


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_UpperCAmelCase      :	Optional[Any]  =logging.get_logger(__name__)

_UpperCAmelCase      :	int  =["""UtteranceLevel"""]



@torch.no_grad()
def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)->			Optional[Any]:
				lowerCAmelCase_			:   Tuple     					=					torch.load(lowerCAmelCase_			, map_location='''cpu'''			)
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""			)

				lowerCAmelCase_			:   Union[str, Any]     					=					checkpoint['''Downstream''']

				lowerCAmelCase_			:   Tuple     					=					HubertConfig.from_pretrained(lowerCAmelCase_			)
				lowerCAmelCase_			:   List[Any]     					=					HubertForSequenceClassification.from_pretrained(lowerCAmelCase_			, config=lowerCAmelCase_			)
				lowerCAmelCase_			:   Optional[int]     					=					WavaVecaFeatureExtractor.from_pretrained(
				    lowerCAmelCase_			, return_attention_mask=lowerCAmelCase_			, do_normalize=lowerCAmelCase_			)

				if hf_congfig.use_weighted_layer_sum:
								lowerCAmelCase_			:   List[str]     					=					checkpoint['''Featurizer''']['''weights''']

				lowerCAmelCase_			:   Any     					=					downstream_dict['''projector.weight''']
				lowerCAmelCase_			:   List[Any]     					=					downstream_dict['''projector.bias''']
				lowerCAmelCase_			:   Optional[int]     					=					downstream_dict['''model.post_net.linear.weight''']
				lowerCAmelCase_			:   Any     					=					downstream_dict['''model.post_net.linear.bias''']

				hf_feature_extractor.save_pretrained(lowerCAmelCase_			)
				hf_model.save_pretrained(lowerCAmelCase_			)


if __name__ == "__main__":
						_UpperCAmelCase      :	Optional[int]  =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      :	Optional[int]  =parser.parse_args()
						convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


"""simple docstring"""
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					Tuple   )						->     str:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		Optional[Any]        =     image.shape
       __magic_name__			:		str        =     cols - block_size[1] + 1
       __magic_name__			:		int        =     rows - block_size[0] + 1
       __magic_name__			:		Optional[Any]        =     zeros((dst_height * dst_width, block_size[1] * block_size[0])   )
       __magic_name__			:		List[Any]        =     0
       for i in range(0     , lowerCamelCase   ):
              for j in range(0     , lowerCamelCase   ):
                     __magic_name__			:		List[Any]        =     ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
                     __magic_name__			:		str        =     window
                     row += 1

       return image_array







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					List[str]   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		List[str]        =     image.shape[0], image.shape[1]
       __magic_name__			:		Any        =     filter_kernel.shape[0]
       __magic_name__			:		Optional[Any]        =     k_size // 2
       # Pads image with the edge values of array.
       __magic_name__			:		Union[str, Any]        =     pad(lowerCamelCase     , lowerCamelCase     , mode='''edge'''   )

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       __magic_name__			:		List[Any]        =     imacol(lowerCamelCase     , (k_size, k_size)   )

       #  turn the kernel into shape(k*k, 1)
       __magic_name__			:		Tuple        =     ravel(lowerCamelCase   )
       # reshape and get the dst image
       __magic_name__			:		int        =     dot(lowerCamelCase     , lowerCamelCase   ).reshape(lowerCamelCase     , lowerCamelCase   )
       return dst


if __name__ == "__main__":
 # read original image
 A									=						imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 A									=						cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 A									=						array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 A									=						img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)
