


import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
lowerCamelCase						=		logging.get_logger('''transformers.models.encodec''')

lowerCamelCase						=		{
    '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
    '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
    '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
    '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
lowerCamelCase						=		{
    '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
    '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
    '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
    '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
    '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
    '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
    '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
    '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
    '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
    '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
    '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
    '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
    '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
    '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
    '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
    '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
    '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
    '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
    '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
lowerCamelCase						=		{
    '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
    '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
    '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
    '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
    '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
    '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
    '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
    '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
    '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
    '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
    '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
    '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
    '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
    '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
    '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
    '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
    '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
    '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
lowerCamelCase						=		{
    '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
    '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
    '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
    '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
    '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
    '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
    '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
    '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
    '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
    '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
    '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
    '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
    '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
    '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
    '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
    '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
    '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
    '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
    '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
lowerCamelCase						=		{
    '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
    '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
    '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
    '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
    '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
    '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
    '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
    '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
    '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
    '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
    '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
    '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
    '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
    '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
    '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
    '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
    '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
    '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
lowerCamelCase						=		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowerCamelCase						=		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowerCamelCase						=		[]
lowerCamelCase						=		[]






def      lowerCamelCase_    (       _a	,   _a	,   _a	,   _a	,   _a       ):




		"""simple docstring"""






		for attribute in key.split('''.'''       ):
				lowerCAmelCase__   :			List[Any]         =					getattr(__a	,   __a       )

		if weight_type is not None:
				lowerCAmelCase__   :			List[Any]         =					getattr(__a	,   __a       ).shape
		else:
				lowerCAmelCase__   :			List[str]         =					hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
				    f' {value.shape} for {full_name}'       )

		if weight_type == "weight":
				lowerCAmelCase__   :			Tuple         =					value
		elif weight_type == "weight_g":
				lowerCAmelCase__   :			Dict         =					value
		elif weight_type == "weight_v":
				lowerCAmelCase__   :			str         =					value
		elif weight_type == "bias":
				lowerCAmelCase__   :			Dict         =					value
		elif weight_type == "running_mean":
				lowerCAmelCase__   :			str         =					value
		elif weight_type == "running_var":
				lowerCAmelCase__   :			str         =					value
		elif weight_type == "num_batches_tracked":
				lowerCAmelCase__   :			Optional[Any]         =					value
		elif weight_type == "weight_ih_l0":
				lowerCAmelCase__   :			List[Any]         =					value
		elif weight_type == "weight_hh_l0":
				lowerCAmelCase__   :			int         =					value
		elif weight_type == "bias_ih_l0":
				lowerCAmelCase__   :			Optional[int]         =					value
		elif weight_type == "bias_hh_l0":
				lowerCAmelCase__   :			Dict         =					value
		elif weight_type == "weight_ih_l1":
				lowerCAmelCase__   :			str         =					value
		elif weight_type == "weight_hh_l1":
				lowerCAmelCase__   :			Dict         =					value
		elif weight_type == "bias_ih_l1":
				lowerCAmelCase__   :			Union[str, Any]         =					value
		elif weight_type == "bias_hh_l1":
				lowerCAmelCase__   :			Tuple         =					value
		else:
				lowerCAmelCase__   :			Dict         =					value

		logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'       )






def      lowerCamelCase_    (       _a	,   _a       ):




		"""simple docstring"""






		for key in ignore_keys:
				if key.endswith('''.*'''       ):
						if name.startswith(key[:-1]       ):
								return True
				elif ".*." in key:
						lowerCAmelCase__   :			int         =					key.split('''.*.'''       )
						if prefix in name and suffix in name:
								return True
				elif key in name:
						return True
		return False






def      lowerCamelCase_    (       _a	,   _a	,   _a       ):




		"""simple docstring"""






		lowerCAmelCase__   :			Optional[int]         =					[]

		if model_name == "encodec_24khz" or "encodec_32khz":
				lowerCAmelCase__   :			Tuple         =					MAPPING_24K
		elif model_name == "encodec_48khz":
				lowerCAmelCase__   :			Any         =					MAPPING_48K
		else:
				raise ValueError(f'Unsupported model: {model_name}'       )

		for name, value in orig_dict.items():
				if should_ignore(__a	,   __a       ):
						logger.info(f'{name} was ignored'       )
						continue

