
"""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


_A			:       Tuple   		=       re.compile(r"""\s+""")


def   __magic_name__	(      __snake_case				:       Any  ) ->				Optional[Any]:
       return {"hash": hashlib.mda(re.sub(__a     , ""     , example["content"]  ).encode("utf-8"  )  ).hexdigest()}


def   __magic_name__	(      __snake_case				:       str  ) ->				Optional[Any]:
       lowercase    :				List[str]       		=							[len(__a  ) for line in example['content'].splitlines()]
       return {"line_mean": np.mean(__a  ), "line_max": max(__a  )}


def   __magic_name__	(      __snake_case				:       str  ) ->				Union[str, Any]:
       lowercase    :				Union[str, Any]       		=							np.mean([c.isalnum() for c in example["content"]]  )
       return {"alpha_frac": alpha_frac}


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Any  ) ->				Union[str, Any]:

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


def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       Union[str, Any]=5  ) ->				Optional[Any]:
       lowercase    :				Optional[int]       		=							['auto-generated', 'autogenerated', 'automatically generated']
       lowercase    :				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   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       Dict=5     , __snake_case				:       Tuple=0.05  ) ->				int:
       lowercase    :				Optional[int]       		=							['unit tests', 'test file', 'configuration file']
       lowercase    :				int       		=							example['content'].splitlines()
       lowercase    :				int       		=							0
       lowercase    :				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
       lowercase    :				int       		=							example['content'].count("\n"  )
       lowercase    :				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   __magic_name__	(      __snake_case				:       Optional[int]  ) ->				Any:
       lowercase    :				List[str]       		=							['def ', 'class ', 'for ', 'while ']
       lowercase    :				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   __magic_name__	(      __snake_case				:       int     , __snake_case				:       Any=4  ) ->				str:
       lowercase    :				List[str]       		=							example['content'].splitlines()
       lowercase    :				Dict       		=							0
       for line in lines:
              counter += line.lower().count("="  )
              if counter > minimum:
                     return {"has_few_assignments": False}
       return {"has_few_assignments": True}


def   __magic_name__	(      __snake_case				:       str  ) ->				Optional[int]:
       lowercase    :				Optional[Any]       		=							tokenizer(example["content"]     , truncation=__a  )['input_ids']
       lowercase    :				Optional[int]       		=							len(example["content"]  ) / len(__a  )
       return {"ratio": ratio}


def   __magic_name__	(      __snake_case				:       str  ) ->				Optional[Any]:
       lowercase    :				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   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       Any     , __snake_case				:       str  ) ->				int:

       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   __magic_name__	(      __snake_case				:       Union[str, Any]  ) ->				int:

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

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

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

# Deduplicate hashes
_A			:       Any   		=       set(ds.unique("""hash"""))
_A			:       Union[str, Any]   		=       len(uniques) / len(ds)
print(F"Fraction of duplicates: {1-frac:.2%}")

# Deduplicate data and apply heuristics
_A			:       Dict   		=       time.time()
_A			:       Dict   		=       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			:       Optional[int]   		=       time.time()
   _A		,     _A			:       Optional[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
_A			:       Optional[int]   		=       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			:       Tuple   		=       output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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







import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


__lowerCAmelCase         =    HUGGINGFACE_HUB_CACHE


__lowerCAmelCase         =    """config.json"""
__lowerCAmelCase         =    """diffusion_pytorch_model.bin"""
__lowerCAmelCase         =    """diffusion_flax_model.msgpack"""
__lowerCAmelCase         =    """model.onnx"""
__lowerCAmelCase         =    """diffusion_pytorch_model.safetensors"""
__lowerCAmelCase         =    """weights.pb"""
__lowerCAmelCase         =    """https://huggingface.co"""
__lowerCAmelCase         =    default_cache_path
__lowerCAmelCase         =    """diffusers_modules"""
__lowerCAmelCase         =    os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
__lowerCAmelCase         =    ["""fp16""", """non-ema"""]
__lowerCAmelCase         =    """.self_attn"""



