






'''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


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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Optional[int]:

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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[Any]    )  ->			str:

   """simple docstring"""
   __a				     =    [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__:	Optional[int]    )  ->			Tuple:

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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any],					SCREAMING_SNAKE_CASE__:	Any    )  ->			str:

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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[Any],					SCREAMING_SNAKE_CASE__:	List[str]=5    )  ->			Dict:

   """simple docstring"""
   __a				     =    ['auto-generated', 'autogenerated', 'automatically generated']
   __a				     =    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__:	str,					SCREAMING_SNAKE_CASE__:	List[str]=5,					SCREAMING_SNAKE_CASE__:	List[str]=0.0_5    )  ->			str:

   """simple docstring"""
   __a				     =    ['unit tests', 'test file', 'configuration file']
   __a				     =    example['content'].splitlines()
   __a				     =    0
   __a				     =    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
   __a				     =    example['content'].count('\n'    )
   __a				     =    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__:	Union[str, Any]    )  ->			List[Any]:

   """simple docstring"""
   __a				     =    ['def ', 'class ', 'for ', 'while ']
   __a				     =    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__:	List[str]=4    )  ->			List[str]:

   """simple docstring"""
   __a				     =    example['content'].splitlines()
   __a				     =    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__:	Any    )  ->			str:

   """simple docstring"""
   __a				     =    tokenizer(example['content'],					truncation=SCREAMING_SNAKE_CASE__    )['input_ids']
   __a				     =    len(example['content']    ) / len(SCREAMING_SNAKE_CASE__    )
   return {"ratio": ratio}



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    {}
   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__:	List[str],					SCREAMING_SNAKE_CASE__:	Any,					SCREAMING_SNAKE_CASE__:	List[str]    )  ->			Optional[int]:

   """simple docstring"""
   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__:	str    )  ->			str:

   """simple docstring"""
   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
__UpperCamelCase      :       Optional[Any]      			=      HfArgumentParser(PreprocessingArguments)
__UpperCamelCase      :       Optional[int]      			=      parser.parse_args()
if args.num_workers is None:
 __UpperCamelCase      :       Optional[Any]      			=      multiprocessing.cpu_count()
__UpperCamelCase      :       int      			=      AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
__UpperCamelCase      :       Tuple      			=      time.time()
__UpperCamelCase      :       Optional[int]      			=      load_dataset(args.dataset_name, split="""train""")
print(f"""Time to load dataset: {time.time()-t_start:.2f}""")

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

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

# Deduplicate data and apply heuristics
__UpperCamelCase      :       List[str]      			=      time.time()
__UpperCamelCase      :       Optional[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:
 __UpperCamelCase      :       Optional[int]      			=      time.time()
 __UpperCamelCase      :       Optional[int]      			=      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      :       List[str]      			=      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      :       str      			=      output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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



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


__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Any      			=      {
    """google/vivit-b-16x2-kinetics400""": (
        """https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vivit"




      def __init__(  self ,   lowerCamelCase=224 ,   lowerCamelCase=32 ,   lowerCamelCase=[2, 16, 16] ,   lowerCamelCase=3 ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu_fast" ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-06 ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    initializer_range
         __a				     =    layer_norm_eps

         __a				     =    image_size
         __a				     =    num_frames
         __a				     =    tubelet_size
         __a				     =    num_channels
         __a				     =    qkv_bias

         super().__init__(**lowerCamelCase      )