






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      :       Union[str, Any]    =						re.compile(R'''\s+''')





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





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





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





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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case=5							):
     __lowerCAmelCase             =       ['auto-generated', 'autogenerated', 'automatically generated']
     __lowerCAmelCase             =       example['content'].splitlines()
     for _, line in zip(range(UpperCamelCase__							)       ,			UpperCamelCase__							):
          for keyword in keywords:
               if keyword in line.lower():
                    return {"autogenerated": True}
     else:
          return {"autogenerated": False}





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case=5       ,			__snake_case=0.05							):
     __lowerCAmelCase             =       ['unit tests', 'test file', 'configuration file']
     __lowerCAmelCase             =       example['content'].splitlines()
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       0
     # first test
     for _, line in zip(range(UpperCamelCase__							)       ,			UpperCamelCase__							):
          for keyword in keywords:
               if keyword in line.lower():
                    return {"config_or_test": True}
                    # second test
     __lowerCAmelCase             =       example['content'].count("\n"							)
     __lowerCAmelCase             =       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   (    __snake_case							):
     __lowerCAmelCase             =       ['def ', 'class ', 'for ', 'while ']
     __lowerCAmelCase             =       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   (    __snake_case       ,			__snake_case=4							):
     __lowerCAmelCase             =       example['content'].splitlines()
     __lowerCAmelCase             =       0
     for line in lines:
          counter += line.lower().count("="							)
          if counter > minimum:
               return {"has_few_assignments": False}
     return {"has_few_assignments": True}





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





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       {}
     results.update(get_hash(UpperCamelCase__							)							)
     results.update(line_stats(UpperCamelCase__							)							)
     results.update(alpha_stats(UpperCamelCase__							)							)
     results.update(char_token_ratio(UpperCamelCase__							)							)
     results.update(is_autogenerated(UpperCamelCase__							)							)
     results.update(is_config_or_test(UpperCamelCase__							)							)
     results.update(has_no_keywords(UpperCamelCase__							)							)
     results.update(has_few_assignments(UpperCamelCase__							)							)
     return results





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     if not check_uniques(UpperCamelCase__       ,			UpperCamelCase__							):
          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   (    __snake_case							):
     with open(UpperCamelCase__       ,			"rb"							) as f_in:
          with gzip.open(str(UpperCamelCase__							) + ".gz"       ,			"wb"       ,			compresslevel=6							) as f_out:
               shutil.copyfileobj(UpperCamelCase__       ,			UpperCamelCase__							)
     os.unlink(UpperCamelCase__							)


# Settings
lowerCamelCase      :       str    =						HfArgumentParser(PreprocessingArguments)
lowerCamelCase      :       Any    =						parser.parse_args()
if args.num_workers is None:
 lowerCamelCase      :       List[str]    =						multiprocessing.cpu_count()
lowerCamelCase      :       int    =						AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
lowerCamelCase      :       List[str]    =						time.time()
lowerCamelCase      :       Union[str, Any]    =						load_dataset(args.dataset_name, split='''train''')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

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

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

# Deduplicate data and apply heuristics
lowerCamelCase      :       Tuple    =						time.time()
lowerCamelCase      :       Union[str, Any]    =						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      :       str    =						time.time()
 lowerCamelCase      :       Any    =						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      :       List[Any]    =						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      :       List[str]    =						output_dir / 'data'
data_dir.mkdir(exist_ok=True)

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










'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def       _lowercase							(					):
 __A	:		Dict	  =      ArgumentParser('Accelerate CLI tool',					usage='accelerate <command> [<args>]',					allow_abbrev=UpperCamelCase__      )
 __A	:		Any	  =      parser.add_subparsers(help='accelerate command helpers'      )

 # Register commands
 get_config_parser(subparsers=UpperCamelCase__      )
 env_command_parser(subparsers=UpperCamelCase__      )
 launch_command_parser(subparsers=UpperCamelCase__      )
 tpu_command_parser(subparsers=UpperCamelCase__      )
 test_command_parser(subparsers=UpperCamelCase__      )

 # Let's go
 __A	:		Optional[Any]	  =      parser.parse_args()

 if not hasattr(UpperCamelCase__,					'func'      ):
  parser.print_help()
  exit(1      )

 # Run
 args.func(UpperCamelCase__      )


if __name__ == "__main__":
 main()






