
'''simple docstring'''
import argparse

import torch

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


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






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	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__						):
	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__						):
	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__						):
	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__":
		lowercase__											=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.')
		lowercase__											=parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowercase__											=subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
lowercase__											=(
    subprocess.check_output(F"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode('utf-8').split()
)

lowercase__											='|'.join(sys.argv[1:])
lowercase__											=re.compile(rF"""^({joined_dirs}).*?\.py$""")

lowercase__											=[x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
