

'''simple docstring'''
import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase  :		str  =		logging.get_logger(__name__)

_UpperCAmelCase  :		str  =		{
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       'data2vec-audio'







 def __init__(       self  , snake_case_=32  , snake_case_=7_68  , snake_case_=12  , snake_case_=12  , snake_case_=30_72  , snake_case_="gelu"  , snake_case_=0.1  , snake_case_=0.1  , snake_case_=0.1  , snake_case_=0.0  , snake_case_=0.1  , snake_case_=0.1  , snake_case_=0.02  , snake_case_=1E-5  , snake_case_="gelu"  , snake_case_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)  , snake_case_=(5, 2, 2, 2, 2, 2, 2)  , snake_case_=(10, 3, 3, 3, 3, 2, 2)  , snake_case_=False  , snake_case_=16  , snake_case_=19  , snake_case_=5  , snake_case_=0.05  , snake_case_=10  , snake_case_=2  , snake_case_=0.0  , snake_case_=10  , snake_case_=0  , snake_case_="sum"  , snake_case_=False  , snake_case_=False  , snake_case_=2_56  , snake_case_=(5_12, 5_12, 5_12, 5_12, 15_00)  , snake_case_=(5, 3, 3, 1, 1)  , snake_case_=(1, 2, 3, 1, 1)  , snake_case_=5_12  , snake_case_=0  , snake_case_=1  , snake_case_=2  , snake_case_=False  , snake_case_=3  , snake_case_=2  , snake_case_=3  , snake_case_=None  , **snake_case_  , ):
   super().__init__(**snake_case_  , pad_token_id=snake_case_  , bos_token_id=snake_case_  , eos_token_id=snake_case_   )
   lowercase									=hidden_size
   lowercase									=feat_extract_activation
   lowercase									=list(snake_case_   )
   lowercase									=list(snake_case_   )
   lowercase									=list(snake_case_   )
   lowercase									=conv_bias
   lowercase									=num_conv_pos_embeddings
   lowercase									=num_conv_pos_embedding_groups
   lowercase									=conv_pos_kernel_size
   lowercase									=len(self.conv_dim   )
   lowercase									=num_hidden_layers
   lowercase									=intermediate_size
   lowercase									=hidden_act
   lowercase									=num_attention_heads
   lowercase									=hidden_dropout
   lowercase									=attention_dropout
   lowercase									=activation_dropout
   lowercase									=feat_proj_dropout
   lowercase									=final_dropout
   lowercase									=layerdrop
   lowercase									=layer_norm_eps
   lowercase									=initializer_range
   lowercase									=vocab_size
   lowercase									=use_weighted_layer_sum

   if (
       (len(self.conv_stride   ) != self.num_feat_extract_layers)
       or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
       or (len(self.conv_dim   ) != self.num_feat_extract_layers)
   ):
     raise ValueError(
         '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
         ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
         f' {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,'
         f' `len(config.conv_kernel) = {len(self.conv_kernel   )}`.'   )

   # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
   lowercase									=mask_time_prob
   lowercase									=mask_time_length
   lowercase									=mask_time_min_masks
   lowercase									=mask_feature_prob
   lowercase									=mask_feature_length
   lowercase									=mask_feature_min_masks

   # ctc loss
   lowercase									=ctc_loss_reduction
   lowercase									=ctc_zero_infinity

   # adapter
   lowercase									=add_adapter
   lowercase									=adapter_kernel_size
   lowercase									=adapter_stride
   lowercase									=num_adapter_layers
   lowercase									=output_hidden_size or hidden_size

   # SequenceClassification-specific parameter. Feel free to ignore for other classes.
   lowercase									=classifier_proj_size

   # XVector-specific parameters. Feel free to ignore for other classes.
   lowercase									=list(snake_case_   )
   lowercase									=list(snake_case_   )
   lowercase									=list(snake_case_   )
   lowercase									=xvector_output_dim







 @property
 def 						_A(       self   ):
   return math.prod(self.conv_stride   )



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


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







def 						lowerCamelCase__      ( __A    :int		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :str	,__A    :int		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :int=5		):


       """simple docstring"""




       __snake_case       	=							["""auto-generated""", """autogenerated""", """automatically generated"""]
       __snake_case       	=							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 						lowerCamelCase__      ( __A    :int	,__A    :int=5	,__A    :str=0.05		):


       """simple docstring"""




       __snake_case       	=							["""unit tests""", """test file""", """configuration file"""]
       __snake_case       	=							example["""content"""].splitlines()
       __snake_case       	=							0
       __snake_case       	=							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
       __snake_case       	=							example["""content"""].count("""\n"""		)
       __snake_case       	=							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 						lowerCamelCase__      ( __A    :Dict		):


       """simple docstring"""




       __snake_case       	=							["""def """, """class """, """for """, """while """]
       __snake_case       	=							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 						lowerCamelCase__      ( __A    :Dict	,__A    :List[str]=4		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       __snake_case       	=							tokenizer(example["""content"""]	,truncation=__A		)["""input_ids"""]
       __snake_case       	=							len(example["""content"""]		) / len(__A		)
       return {"ratio": ratio}







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							{}
       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 						lowerCamelCase__      ( __A    :int	,__A    :str	,__A    :Dict		):


       """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 						lowerCamelCase__      ( __A    :str		):


       """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
UpperCamelCase__		 =     HfArgumentParser(PreprocessingArguments)
UpperCamelCase__		 =     parser.parse_args()
if args.num_workers is None:
    UpperCamelCase__		 =     multiprocessing.cpu_count()
UpperCamelCase__		 =     AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

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

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

