




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCamelCase      :  List[str]      						=						logging.get_logger("transformers.models.speecht5")




def 							_UpperCAmelCase  (UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : Dict      ,    UpperCamelCase_ : List[str]       ):
    '''simple docstring'''
    hf_model.apply_weight_norm()

    _lowerCAmelCase    :    Optional[int]										=      checkpoint["input_conv.weight_g"]
    _lowerCAmelCase    :    List[str]										=      checkpoint["input_conv.weight_v"]
    _lowerCAmelCase    :    List[str]										=      checkpoint["input_conv.bias"]

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

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

    _lowerCAmelCase    :    str										=      checkpoint["output_conv.1.weight_g"]
    _lowerCAmelCase    :    Dict										=      checkpoint["output_conv.1.weight_v"]
    _lowerCAmelCase    :    Tuple										=      checkpoint["output_conv.1.bias"]

    hf_model.remove_weight_norm()




@torch.no_grad()
def 							_UpperCAmelCase  (UpperCamelCase_ : int      ,    UpperCamelCase_ : Dict      ,    UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : Dict=None      ,    UpperCamelCase_ : List[Any]=None      ,    ):
    '''simple docstring'''







    if config_path is not None:
        _lowerCAmelCase    :    Any										=      SpeechTaHifiGanConfig.from_pretrained(UpperCamelCase_       )
    else:
        _lowerCAmelCase    :    Tuple										=      SpeechTaHifiGanConfig()

    _lowerCAmelCase    :    Union[str, Any]										=      SpeechTaHifiGan(UpperCamelCase_       )

    _lowerCAmelCase    :    Tuple										=      torch.load(UpperCamelCase_       )
    load_weights(orig_checkpoint["""model"""]["""generator"""]      ,    UpperCamelCase_      ,    UpperCamelCase_       )

    _lowerCAmelCase    :    Tuple										=      np.load(UpperCamelCase_       )
    _lowerCAmelCase    :    Dict										=      stats[0].reshape(-1       )
    _lowerCAmelCase    :    Union[str, Any]										=      stats[1].reshape(-1       )
    _lowerCAmelCase    :    Any										=      torch.from_numpy(UpperCamelCase_       ).float()
    _lowerCAmelCase    :    Union[str, Any]										=      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__":
 _lowerCamelCase      :  Optional[int]      						=						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      :  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 cva
import numpy as np
class        __snake_case  :



				def __init__(      self      :		List[str]		,	_UpperCAmelCase      :		float		,	_UpperCAmelCase      :		int      )  ->    int:





								'''simple docstring'''




								if k in (0.04, 0.06):
												_lowerCAmelCase    :    str										=      k
												_lowerCAmelCase    :    Optional[Any]										=      window_size
								else:
												raise ValueError("""invalid k value"""      )



				def __str__(      self      :		int      )  ->    str:





								'''simple docstring'''




								return str(self.k      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		str      )  ->    tuple[cva.Mat, list[list[int]]]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      cva.imread(_UpperCAmelCase		,	0      )
								_lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      img.shape
								_lowerCAmelCase    :    list[list[int]]										=      []
								_lowerCAmelCase    :    int										=      img.copy()
								_lowerCAmelCase    :    str										=      cva.cvtColor(_UpperCAmelCase		,	cva.COLOR_GRAY2RGB      )
								_lowerCAmelCase					,    _lowerCAmelCase    :    int										=      np.gradient(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      dx**2
								_lowerCAmelCase    :    Optional[int]										=      dy**2
								_lowerCAmelCase    :    Optional[Any]										=      dx * dy
								_lowerCAmelCase    :    Dict										=      0.04
								_lowerCAmelCase    :    Tuple										=      self.window_size // 2
								for y in range(_UpperCAmelCase		,	h - offset      ):
												for x in range(_UpperCAmelCase		,	w - offset      ):
																_lowerCAmelCase    :    Optional[int]										=      ixx[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()
																_lowerCAmelCase    :    int										=      iyy[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()
																_lowerCAmelCase    :    Any										=      ixy[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()

																_lowerCAmelCase    :    Dict										=      (wxx * wyy) - (wxy**2)
																_lowerCAmelCase    :    Union[str, Any]										=      wxx + wyy
																_lowerCAmelCase    :    int										=      det - k * (trace**2)
																# Can change the value
																if r > 0.5:
																				corner_list.append([x, y, r]      )
																				color_img.itemset((y, x, 0)		,	0      )
																				color_img.itemset((y, x, 1)		,	0      )
																				color_img.itemset((y, x, 2)		,	255      )
								return color_img, corner_list


if __name__ == "__main__":
	_lowerCamelCase      :  int      						=						HarrisCorner(0.0_4, 3)
	_lowerCamelCase						, _lowerCamelCase      :  Any      						=						edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)

