




"""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 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Tuple		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

  _snake_case							:		str								=  WavaVecaForSequenceClassification.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		List[Any]								=  downstream_dict["""projector.weight"""]
  _snake_case							:		Optional[Any]								=  downstream_dict["""projector.bias"""]
  _snake_case							:		int								=  downstream_dict["""model.post_net.linear.weight"""]
  _snake_case							:		List[str]								=  downstream_dict["""model.post_net.linear.bias"""]
  return model


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict		,   snake_case__							:					Tuple    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  WavaVecaForAudioFrameClassification.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		Optional[int]								=  downstream_dict["""model.linear.weight"""]
  _snake_case							:		Dict								=  downstream_dict["""model.linear.bias"""]
  return model


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					List[Any]		,   snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  WavaVecaForXVector.from_pretrained(snake_case__		,   config=snake_case__    )
  _snake_case							:		Optional[Any]								=  downstream_dict["""connector.weight"""]
  _snake_case							:		Optional[int]								=  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
    _snake_case							:		Tuple								=  downstream_dict[
        F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
    ]
    _snake_case							:		str								=  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

  _snake_case							:		Any								=  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  _snake_case							:		Union[str, Any]								=  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  _snake_case							:		List[Any]								=  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  _snake_case							:		Dict								=  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  _snake_case							:		List[str]								=  downstream_dict["""objective.W"""]
  return model


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict		,   snake_case__							:					Dict		,   snake_case__							:					Dict    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  torch.load(snake_case__		,   map_location="""cpu"""    )

  _snake_case							:		Union[str, Any]								=  checkpoint["""Downstream"""]

  _snake_case							:		Optional[Any]								=  WavaVecaConfig.from_pretrained(snake_case__    )
  _snake_case							:		Optional[int]								=  WavaVecaFeatureExtractor.from_pretrained(
      snake_case__		,   return_attention_mask=snake_case__		,   do_normalize=snake_case__    )

  _snake_case							:		Optional[int]								=  hf_config.architectures[0]
  if arch.endswith("""ForSequenceClassification"""    ):
    _snake_case							:		Optional[Any]								=  convert_classification(snake_case__		,   snake_case__		,   snake_case__    )
  elif arch.endswith("""ForAudioFrameClassification"""    ):
    _snake_case							:		Dict								=  convert_diarization(snake_case__		,   snake_case__		,   snake_case__    )
  elif arch.endswith("""ForXVector"""    ):
    _snake_case							:		List[str]								=  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:
    _snake_case							:		Optional[int]								=  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)








"""simple docstring"""


import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


A_						=				re.compile(r'''\s+''')


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

  return {"hash": hashlib.mda(re.sub(snake_case__		,   """"""		,   example["""content"""]    ).encode("""utf-8"""    )    ).hexdigest()}


def 					UpperCAmelCase__       (snake_case__							:					Dict    ):
  """simple docstring"""

  _snake_case							:		Any								=  [len(snake_case__    ) for line in example["""content"""].splitlines()]
  return {"line_mean": np.mean(snake_case__    ), "line_max": max(snake_case__    )}


def 					UpperCAmelCase__       (snake_case__							:					List[Any]    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  np.mean([c.isalnum() for c in example["""content"""]]    )
  return {"alpha_frac": alpha_frac}


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

  if example["hash"] in uniques:
    uniques.remove(example["""hash"""]    )
    return True
  else:
    return False


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					List[str]=5    ):
  """simple docstring"""

  _snake_case							:		Any								=  ["""auto-generated""", """autogenerated""", """automatically generated"""]
  _snake_case							:		Tuple								=  example["""content"""].splitlines()
  for _, line in zip(range(snake_case__    )		,   snake_case__    ):
    for keyword in keywords:
      if keyword in line.lower():
        return {"autogenerated": True}
  else:
    return {"autogenerated": False}


