
import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:Dict										=    logging.get_logger(__name__)


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Optional[int]:







				"""simple docstring"""



				__A             =			WavaVecaForSequenceClassification.from_pretrained(a_		,       config=a_						)
				__A             =			downstream_dict["projector.weight"]
				__A             =			downstream_dict["projector.bias"]
				__A             =			downstream_dict["model.post_net.linear.weight"]
				__A             =			downstream_dict["model.post_net.linear.bias"]
				return model


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     int:







				"""simple docstring"""



				__A             =			WavaVecaForAudioFrameClassification.from_pretrained(a_		,       config=a_						)
				__A             =			downstream_dict["model.linear.weight"]
				__A             =			downstream_dict["model.linear.bias"]
				return model


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Any:







				"""simple docstring"""



				__A             =			WavaVecaForXVector.from_pretrained(a_		,       config=a_						)
				__A             =			downstream_dict["connector.weight"]
				__A             =			downstream_dict["connector.bias"]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
								__A             =			downstream_dict[
								    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
								]
								__A             =			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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


@torch.no_grad()
def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_						)		->     Optional[int]:







				"""simple docstring"""



				__A             =			torch.load(a_		,       map_location="cpu"						)

				__A             =			checkpoint["Downstream"]

				__A             =			WavaVecaConfig.from_pretrained(a_						)
				__A             =			WavaVecaFeatureExtractor.from_pretrained(
				    a_		,       return_attention_mask=a_		,       do_normalize=a_						)

				__A             =			hf_config.architectures[0]
				if arch.endswith("ForSequenceClassification"						):
								__A             =			convert_classification(a_		,       a_		,       a_						)
				elif arch.endswith("ForAudioFrameClassification"						):
								__A             =			convert_diarization(a_		,       a_		,       a_						)
				elif arch.endswith("ForXVector"						):
								__A             =			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             =			checkpoint["Featurizer"]["weights"]

				hf_feature_extractor.save_pretrained(a_						)
				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:List[str]										=    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.')
					SCREAMING_SNAKE_CASE							:Tuple										=    parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


import copy
import re





class 							UpperCAmelCase				:




	'''simple docstring'''





	snake_case_    =					"hp"
	snake_case_    =					{}
	snake_case_    =					None






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict							,A :			Dict							,A :			Any			):
					__A             =			prefix
					__A             =			defaults
					cls.build_naming_info()






	@staticmethod
	def     UpperCamelCase_  (	A :			Dict							,A :			int			):
					if len(A			) == 0:
									return ""
					__A             =			None
					if any(char.isdigit() for char in word			):
									raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''			)
					if word in info["short_word"]:
									return info["short_word"][word]
					for prefix_len in range(1							,len(A			) + 1			):
									__A             =			word[:prefix_len]
									if prefix in info["reverse_short_word"]:
													continue
									else:
													__A             =			prefix
													break

					if short_word is None:
									# Paranoid fallback
									def int_to_alphabetic(A :			str			):
													__A             =			""
													while integer != 0:
																	__A             =			chr(ord("A"			) + integer % 10			) + s
																	integer //= 10
													return s

									__A             =			0
									while True:
													__A             =			word + "#" + int_to_alphabetic(A			)
													if sword in info["reverse_short_word"]:
																	continue
													else:
																	__A             =			sword
																	break

					__A             =			short_word
					__A             =			word
					return short_word






	@staticmethod
	def     UpperCamelCase_  (	A :			int							,A :			Tuple			):
					__A             =			param_name.split("_"			)

					__A             =			[TrialShortNamer.shortname_for_word(A							,A			) for word in words]

					# We try to create a separatorless short name, but if there is a collision we have to fallback
					# to a separated short name
					__A             =			["", "_"]

					for separator in separators:
									__A             =			separator.join(A			)
									if shortname not in info["reverse_short_param"]:
													__A             =			shortname
													__A             =			param_name
													return shortname

					return param_name






	@staticmethod
	def     UpperCamelCase_  (	A :			Optional[Any]							,A :			Tuple			):
					__A             =			TrialShortNamer.shortname_for_key(A							,A			)
					__A             =			short_name
					__A             =			param_name






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict			):
					if cls.NAMING_INFO is not None:
									return

					__A             =			{
					    "short_word": {},
					    "reverse_short_word": {},
					    "short_param": {},
					    "reverse_short_param": {},
					}

					__A             =			list(cls.DEFAULTS.keys()			)

					for k in field_keys:
									cls.add_new_param_name(A							,A			)

					__A             =			info






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict							,A :			List[str]			):
					cls.build_naming_info()
					assert cls.PREFIX is not None
					__A             =			[copy.copy(cls.PREFIX			)]

					for k, v in params.items():
									if k not in cls.DEFAULTS:
													raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''			)
									if v == cls.DEFAULTS[k]:
													# The default value is not added to the name
													continue

									__A             =			cls.NAMING_INFO["short_param"][k]

									if isinstance(A							,A			):
													__A             =			1 if v else 0

									__A             =			"" if isinstance(A							,(int, float)			) else "-"
									__A             =			f'''{key}{sep}{v}'''
									name.append(A			)

					return "_".join(A			)







	@classmethod
	def     UpperCamelCase_  (	cls :			Tuple							,A :			Tuple			):
					__A             =			repr[len(cls.PREFIX			) + 1 :]
					if repr == "":
									__A             =			[]
					else:
									__A             =			repr.split("_"			)

					__A             =			{}

					for value in values:
									if "-" in value:
													__A      ,							__A             =			value.split("-"			)
									else:
													__A             =			re.sub("[0-9.]"							,""							,A			)
													__A             =			float(re.sub("[^0-9.]"							,""							,A			)			)

									__A             =			cls.NAMING_INFO["reverse_short_param"][p_k]

									__A             =			p_v

					for k in cls.DEFAULTS:
									if k not in parameters:
													__A             =			cls.DEFAULTS[k]

					return parameters
