


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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     __UpperCamelCase   							=[len(SCREAMING_SNAKE_CASE__    ) for line in example['content'].splitlines()]
     return {"line_mean": np.mean(SCREAMING_SNAKE_CASE__    ), "line_max": max(SCREAMING_SNAKE_CASE__    )}

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : Any    ):
     if example["hash"] in uniques:
          uniques.remove(example['hash']    )
          return True
     else:
          return False

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : List[Any]=5    ):
     __UpperCamelCase   							=['auto-generated', 'autogenerated', 'automatically generated']
     __UpperCamelCase   							=example['content'].splitlines()
     for _, line in zip(range(SCREAMING_SNAKE_CASE__    )      ,					SCREAMING_SNAKE_CASE__    ):
          for keyword in keywords:
               if keyword in line.lower():
                    return {"autogenerated": True}
     else:
          return {"autogenerated": False}

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Tuple=5      ,					SCREAMING_SNAKE_CASE__      : List[Any]=0.05    ):
     __UpperCamelCase   							=['unit tests', 'test file', 'configuration file']
     __UpperCamelCase   							=example['content'].splitlines()
     __UpperCamelCase   							=0
     __UpperCamelCase   							=0
     # first test
     for _, line in zip(range(SCREAMING_SNAKE_CASE__    )      ,					SCREAMING_SNAKE_CASE__    ):
          for keyword in keywords:
               if keyword in line.lower():
                    return {"config_or_test": True}
    # second test
     __UpperCamelCase   							=example['content'].count('\n'    )
     __UpperCamelCase   							=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    (      SCREAMING_SNAKE_CASE__      : Dict    ):
     __UpperCamelCase   							=['def ', 'class ', 'for ', 'while ']
     __UpperCamelCase   							=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    (      SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : int=4    ):
     __UpperCamelCase   							=example['content'].splitlines()
     __UpperCamelCase   							=0
     for line in lines:
          counter += line.lower().count('='    )
          if counter > minimum:
               return {"has_few_assignments": False}
     return {"has_few_assignments": True}

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     __UpperCamelCase   							=tokenizer(example['content']      ,					truncation=SCREAMING_SNAKE_CASE__    )['input_ids']
     __UpperCamelCase   							=len(example['content']    ) / len(SCREAMING_SNAKE_CASE__    )
     return {"ratio": ratio}

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     __UpperCamelCase   							={}
     results.update(get_hash(SCREAMING_SNAKE_CASE__    )    )
     results.update(line_stats(SCREAMING_SNAKE_CASE__    )    )
     results.update(alpha_stats(SCREAMING_SNAKE_CASE__    )    )
     results.update(char_token_ratio(SCREAMING_SNAKE_CASE__    )    )
     results.update(is_autogenerated(SCREAMING_SNAKE_CASE__    )    )
     results.update(is_config_or_test(SCREAMING_SNAKE_CASE__    )    )
     results.update(has_no_keywords(SCREAMING_SNAKE_CASE__    )    )
     results.update(has_few_assignments(SCREAMING_SNAKE_CASE__    )    )
     return results

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : List[str]    ):
     if not check_uniques(SCREAMING_SNAKE_CASE__      ,					SCREAMING_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    (      SCREAMING_SNAKE_CASE__      : Any    ):
     with open(SCREAMING_SNAKE_CASE__      ,					'rb'    ) as f_in:
          with gzip.open(str(SCREAMING_SNAKE_CASE__    ) + '.gz'      ,					'wb'      ,					compresslevel=6    ) as f_out:
               shutil.copyfileobj(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     os.unlink(SCREAMING_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}""")









import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		int              =  0
    UpperCAmelCase__						:		bool              =  False
    UpperCAmelCase__						:		float              =  3.0






class    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    def _a (      self   )  ->	Any:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs() ,       {}   )
         self.assertDictEqual(MockClass(a=2   ).to_kwargs() ,       {'a': 2}   )
         self.assertDictEqual(MockClass(a=2 ,       b=A_   ).to_kwargs() ,       {'a': 2, 'b': True}   )
         self.assertDictEqual(MockClass(a=2 ,       c=2.25   ).to_kwargs() ,       {'a': 2, 'c': 2.25}   )






    @require_cuda
    def _a (      self   )  ->	Union[str, Any]:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         __UpperCamelCase   							=GradScalerKwargs(init_scale=1024 ,       growth_factor=2   )
         AcceleratorState._reset_state()
         __UpperCamelCase   							=Accelerator(mixed_precision='fp16' ,       kwargs_handlers=[scaler_handler]   )
         print(accelerator.use_fpaa   )
         __UpperCamelCase   							=accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale ,       1024.0   )
         self.assertEqual(scaler._growth_factor ,       2.0   )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor ,       0.5   )
         self.assertEqual(scaler._growth_interval ,       2000   )
         self.assertEqual(scaler._enabled ,       A_   )






    @require_multi_gpu
    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							=['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
         execute_subprocess_async(A_ ,       env=os.environ.copy()   )


if __name__ == "__main__":
   _A			=						DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _A			=						Accelerator(kwargs_handlers=[ddp_scaler])
   _A			=						torch.nn.Linear(100, 200)
   _A			=						accelerator.prepare(model)

   # Check the values changed in kwargs
   _A			=						''
   _A			=						model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)





