREAMING_SNAKE_CASE								=			0
  SCREAMING_SNAKE_CASE								=			-1
  SCREAMING_SNAKE_CASE								=			0
  for i in range(len(_UpperCamelCase	)	):
   SCREAMING_SNAKE_CASE								=			chart[i].count(1	)
   if count_n > max_n:
    SCREAMING_SNAKE_CASE								=			count_n
    SCREAMING_SNAKE_CASE								=			i

  if max_n == 0:
   return temp

  temp.append(prime_implicants[rem]	)

  for i in range(len(chart[0]	)	):
   if chart[rem][i] == 1:
    for j in range(len(_UpperCamelCase	)	):
     SCREAMING_SNAKE_CASE								=			0


def        __lowerCAmelCase    (       _UpperCamelCase		:  list[str]		,							_UpperCamelCase		:  list[str]	)		->							List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[[0 for x in range(len(_UpperCamelCase	)	)] for x in range(len(_UpperCamelCase	)	)]
 for i in range(len(_UpperCamelCase	)	):
  SCREAMING_SNAKE_CASE								=			prime_implicants[i].count('_'	)
  for j in range(len(_UpperCamelCase	)	):
   if is_for_table(prime_implicants[i]		,							binary[j]		,							_UpperCamelCase	):
    SCREAMING_SNAKE_CASE								=			1

 return chart


def        __lowerCAmelCase    (       )		->							Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			int(input('Enter the no. of variables\n'	)	)
 SCREAMING_SNAKE_CASE								=			[
     float(_UpperCamelCase	)
     for x in input(
         'Enter the decimal representation of Minterms \'Spaces Separated\'\n'	).split()
 ]
 SCREAMING_SNAKE_CASE								=			decimal_to_binary(_UpperCamelCase		,							_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			check(_UpperCamelCase	)
 print('Prime Implicants are:'	)
 print(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			prime_implicant_chart(_UpperCamelCase		,							_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			selection(_UpperCamelCase		,							_UpperCamelCase	)
 print('Essential Prime Implicants are:'	)
 print(_UpperCamelCase	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
a_    :						List[Any]    		=      logging.get_logger("transformers.models.speecht5")


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Any	)		->							Dict:
 '''simple docstring'''


 hf_model.apply_weight_norm()

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

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

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

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

 hf_model.remove_weight_norm()


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Any=None		,							_UpperCamelCase		:  List[str]=None		,							)		->							Tuple:
 '''simple docstring'''


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

 SCREAMING_SNAKE_CASE								=			SpeechTaHifiGan(_UpperCamelCase	)

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

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

      a_    :						Tuple    		=      parser.parse_args()
      convert_hifigan_checkpoint(
          args.checkpoint_path,
          args.stats_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )

