






'''simple docstring'''




def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    int:



			"""simple docstring"""



			if len(__magic_name__						) != len(__magic_name__						):
						raise ValueError("The length of profit and weight must be same."						)
			if max_weight <= 0:
						raise ValueError("max_weight must greater than zero."						)
			if any(p < 0 for p in profit						):
						raise ValueError("Profit can not be negative."						)
			if any(w < 0 for w in weight						):
						raise ValueError("Weight can not be negative."						)

			# List created to store profit gained for the 1kg in case of each weight
			# respectively.  Calculate and append profit/weight for each element.
			snake_case_				:					Optional[Any]   =       [p / w for p, w in zip(__magic_name__     ,__magic_name__						)]

			# Creating a copy of the list and sorting profit/weight in ascending order
			snake_case_				:					List[Any]   =       sorted(__magic_name__						)

			# declaring useful variables
			snake_case_				:					Optional[int]   =       len(__magic_name__						)
			snake_case_				:					Union[str, Any]   =       0
			snake_case_				:					int   =       0
			snake_case_				:					Union[str, Any]   =       0

			# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
			while limit <= max_weight and i < length:
						# flag value for encountered greatest element in sorted_profit_by_weight
						snake_case_				:					List[Any]   =       sorted_profit_by_weight[length - i - 1]
						snake_case_				:					str   =       profit_by_weight.index(__magic_name__						)
						snake_case_				:					Union[str, Any]   =       -1

						# check if the weight encountered is less than the total weight
						# encountered before.
						if max_weight - limit >= weight[index]:
									limit += weight[index]
									# Adding profit gained for the given weight 1 ===
									# weight[index]/weight[index]
									gain += 1 * profit[index]
						else:
									# Since the weight encountered is greater than limit, therefore take the
									# required number of remaining kgs and calculate profit for it.
									# weight remaining / weight[index]
									gain += (max_weight - limit) / weight[index] * profit[index]
									break
						i += 1
			return gain


if __name__ == "__main__":
		print(
		    '''Input profits, weights, and then max_weight (all positive ints) separated by '''
		    '''spaces.'''
		)

		__lowerCamelCase   :					Any   							=					[int(x) for x in input('''Input profits separated by spaces: ''').split()]
		__lowerCamelCase   :					str   							=					[int(x) for x in input('''Input weights separated by spaces: ''').split()]
		__lowerCamelCase   :					Optional[Any]   							=					int(input('''Max weight allowed: '''))

		# Function Call
		calc_profit(profit, weight, max_weight)












'''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


__lowerCamelCase   :					List[str]   							=					re.compile(R'''\s+''')

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



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       [len(__magic_name__						) for line in example["content"].splitlines()]
			return {"line_mean": np.mean(__magic_name__						), "line_max": max(__magic_name__						)}

def 	__UpperCAmelCase       ( __magic_name__						)->    int:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       np.mean([c.isalnum() for c in example["content"]]						)
			return {"alpha_frac": alpha_frac}

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=5						)->    Tuple:



			"""simple docstring"""



			snake_case_				:					List[str]   =       ["auto-generated", "autogenerated", "automatically generated"]
			snake_case_				:					Optional[Any]   =       example["content"].splitlines()
			for _, line in zip(range(__magic_name__						)     ,__magic_name__						):
						for keyword in keywords:
									if keyword in line.lower():
												return {"autogenerated": True}
			else:
						return {"autogenerated": False}

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=5     ,__magic_name__=0.05						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       ["unit tests", "test file", "configuration file"]
			snake_case_				:					int   =       example["content"].splitlines()
			snake_case_				:					Optional[Any]   =       0
			snake_case_				:					Any   =       0
			# first test
			for _, line in zip(range(__magic_name__						)     ,__magic_name__						):
						for keyword in keywords:
									if keyword in line.lower():
												return {"config_or_test": True}
    # second test
			snake_case_				:					Tuple   =       example["content"].count("\n"						)
			snake_case_				:					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       ( __magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					List[Any]   =       ["def ", "class ", "for ", "while "]
			snake_case_				:					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       ( __magic_name__     ,__magic_name__=4						)->    Optional[int]:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       tokenizer(example["content"]     ,truncation=__magic_name__						)["input_ids"]
			snake_case_				:					int   =       len(example["content"]						) / len(__magic_name__						)
			return {"ratio": ratio}

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       {}
			results.update(get_hash(__magic_name__						)						)
			results.update(line_stats(__magic_name__						)						)
			results.update(alpha_stats(__magic_name__						)						)
			results.update(char_token_ratio(__magic_name__						)						)
			results.update(is_autogenerated(__magic_name__						)						)
			results.update(is_config_or_test(__magic_name__						)						)
			results.update(has_no_keywords(__magic_name__						)						)
			results.update(has_few_assignments(__magic_name__						)						)
			return results

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



			if not check_uniques(__magic_name__     ,__magic_name__						):
						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       ( __magic_name__						)->    Dict:



			"""simple docstring"""



			with open(__magic_name__     ,"rb"						) as f_in:
						with gzip.open(str(__magic_name__						) + ".gz"     ,"wb"     ,compresslevel=6						) as f_out:
									shutil.copyfileobj(__magic_name__     ,__magic_name__						)
			os.unlink(__magic_name__						)


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

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

# Run preprocessing
__lowerCamelCase   :					List[str]   							=					time.time()
__lowerCamelCase   :					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   :					Optional[int]   							=					len(uniques) / len(ds)
print(f'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
__lowerCamelCase   :					List[str]   							=					time.time()
__lowerCamelCase   :					Tuple   							=					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   :					List[str]   							=					time.time()
		__lowerCamelCase							,      __lowerCamelCase   :					Tuple   							=					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   :					int   							=					time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
		__lowerCamelCase   :					Union[str, Any]   							=					str(data_dir / f'''file-{file_number+1:012}.json''')
		__lowerCamelCase   :					List[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}''')




