

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 (	__a      :			Dict	)			->     Optional[Any]:

					"""simple docstring"""

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



def    __UpperCAmelCase (	__a      :			List[str]	)			->     Dict:

					"""simple docstring"""

					_a	:    List[Any]         =		[len(__A	) for line in example['content'].splitlines()]
					return {"line_mean": np.mean(__A	), "line_max": max(__A	)}



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

					"""simple docstring"""

					_a	:    List[Any]         =		np.mean([c.isalnum() for c in example['''content''']]	)
					return {"alpha_frac": alpha_frac}



def    __UpperCAmelCase (	__a      :			Optional[int]		,__a      :			Optional[int]	)			->     Optional[int]:

					"""simple docstring"""

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



def    __UpperCAmelCase (	__a      :			Optional[Any]		,__a      :			int=5	)			->     Any:

					"""simple docstring"""

					_a	:    Dict         =		['auto-generated', 'autogenerated', 'automatically generated']
					_a	:    Optional[int]         =		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    __UpperCAmelCase (	__a      :			Union[str, Any]		,__a      :			Tuple=5		,__a      :			List[str]=0.05	)			->     List[Any]:

					"""simple docstring"""

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

					"""simple docstring"""

					_a	:    str         =		['def ', 'class ', 'for ', 'while ']
					_a	:    Optional[Any]         =		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 (	__a      :			Any		,__a      :			List[Any]=4	)			->     Optional[Any]:

					"""simple docstring"""

					_a	:    Any         =		example['content'].splitlines()
					_a	:    List[Any]         =		0
					for line in lines:
										counter += line.lower().count('''='''	)
										if counter > minimum:
															return {"has_few_assignments": False}
					return {"has_few_assignments": True}



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

					"""simple docstring"""

					_a	:    Optional[int]         =		tokenizer(example['''content''']		,truncation=__A	)['input_ids']
					_a	:    Union[str, Any]         =		len(example['''content''']	) / len(__A	)
					return {"ratio": ratio}



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

					"""simple docstring"""

					_a	:    List[Any]         =		{}
					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    __UpperCAmelCase (	__a      :			str		,__a      :			str		,__a      :			Union[str, Any]	)			->     Optional[Any]:

					"""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    __UpperCAmelCase (	__a      :			List[str]	)			->     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
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__				=		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}''')







def        SCREAMING_SNAKE_CASE_						(			__A :			list[int]  ,    __A :			str				)	->		list[int]:

       """simple docstring"""

       a_					:		Any							=  int(__A				)

       # Initialize Result
       a_					:		Tuple							=  []

       # Traverse through all denomination
       for denomination in reversed(__A				):
              # Find denominations
              while int(__A				) >= int(__A				):
                     total_value -= int(__A				)
                     answer.append(__A				)  # Append the "answers" array

       return answer


# Driver Code
if __name__ == "__main__":
   UpperCAmelCase_      : Union[str, Any]  					=       []
   UpperCAmelCase_      : Union[str, Any]  					=       '0'

   if (
       input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
       == "y"
   ):
      UpperCAmelCase_      : List[Any]  					=       int(input('Enter the number of denominations you want to add: ').strip())

      for i in range(0, n):
         denominations.append(int(input(F'Denomination {i}: ').strip()))
      UpperCAmelCase_      : str  					=       input('Enter the change you want to make in Indian Currency: ').strip()
   else:
      # All denominations of Indian Currency if user does not enter
      UpperCAmelCase_      : List[Any]  					=       [1, 2, 5, 10, 20, 50, 100, 500, 2000]
      UpperCAmelCase_      : str  					=       input('Enter the change you want to make: ').strip()

   if int(value) == 0 or int(value) < 0:
      print('The total value cannot be zero or negative.')

   else:
      print(F'Following is minimal change for {value}: ')
      UpperCAmelCase_      : Optional[Any]  					=       find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=' ')


