

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 a(lowercase__	):





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





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= [len(lowercase__	) for line in example['content'].splitlines()]
	return {"line_mean": np.mean(lowercase__	), "line_max": max(lowercase__	)}





def a(lowercase__	):





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





def a(lowercase__			, lowercase__	):





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





def a(lowercase__			, lowercase__=5	):





	'''simple docstring'''
	snake_case_      		= ['auto-generated', 'autogenerated', 'automatically generated']
	snake_case_      		= example['content'].splitlines()
	for _, line in zip(range(lowercase__	)			, lowercase__	):
		for keyword in keywords:
			if keyword in line.lower():
				return {"autogenerated": True}
	else:
		return {"autogenerated": False}





def a(lowercase__			, lowercase__=5			, lowercase__=0.05	):





	'''simple docstring'''
	snake_case_      		= ['unit tests', 'test file', 'configuration file']
	snake_case_      		= example['content'].splitlines()
	snake_case_      		= 0
	snake_case_      		= 0
	# first test
	for _, line in zip(range(lowercase__	)			, lowercase__	):
		for keyword in keywords:
			if keyword in line.lower():
				return {"config_or_test": True}
    # second test
	snake_case_      		= example['content'].count('\n'	)
	snake_case_      		= 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 a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= ['def ', 'class ', 'for ', 'while ']
	snake_case_      		= 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 a(lowercase__			, lowercase__=4	):





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





def a(lowercase__	):





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





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= {}
	results.update(get_hash(lowercase__	)	)
	results.update(line_stats(lowercase__	)	)
	results.update(alpha_stats(lowercase__	)	)
	results.update(char_token_ratio(lowercase__	)	)
	results.update(is_autogenerated(lowercase__	)	)
	results.update(is_config_or_test(lowercase__	)	)
	results.update(has_no_keywords(lowercase__	)	)
	results.update(has_few_assignments(lowercase__	)	)
	return results





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	if not check_uniques(lowercase__			, lowercase__	):
		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 a(lowercase__	):





	'''simple docstring'''
	with open(lowercase__			, 'rb'	) as f_in:
		with gzip.open(str(lowercase__	) + '.gz'			, 'wb'			, compresslevel=6	) as f_out:
			shutil.copyfileobj(lowercase__			, lowercase__	)
	os.unlink(lowercase__	)


# 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}""")



class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= name
				snake_case_      		= val


			def __str__(    self							):

				"""simple docstring"""


				return f"""{self.__class__.__name__}({self.name}, {self.val})"""







			def __lt__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.val < other.val







class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= {}
				snake_case_      		= {}
				snake_case_      		= self.build_heap(__UpperCamelCase							)


			def __getitem__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.get_value(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return (idx - 1) // 2


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return idx * 2 + 1


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return idx * 2 + 2


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.heap_dict[key]


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= len(__UpperCamelCase							) - 1
				snake_case_      		= self.get_parent_idx(__UpperCamelCase							)

				for idx, i in enumerate(__UpperCamelCase							):
					snake_case_      		= idx
					snake_case_      		= i.val

				for i in range(__UpperCamelCase ,				-1 ,				-1							):
					self.sift_down(__UpperCamelCase ,				__UpperCamelCase							)
				return array


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				while True:
					snake_case_      		= self.get_left_child_idx(__UpperCamelCase							)  # noqa: E741
					snake_case_      		= self.get_right_child_idx(__UpperCamelCase							)

					snake_case_      		= idx
					if l < len(__UpperCamelCase							) and array[l] < array[idx]:
						snake_case_      		= l
					if r < len(__UpperCamelCase							) and array[r] < array[smallest]:
						snake_case_      		= r

					if smallest != idx:
						snake_case_		,       snake_case_      		= array[smallest], array[idx]
						(
						    (
						    snake_case_
						)		,       (
						    snake_case_
						)		,       
						)      		= (
						    self.idx_of_element[array[smallest]],
						    self.idx_of_element[array[idx]],
						)
						snake_case_      		= smallest
					else:
						break


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.get_parent_idx(__UpperCamelCase							)
				while p >= 0 and self.heap[p] > self.heap[idx]:
					snake_case_		,       snake_case_      		= self.heap[idx], self.heap[p]
					snake_case_		,       snake_case_      		= (
					    self.idx_of_element[self.heap[idx]],
					    self.idx_of_element[self.heap[p]],
					)
					snake_case_      		= p
					snake_case_      		= self.get_parent_idx(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return self.heap[0]


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.heap[-1], self.heap[0]
				snake_case_		,       snake_case_      		= (
				    self.idx_of_element[self.heap[-1]],
				    self.idx_of_element[self.heap[0]],
				)

				snake_case_      		= self.heap.pop()
				del self.idx_of_element[x]
				self.sift_down(0 ,				self.heap							)
				return x


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				self.heap.append(__UpperCamelCase							)
				snake_case_      		= len(self.heap							) - 1
				snake_case_      		= node.val
				self.sift_up(len(self.heap							) - 1							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return len(self.heap							) == 0







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				assert (
				    self.heap[self.idx_of_element[node]].val > new_value
				), "newValue must be less that current value"
				snake_case_      		= new_value
				snake_case_      		= new_value
				self.sift_up(self.idx_of_element[node]							)







A											=     Node('R', -1)
A											=     Node('B', 6)
A											=     Node('A', 3)
A											=     Node('X', 1)
A											=     Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
A											=     MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
		print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
		print(i)

if __name__ == "__main__":
		import doctest

		doctest.testmod()
