

import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate



def       __lowerCAmelCase      (						A    ):
	return x + 2




class 						__UpperCamelCase    (					unittest.TestCase						):




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"x = 3"
		UpperCAmelCase_       				=			{}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{}		,	state=lowerCAmelCase							)
		assert result == 3
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3}							)

		UpperCAmelCase_       				=			"x = y"
		UpperCAmelCase_       				=			{"y": 5}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{}		,	state=lowerCAmelCase							)
		# evaluate returns the value of the last assignment.
		assert result == 5
		self.assertDictEqual(lowerCAmelCase		,	{"x": 5, "y": 5}							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"y = add_two(x)"
		UpperCAmelCase_       				=			{"x": 3}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{"add_two": add_two}		,	state=lowerCAmelCase							)
		assert result == 5
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3, "y": 5}							)

		# Won't work without the tool
		with CaptureStdout() as out:
			UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{}		,	state=lowerCAmelCase							)
		assert result is None
		assert "tried to execute add_two" in out.out




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"x = 3"
		UpperCAmelCase_       				=			{}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{}		,	state=lowerCAmelCase							)
		assert result == 3
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3}							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"test_dict = {'x': x, 'y': add_two(x)}"
		UpperCAmelCase_       				=			{"x": 3}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{"add_two": add_two}		,	state=lowerCAmelCase							)
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3, "y": 5}							)
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3, "test_dict": {"x": 3, "y": 5}}							)




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"x = 3\ny = 5"
		UpperCAmelCase_       				=			{}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{}		,	state=lowerCAmelCase							)
		# evaluate returns the value of the last assignment.
		assert result == 5
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3, "y": 5}							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"text = f'This is x: {x}.'"
		UpperCAmelCase_       				=			{"x": 3}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{}		,	state=lowerCAmelCase							)
		# evaluate returns the value of the last assignment.
		assert result == "This is x: 3."
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3, "text": "This is x: 3."}							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"if x <= 3:\n    y = 2\nelse:\n    y = 5"
		UpperCAmelCase_       				=			{"x": 3}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{}		,	state=lowerCAmelCase							)
		# evaluate returns the value of the last assignment.
		assert result == 2
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3, "y": 2}							)

		UpperCAmelCase_       				=			{"x": 8}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{}		,	state=lowerCAmelCase							)
		# evaluate returns the value of the last assignment.
		assert result == 5
		self.assertDictEqual(lowerCAmelCase		,	{"x": 8, "y": 5}							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"test_list = [x, add_two(x)]"
		UpperCAmelCase_       				=			{"x": 3}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{"add_two": add_two}		,	state=lowerCAmelCase							)
		self.assertListEqual(lowerCAmelCase		,	[3, 5]							)
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3, "test_list": [3, 5]}							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"y = x"
		UpperCAmelCase_       				=			{"x": 3}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{}		,	state=lowerCAmelCase							)
		assert result == 3
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3, "y": 3}							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"test_list = [x, add_two(x)]\ntest_list[1]"
		UpperCAmelCase_       				=			{"x": 3}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{"add_two": add_two}		,	state=lowerCAmelCase							)
		assert result == 5
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3, "test_list": [3, 5]}							)

		UpperCAmelCase_       				=			"test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
		UpperCAmelCase_       				=			{"x": 3}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{"add_two": add_two}		,	state=lowerCAmelCase							)
		assert result == 5
		self.assertDictEqual(lowerCAmelCase		,	{"x": 3, "test_dict": {"x": 3, "y": 5}}							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"x = 0\nfor i in range(3):\n    x = i"
		UpperCAmelCase_       				=			{}
		UpperCAmelCase_       				=			evaluate(lowerCAmelCase		,	{"range": range}		,	state=lowerCAmelCase							)
		assert result == 2
		self.assertDictEqual(lowerCAmelCase		,	{"x": 2, "i": 2}							)


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:    Optional[int] 							=			re.compile(r"""\s+""")



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



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			[len(A    ) for line in example["content"].splitlines()]
	return {"line_mean": np.mean(A    ), "line_max": max(A    )}



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			np.mean([c.isalnum() for c in example["content"]]    )
	return {"alpha_frac": alpha_frac}



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



def       __lowerCAmelCase      (						A       ,     A=5    ):
	UpperCAmelCase_       				=			["auto-generated", "autogenerated", "automatically generated"]
	UpperCAmelCase_       				=			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       __lowerCAmelCase      (						A       ,     A=5       ,     A=0.05    ):
	UpperCAmelCase_       				=			["unit tests", "test file", "configuration file"]
	UpperCAmelCase_       				=			example["content"].splitlines()
	UpperCAmelCase_       				=			0
	UpperCAmelCase_       				=			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
	UpperCAmelCase_       				=			example["content"].count("\n"    )
	UpperCAmelCase_       				=			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       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			["def ", "class ", "for ", "while "]
	UpperCAmelCase_       				=			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       __lowerCAmelCase      (						A       ,     A=4    ):
	UpperCAmelCase_       				=			example["content"].splitlines()
	UpperCAmelCase_       				=			0
	for line in lines:
		counter += line.lower().count("="    )
		if counter > minimum:
			return {"has_few_assignments": False}
	return {"has_few_assignments": True}



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			tokenizer(example["content"]       ,     truncation=A    )["input_ids"]
	UpperCAmelCase_       				=			len(example["content"]    ) / len(A    )
	return {"ratio": ratio}



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			{}
	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       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	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       __lowerCAmelCase      (						A    ):
	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:    str 							=			HfArgumentParser(PreprocessingArguments)
_a:    Any 							=			parser.parse_args()
if args.num_workers is None:
				_a:    Tuple 							=			multiprocessing.cpu_count()
_a:    str 							=			AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
_a:    Optional[Any] 							=			time.time()
_a:    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:
				_a:    Tuple 							=			time.time()
				_a							,			_a:    Dict 							=			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:    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)

_a:    int 							=			output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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