



'''simple docstring'''

import argparse

import torch

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


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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=WavaVecaForSequenceClassification.from_pretrained(__UpperCAmelCase				,							config=__UpperCAmelCase					)
			snake_case:    Optional[Any]      							=downstream_dict['projector.weight']
			snake_case:    Tuple      							=downstream_dict['projector.bias']
			snake_case:    Optional[int]      							=downstream_dict['model.post_net.linear.weight']
			snake_case:    List[Any]      							=downstream_dict['model.post_net.linear.bias']
			return model







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=WavaVecaForAudioFrameClassification.from_pretrained(__UpperCAmelCase				,							config=__UpperCAmelCase					)
			snake_case:    Any      							=downstream_dict['model.linear.weight']
			snake_case:    int      							=downstream_dict['model.linear.bias']
			return model







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    int      							=WavaVecaForXVector.from_pretrained(__UpperCAmelCase				,							config=__UpperCAmelCase					)
			snake_case:    Dict      							=downstream_dict['connector.weight']
			snake_case:    Tuple      							=downstream_dict['connector.bias']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
						snake_case:    Union[str, Any]      							=downstream_dict[
						    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
						]
						snake_case:    Optional[Any]      							=downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

			snake_case:    Any      							=downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
			snake_case:    str      							=downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
			snake_case:    Tuple      							=downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
			snake_case:    int      							=downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
			snake_case:    Tuple      							=downstream_dict['objective.W']
			return model







@torch.no_grad()
def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    Dict      							=torch.load(__UpperCAmelCase				,							map_location='cpu'					)

			snake_case:    List[Any]      							=checkpoint['Downstream']

			snake_case:    List[str]      							=WavaVecaConfig.from_pretrained(__UpperCAmelCase					)
			snake_case:    Any      							=WavaVecaFeatureExtractor.from_pretrained(
			    __UpperCAmelCase				,							return_attention_mask=__UpperCAmelCase				,							do_normalize=__UpperCAmelCase					)

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

			if hf_config.use_weighted_layer_sum:
						snake_case:    Optional[Any]      							=checkpoint['Featurizer']['weights']

			hf_feature_extractor.save_pretrained(__UpperCAmelCase					)
			hf_model.save_pretrained(__UpperCAmelCase					)


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)









'''simple docstring'''

from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class 						a_		(     snake_case    ):


							def __lt__(				self   :						List[Any]      ,						a_   :						Optional[int]			)			->					List[str]:
										return self[-1] < other[-1]




							def __eq__(				self   :						int      ,						a_   :						Union[str, Any]			)			->					List[str]:
										return self[-1] == other[-1]







def a_     (    __UpperCAmelCase					)				->					list:





			"""simple docstring"""







			snake_case:    list[Stack]      							=[]
			# sort into stacks
			for element in collection:
						snake_case:    int      							=Stack([element]					)
						snake_case:    Union[str, Any]      							=bisect_left(__UpperCAmelCase				,							__UpperCAmelCase					)
						if i != len(__UpperCAmelCase					):
									stacks[i].append(__UpperCAmelCase					)
						else:
									stacks.append(__UpperCAmelCase					)

    # use a heap-based merge to merge stack efficiently
			snake_case:    int      							=merge(*(reversed(__UpperCAmelCase					) for stack in stacks)					)
			return collection


if __name__ == "__main__":
	a							   =				input('Enter numbers separated by a comma:\n').strip()
	a							   =				[int(item) for item in user_input.split(',')]
	print(patience_sort(unsorted))




