

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


UpperCamelCase__		 =     logging.get_logger(__name__)

UpperCamelCase__		 =     {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							"speech_to_text"
       __SCREAMING_SNAKE_CASE				    =							["past_key_values"]
       __SCREAMING_SNAKE_CASE				    =							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


       def __init__(		self      , _UpperCamelCase=1_00_00      , _UpperCamelCase=12      , _UpperCamelCase=20_48      , _UpperCamelCase=4      , _UpperCamelCase=6      , _UpperCamelCase=20_48      , _UpperCamelCase=4      , _UpperCamelCase=0.0      , _UpperCamelCase=0.0      , _UpperCamelCase=True      , _UpperCamelCase=True      , _UpperCamelCase="relu"      , _UpperCamelCase=2_56      , _UpperCamelCase=0.1      , _UpperCamelCase=0.0      , _UpperCamelCase=0.0      , _UpperCamelCase=0.02      , _UpperCamelCase=2      , _UpperCamelCase=True      , _UpperCamelCase=1      , _UpperCamelCase=0      , _UpperCamelCase=2      , _UpperCamelCase=60_00      , _UpperCamelCase=10_24      , _UpperCamelCase=2      , _UpperCamelCase=(5, 5)      , _UpperCamelCase=10_24      , _UpperCamelCase=80      , _UpperCamelCase=1      , **_UpperCamelCase      , )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							vocab_size
              __snake_case       	=							d_model
              __snake_case       	=							encoder_ffn_dim
              __snake_case       	=							encoder_layers
              __snake_case       	=							encoder_attention_heads
              __snake_case       	=							decoder_ffn_dim
              __snake_case       	=							decoder_layers
              __snake_case       	=							decoder_attention_heads
              __snake_case       	=							dropout
              __snake_case       	=							attention_dropout
              __snake_case       	=							activation_dropout
              __snake_case       	=							activation_function
              __snake_case       	=							init_std
              __snake_case       	=							encoder_layerdrop
              __snake_case       	=							decoder_layerdrop
              __snake_case       	=							use_cache
              __snake_case       	=							encoder_layers
              __snake_case       	=							scale_embedding  # scale factor will be sqrt(d_model) if True
              __snake_case       	=							max_source_positions
              __snake_case       	=							max_target_positions
              __snake_case       	=							num_conv_layers
              __snake_case       	=							list(_UpperCamelCase   )
              __snake_case       	=							conv_channels
              __snake_case       	=							input_feat_per_channel
              __snake_case       	=							input_channels

              if len(self.conv_kernel_sizes   ) != self.num_conv_layers:
                     raise ValueError(
                         """Configuration for convolutional module is incorrect. """
                         """It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` """
                         F'but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes   )}`, '
                         F'`config.num_conv_layers = {self.num_conv_layers}`.'   )

              super().__init__(
                  pad_token_id=_UpperCamelCase      , bos_token_id=_UpperCamelCase      , eos_token_id=_UpperCamelCase      , is_encoder_decoder=_UpperCamelCase      , decoder_start_token_id=_UpperCamelCase      , **_UpperCamelCase      , )




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}')

