




import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
a													=			logging.get_logger(__name__)




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			WavaVecaForSequenceClassification.from_pretrained(snake_case			,     config=snake_case       )
						_UpperCAmelCase      =			downstream_dict["projector.weight"]
						_UpperCAmelCase      =			downstream_dict["projector.bias"]
						_UpperCAmelCase      =			downstream_dict["model.post_net.linear.weight"]
						_UpperCAmelCase      =			downstream_dict["model.post_net.linear.bias"]
						return model




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Dict:
						_UpperCAmelCase      =			WavaVecaForAudioFrameClassification.from_pretrained(snake_case			,     config=snake_case       )
						_UpperCAmelCase      =			downstream_dict["model.linear.weight"]
						_UpperCAmelCase      =			downstream_dict["model.linear.bias"]
						return model




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						_UpperCAmelCase      =			WavaVecaForXVector.from_pretrained(snake_case			,     config=snake_case       )
						_UpperCAmelCase      =			downstream_dict["connector.weight"]
						_UpperCAmelCase      =			downstream_dict["connector.bias"]
						for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
												_UpperCAmelCase      =			downstream_dict[
												    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
												]
												_UpperCAmelCase      =			downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

						_UpperCAmelCase      =			downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
						_UpperCAmelCase      =			downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
						_UpperCAmelCase      =			downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
						_UpperCAmelCase      =			downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
						_UpperCAmelCase      =			downstream_dict["objective.W"]
						return model




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Any:
						_UpperCAmelCase      =			torch.load(snake_case			,     map_location="""cpu"""       )

						_UpperCAmelCase      =			checkpoint["Downstream"]

						_UpperCAmelCase      =			WavaVecaConfig.from_pretrained(snake_case       )
						_UpperCAmelCase      =			WavaVecaFeatureExtractor.from_pretrained(
						    snake_case			,     return_attention_mask=snake_case			,     do_normalize=snake_case       )

						_UpperCAmelCase      =			hf_config.architectures[0]
						if arch.endswith("""ForSequenceClassification"""       ):
												_UpperCAmelCase      =			convert_classification(snake_case			,     snake_case			,     snake_case       )
						elif arch.endswith("""ForAudioFrameClassification"""       ):
												_UpperCAmelCase      =			convert_diarization(snake_case			,     snake_case			,     snake_case       )
						elif arch.endswith("""ForXVector"""       ):
												_UpperCAmelCase      =			convert_xvector(snake_case			,     snake_case			,     snake_case       )
						else:
												raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"       )

						if hf_config.use_weighted_layer_sum:
												_UpperCAmelCase      =			checkpoint["Featurizer"]["weights"]

						hf_feature_extractor.save_pretrained(snake_case       )
						hf_model.save_pretrained(snake_case       )


if __name__ == "__main__":
				a													=			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.")
				a													=			parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				bool:
						_UpperCAmelCase      =			len(snake_case       ) + 1
						_UpperCAmelCase      =			len(snake_case       ) + 1

						# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
						# length i of input_string matches with prefix string of length j of
						# given pattern.
						# "dp" stands for dynamic programming.
						_UpperCAmelCase      =			[[0 for i in range(snake_case       )] for j in range(snake_case       )]

						# since string of zero length match pattern of zero length
						_UpperCAmelCase      =			1

						# since pattern of zero length will never match with string of non-zero length
						for i in range(1			,     snake_case       ):
												_UpperCAmelCase      =			0

						# since string of zero length will match with pattern where there
						# is at least one * alternatively
						for j in range(1			,     snake_case       ):
												_UpperCAmelCase      =			dp[0][j - 2] if pattern[j - 1] == """*""" else 0

						# now using bottom-up approach to find for all remaining lengths
						for i in range(1			,     snake_case       ):
												for j in range(1			,     snake_case       ):
																		if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																								_UpperCAmelCase      =			dp[i - 1][j - 1]

																		elif pattern[j - 1] == "*":
																								if dp[i][j - 2] == 1:
																														_UpperCAmelCase      =			1
																								elif pattern[j - 2] in (input_string[i - 1], "."):
																														_UpperCAmelCase      =			dp[i - 1][j]
																								else:
																														_UpperCAmelCase      =			0
																		else:
																								_UpperCAmelCase      =			0

						return bool(dp[-1][-1]       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# inputing the strings
				# input_string = input("input a string :")
				# pattern = input("input a pattern :")

				a													=			"aab"
				a													=			"c*a*b"

				# using function to check whether given string matches the given pattern
				if match_pattern(input_string, pattern):
								print(F'{input_string} matches the given pattern {pattern}')
				else:
								print(F'{input_string} does not match with the given pattern {pattern}')