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Union[str, Any]=5		,   snake_case__							:					Any=0.05    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  ["""unit tests""", """test file""", """configuration file"""]
  _snake_case							:		List[Any]								=  example["""content"""].splitlines()
  _snake_case							:		Dict								=  0
  _snake_case							:		str								=  0
  # first test
  for _, line in zip(range(snake_case__    )		,   snake_case__    ):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  _snake_case							:		Optional[int]								=  example["""content"""].count("""\n"""    )
  _snake_case							:		Tuple								=  int(coeff * nlines    )
  for line in lines:
    count_config += line.lower().count("""config"""    )
    count_test += line.lower().count("""test"""    )
    if count_config > threshold or count_test > threshold:
      return {"config_or_test": True}
  return {"config_or_test": False}


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  ["""def """, """class """, """for """, """while """]
  _snake_case							:		str								=  example["""content"""].splitlines()
  for line in lines:
    for keyword in keywords:
      if keyword in line.lower():
        return {"has_no_keywords": False}
  return {"has_no_keywords": True}


def 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					List[str]=4    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  example["""content"""].splitlines()
  _snake_case							:		str								=  0
  for line in lines:
    counter += line.lower().count("""="""    )
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}


def 					UpperCAmelCase__       (snake_case__							:					List[str]    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  tokenizer(example["""content"""]		,   truncation=snake_case__    )["""input_ids"""]
  _snake_case							:		Optional[Any]								=  len(example["""content"""]    ) / len(snake_case__    )
  return {"ratio": ratio}


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  {}
  results.update(get_hash(snake_case__    )    )
  results.update(line_stats(snake_case__    )    )
  results.update(alpha_stats(snake_case__    )    )
  results.update(char_token_ratio(snake_case__    )    )
  results.update(is_autogenerated(snake_case__    )    )
  results.update(is_config_or_test(snake_case__    )    )
  results.update(has_no_keywords(snake_case__    )    )
  results.update(has_few_assignments(snake_case__    )    )
  return results


def 					UpperCAmelCase__       (snake_case__							:					Tuple		,   snake_case__							:					List[Any]		,   snake_case__							:					List[str]    ):
  """simple docstring"""

  if not check_uniques(snake_case__		,   snake_case__    ):
    return False
  elif example["autogenerated"]:
    return False
  elif example["line_max"] > args.line_max:
    return False
  elif example["line_mean"] > args.line_mean:
    return False
  elif example["alpha_frac"] < args.alpha_frac:
    return False
  elif example["ratio"] < args.min_token_ratio:
    return False
  elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_few_assignments"]:
    return False
  else:
    return True


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  with open(snake_case__		,   """rb"""    ) as f_in:
    with gzip.open(str(snake_case__    ) + """.gz"""		,   """wb"""		,   compresslevel=6    ) as f_out:
      shutil.copyfileobj(snake_case__		,   snake_case__    )
  os.unlink(snake_case__    )


# Settings
A_						=				HfArgumentParser(PreprocessingArguments)
A_						=				parser.parse_args()
if args.num_workers is None:
     A_						=				multiprocessing.cpu_count()
A_						=				AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
A_						=				time.time()
A_						=				load_dataset(args.dataset_name, split='''train''')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
A_						=				time.time()
A_						=				ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
A_						=				set(ds.unique('''hash'''))
A_						=				len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
A_						=				time.time()
A_						=				ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
     A_						=				time.time()
     A_	,    A_						=				deduplicate_dataset(ds_filter, args.jaccard_threshold)
     print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
     print(F'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
A_						=				Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
     with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
          json.dump(duplicate_clusters, f)

A_						=				output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

A_						=				time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
     A_						=				str(data_dir / F'''file-{file_number+1:012}.json''')
     A_						=				min(len(ds_filter), index + args.samples_per_file)
     ds_filter.select(list(range(index, end_index))).to_json(file_path)
     compress_file(file_path)






print(F'''Time to save dataset: {time.time()-t_start:.2f}''')


