






import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCAmelCase						:       int        =					10_00_00_00_00_00
UpperCAmelCase						:       int        =					1_00_00

UpperCAmelCase						:       str        =					os.path.split(__file__)
UpperCAmelCase						:       str        =					os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				datasets.Dataset:
					__A     :						Optional[Any]          =    pa.Table.from_pydict({'col': [0] * chunk_size}  )
					__A     :						Dict          =    pa.concat_tables([table] * (num_examples // chunk_size)  )
					return datasets.Dataset(__lowercase ,						fingerprint='table_100B'  )





@dataclass
class 						_A:


				"""simple docstring"""


				UpperCamelCase   :  List[str]    =     42
				UpperCamelCase   :  Union[str, Any]    =     42
				UpperCamelCase   :  List[Any]    =     42
				UpperCamelCase   :  int    =     42



				def  UpperCAmelCase_		(      self					):
									__A     :						List[Any]          =    np.random.default_rng(self.seed					)
									__A     :						Optional[int]          =    rng.integers(low=self.low					, high=self.high					, size=self.size					).tolist()



				def __iter__(      self					):
									return iter(self._sampled_values					)





				def __len__(      self					):
									return self.size







@get_duration
def 			_SCREAMING_SNAKE_CASE    (       a  )				->				List[Any]:
					__A     :						int          =    dataset[0]







@get_duration
def 			_SCREAMING_SNAKE_CASE    (       a  )				->				List[str]:
					__A     :						Dict          =    dataset[-1]







@get_duration
def 			_SCREAMING_SNAKE_CASE    (       a  )				->				List[str]:
					__A     :						Tuple          =    dataset[range(len(__lowercase  ) // 2 ,						len(__lowercase  ) // 2 + 10_24  )]







@get_duration
def 			_SCREAMING_SNAKE_CASE    (       a  )				->				Tuple:
					__A     :						List[Any]          =    dataset[RandIter(0 ,						len(__lowercase  ) ,						10_24 ,						seed=42  )]







def 			_SCREAMING_SNAKE_CASE    (       )				->				Optional[int]:
					__A     :						int          =    {'num examples': SPEED_TEST_N_EXAMPLES}
					__A     :						Union[str, Any]          =    (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print('generating dataset'  )
					__A     :						Any          =    generate_100B_dataset(num_examples=__lowercase ,						chunk_size=__lowercase  )
					print('Functions'  )
					for func in functions:
										print(func.__name__  )
										__A     :						str          =    func(__lowercase  )

					with open(__lowercase ,						'wb'  ) as f:
										f.write(json.dumps(__lowercase  ).encode('utf-8'  )  )


if __name__ == "__main__":  # useful to run the profiler
				benchmark_table_100B()











import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase						:       Tuple        =					logging.get_logger(__name__)

UpperCAmelCase						:       List[Any]        =					['''UtteranceLevel''']







@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a  )				->				List[str]:
					__A     :						Any          =    torch.load(a ,						map_location='cpu'  )
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""  )

					__A     :						Dict          =    checkpoint['Downstream']

					__A     :						Optional[Any]          =    HubertConfig.from_pretrained(a  )
					__A     :						Tuple          =    HubertForSequenceClassification.from_pretrained(a ,						config=a  )
					__A     :						str          =    WavaVecaFeatureExtractor.from_pretrained(
					    a ,						return_attention_mask=a ,						do_normalize=a  )

					if hf_congfig.use_weighted_layer_sum:
										__A     :						Optional[Any]          =    checkpoint['Featurizer']['weights']

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

					hf_feature_extractor.save_pretrained(a  )
					hf_model.save_pretrained(a  )


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




