

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_							:					Any		 =   logging.get_logger(__name__)
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   int			,a__    :   Tuple       )					->				Optional[int]:
	__A :			int		    =			UniSpeechSatForSequenceClassification.from_pretrained(a__			,config=a__       )
	__A :			Dict		    =			downstream_dict["""projector.weight"""]
	__A :			int		    =			downstream_dict["""projector.bias"""]
	__A :			Any		    =			downstream_dict["""model.post_net.linear.weight"""]
	__A :			Any		    =			downstream_dict["""model.post_net.linear.bias"""]
	return model
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[int]			,a__    :   Optional[Any]			,a__    :   str       )					->				str:
	__A :			Dict		    =			UniSpeechSatForAudioFrameClassification.from_pretrained(a__			,config=a__       )
	__A :			List[str]		    =			downstream_dict["""model.linear.weight"""]
	__A :			int		    =			downstream_dict["""model.linear.bias"""]
	return model
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   str			,a__    :   Optional[Any]       )					->				str:
	__A :			Optional[Any]		    =			UniSpeechSatForXVector.from_pretrained(a__			,config=a__       )
	__A :			int		    =			downstream_dict["""connector.weight"""]
	__A :			Dict		    =			downstream_dict["""connector.bias"""]
	for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
		__A :			str		    =			downstream_dict[
		    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
		]
		__A :			Tuple		    =			downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

	__A :			str		    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
	__A :			Optional[Any]		    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
	__A :			Union[str, Any]		    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
	__A :			Tuple		    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
	__A :			Union[str, Any]		    =			downstream_dict["""objective.W"""]
	return model
@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   Dict			,a__    :   List[Any]			,a__    :   Tuple       )					->				List[str]:
	__A :			Dict		    =			torch.load(a__			,map_location="""cpu"""       )

	__A :			int		    =			checkpoint["""Downstream"""]

	__A :			Optional[int]		    =			UniSpeechSatConfig.from_pretrained(a__       )
	__A :			Optional[Any]		    =			WavaVecaFeatureExtractor.from_pretrained(
	    a__			,return_attention_mask=a__			,do_normalize=a__       )

	__A :			Union[str, Any]		    =			hf_config.architectures[0]
	if arch.endswith("""ForSequenceClassification"""       ):
		__A :			List[str]		    =			convert_classification(a__			,a__			,a__       )
	elif arch.endswith("""ForAudioFrameClassification"""       ):
		__A :			List[Any]		    =			convert_diarization(a__			,a__			,a__       )
	elif arch.endswith("""ForXVector"""       ):
		__A :			Tuple		    =			convert_xvector(a__			,a__			,a__       )
	else:
		raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""       )

	if hf_config.use_weighted_layer_sum:
		__A :			List[str]		    =			checkpoint["""Featurizer"""]["""weights"""]

	hf_feature_extractor.save_pretrained(a__       )
	hf_model.save_pretrained(a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Any		 =   argparse.ArgumentParser()
			parser.add_argument(
			    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
			)
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
			parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
			UpperCAmelCase_							:					Dict		 =   parser.parse_args()
			convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     np.ndarray:

						# prepare kernel
						# the kernel size have to be odd
						if (ksize % 2) == 0:
												_lowercase					       =					ksize + 1
						_lowercase					       =					np.zeros((ksize, ksize)		,   dtype=np.floataa       )

						# each value
						for y in range(SCREAMING_SNAKE_CASE_       ):
												for x in range(SCREAMING_SNAKE_CASE_       ):
																		# distance from center
																		_lowercase					       =					x - ksize // 2
																		_lowercase					       =					y - ksize // 2

																		# degree to radiant
																		_lowercase					       =					theta / 1_80 * np.pi
																		_lowercase					       =					np.cos(_theta       )
																		_lowercase					       =					np.sin(_theta       )

																		# get kernel x
																		_lowercase					       =					cos_theta * px + sin_theta * py

																		# get kernel y
																		_lowercase					       =					-sin_theta * px + cos_theta * py

																		# fill kernel
																		_lowercase					       =					np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)       ) * np.cos(2 * np.pi * _x / lambd + psi       )

						return gabor


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# read original image
		A		: Optional[Any]								=     imread('''../image_data/lena.jpg''')
		# turn image in gray scale value
		A		: Optional[int]								=     cvtColor(img, COLOR_BGR2GRAY)

		# Apply multiple Kernel to detect edges
		A		: Optional[int]								=     np.zeros(gray.shape[:2])
		for theta in [0, 30, 60, 90, 120, 150]:
				A		: Optional[int]								=     gabor_filter_kernel(10, 8, theta, 10, 0, 0)
				out += filteraD(gray, CV_8UC3, kernel_aa)
		A		: int								=     out / out.max() * 255
		A		: str								=     out.astype(np.uinta)

		imshow('''Original''', gray)
		imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

		waitKey(0)