

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}""")



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A									=  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MraForMaskedLM',
		    'MraForMultipleChoice',
		    'MraForQuestionAnswering',
		    'MraForSequenceClassification',
		    'MraForTokenClassification',
		    'MraLayer',
		    'MraModel',
		    'MraPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure)