				lowerCAmelCase__   :			Tuple         =					False
				for key, mapped_key in MAPPING.items():
						if "*" in key:
								lowerCAmelCase__   :			Union[str, Any]         =					key.split('''.*.'''       )
								if prefix in name and suffix in name:
										lowerCAmelCase__   :			Union[str, Any]         =					suffix

						if key in name:
								# HACK otherwise .embed gets initialized with .embed_avg too
								if key.endswith('''embed'''       ) and name.endswith('''embed_avg'''       ):
										continue

								lowerCAmelCase__   :			int         =					True
								if "*" in mapped_key:
										lowerCAmelCase__   :			int         =					name.split(__a       )[0].split('''.'''       )[-2]
										lowerCAmelCase__   :			Dict         =					mapped_key.replace('''*'''	,   __a       )
								if "weight_g" in name:
										lowerCAmelCase__   :			List[Any]         =					'weight_g'
								elif "weight_v" in name:
										lowerCAmelCase__   :			List[str]         =					'weight_v'
								elif "weight_ih_l0" in name:
										lowerCAmelCase__   :			Tuple         =					'weight_ih_l0'
								elif "weight_hh_l0" in name:
										lowerCAmelCase__   :			Optional[Any]         =					'weight_hh_l0'
								elif "bias_ih_l0" in name:
										lowerCAmelCase__   :			List[str]         =					'bias_ih_l0'
								elif "bias_hh_l0" in name:
										lowerCAmelCase__   :			Union[str, Any]         =					'bias_hh_l0'
								elif "weight_ih_l1" in name:
										lowerCAmelCase__   :			Dict         =					'weight_ih_l1'
								elif "weight_hh_l1" in name:
										lowerCAmelCase__   :			Optional[int]         =					'weight_hh_l1'
								elif "bias_ih_l1" in name:
										lowerCAmelCase__   :			Optional[Any]         =					'bias_ih_l1'
								elif "bias_hh_l1" in name:
										lowerCAmelCase__   :			Dict         =					'bias_hh_l1'
								elif "bias" in name:
										lowerCAmelCase__   :			List[Any]         =					'bias'
								elif "weight" in name:
										lowerCAmelCase__   :			Dict         =					'weight'
								elif "running_mean" in name:
										lowerCAmelCase__   :			Optional[Any]         =					'running_mean'
								elif "running_var" in name:
										lowerCAmelCase__   :			List[Any]         =					'running_var'
								elif "num_batches_tracked" in name:
										lowerCAmelCase__   :			Tuple         =					'num_batches_tracked'
								else:
										lowerCAmelCase__   :			List[str]         =					None
								set_recursively(__a	,   __a	,   __a	,   __a	,   __a       )
						continue
				if not is_used:
						unused_weights.append(__a       )

		logger.warning(f'Unused weights: {unused_weights}'       )






@torch.no_grad()
def      lowerCamelCase_    (       _a	,   _a	,   _a	,   _a=None	,   _a=None	,   ):




		"""simple docstring"""






		if config_path is not None:
				lowerCAmelCase__   :			str         =					EncodecConfig.from_pretrained(__a       )
		else:
				lowerCAmelCase__   :			Optional[int]         =					EncodecConfig()

		if model_name == "encodec_24khz":
				pass  # config is already correct
		elif model_name == "encodec_32khz":
				lowerCAmelCase__   :			str         =					[8, 5, 4, 4]
				lowerCAmelCase__   :			Tuple         =					[2.2]
				lowerCAmelCase__   :			Tuple         =					64
				lowerCAmelCase__   :			Optional[Any]         =					32_000
				lowerCAmelCase__   :			List[str]         =					2_048
				lowerCAmelCase__   :			Optional[int]         =					False
				lowerCAmelCase__   :			Optional[int]         =					False
				lowerCAmelCase__   :			Dict         =					False
		elif model_name == "encodec_48khz":
				lowerCAmelCase__   :			int         =					[8, 5, 4, 2]
				lowerCAmelCase__   :			Dict         =					[3.0, 6.0, 12.0, 24.0]
				lowerCAmelCase__   :			Union[str, Any]         =					48_000
				lowerCAmelCase__   :			Union[str, Any]         =					2
				lowerCAmelCase__   :			Tuple         =					False
				lowerCAmelCase__   :			str         =					'time_group_norm'
				lowerCAmelCase__   :			Any         =					True
				lowerCAmelCase__   :			Tuple         =					1.0
				lowerCAmelCase__   :			Tuple         =					0.01
		else:
				raise ValueError(f'Unknown model name: {model_name}'       )

		lowerCAmelCase__   :			Union[str, Any]         =					EncodecModel(__a       )

		lowerCAmelCase__   :			Tuple         =					EncodecFeatureExtractor(
		    feature_size=config.audio_channels	,   sampling_rate=config.sampling_rate	,   chunk_length_s=config.chunk_length_s	,   overlap=config.overlap	,   )
		feature_extractor.save_pretrained(__a       )

		lowerCAmelCase__   :			Any         =					torch.load(__a       )
		if "best_state" in original_checkpoint:
				# we might have a training state saved, in which case discard the yaml results and just retain the weights
				lowerCAmelCase__   :			List[Any]         =					original_checkpoint['best_state']
		recursively_load_weights(__a	,   __a	,   __a       )
		model.save_pretrained(__a       )

		if repo_id:
				print('''Pushing to the hub...'''       )
				feature_extractor.push_to_hub(__a       )
				model.push_to_hub(__a       )


if __name__ == "__main__":
		lowerCamelCase						=		argparse.ArgumentParser()
		parser.add_argument(
		    '''--model''',
		    default='''encodec_24khz''',
		    type=str,
		    help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
		)
		parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
		)

		lowerCamelCase						=		parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)






'''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


__lowerCAmelCase         =    re.compile(r"""\s+""")





def 					UpperCAmelCase_		(__a      :    Any ):






   """simple docstring"""

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





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	List[str]			=   [len(__a ) for line in example['content'].splitlines()]
   return {"line_mean": np.mean(__a ), "line_max": max(__a )}





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	Union[str, Any]			=   np.mean([c.isalnum() for c in example['content']] )
   return {"alpha_frac": alpha_frac}





def 					UpperCAmelCase_		(__a      :    Optional[int] ,	__a      :    Any ):






   """simple docstring"""

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





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Union[str, Any]=5 ):






   """simple docstring"""

   _a				:	Optional[int]			=   ['auto-generated', 'autogenerated', 'automatically generated']
   _a				:	List[str]			=   example['content'].splitlines()
   for _, line in zip(range(__a ) ,	__a ):
      for keyword in keywords:
         if keyword in line.lower():
            return {"autogenerated": True}
   else:
      return {"autogenerated": False}





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    Dict=5 ,	__a      :    Tuple=0.05 ):






   """simple docstring"""

   _a				:	Optional[int]			=   ['unit tests', 'test file', 'configuration file']
   _a				:	int			=   example['content'].splitlines()
   _a				:	int			=   0
   _a				:	Dict			=   0
   # first test
   for _, line in zip(range(__a ) ,	__a ):
      for keyword in keywords:
         if keyword in line.lower():
            return {"config_or_test": True}
    # second test
   _a				:	int			=   example['content'].count('\n' )
   _a				:	int			=   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_		(__a      :    Optional[int] ):






   """simple docstring"""

   _a				:	List[str]			=   ['def ', 'class ', 'for ', 'while ']
   _a				:	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_		(__a      :    int ,	__a      :    Any=4 ):






   """simple docstring"""

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





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	Optional[Any]			=   tokenizer(example['content'] ,	truncation=__a )['input_ids']
   _a				:	Optional[int]			=   len(example['content'] ) / len(__a )
   return {"ratio": ratio}





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	Dict			=   {}
   results.update(get_hash(__a ) )
   results.update(line_stats(__a ) )
   results.update(alpha_stats(__a ) )
   results.update(char_token_ratio(__a ) )
   results.update(is_autogenerated(__a ) )
   results.update(is_config_or_test(__a ) )
   results.update(has_no_keywords(__a ) )
   results.update(has_few_assignments(__a ) )
   return results





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Any ,	__a      :    str ):






   """simple docstring"""

   if not check_uniques(__a ,	__a ):
      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_		(__a      :    Union[str, Any] ):






   """simple docstring"""

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


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

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

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

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

# Deduplicate data and apply heuristics
__lowerCAmelCase         =    time.time()
__lowerCAmelCase         =    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:
  __lowerCAmelCase         =    time.time()
  __lowerCAmelCase   ,				__lowerCAmelCase         =    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
__lowerCAmelCase         =    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)

__lowerCAmelCase         =    output_dir / """data"""
data_dir.mkdir(exist_ok=True)

__lowerCAmelCase         =    time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
  __lowerCAmelCase         =    str(data_dir / f'''file-{file_number+1:012}.json''')
  __lowerCAmelCase         =    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}''')



