

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_ :				Dict						 =    re.compile(r'\s+')

def 		UpperCamelCase      (lowercase_:				List[str]      )  ->   List[Any]:
	return {"hash": hashlib.mda(re.sub(lowercase_					,   """"""					,   example["""content"""]      ).encode("""utf-8"""      )      ).hexdigest()}

def 		UpperCamelCase      (lowercase_:				Union[str, Any]      )  ->   int:
	A__							:					Any       =	[len(lowercase_      ) for line in example["""content"""].splitlines()]
	return {"line_mean": np.mean(lowercase_      ), "line_max": max(lowercase_      )}

def 		UpperCamelCase      (lowercase_:				Dict      )  ->   Union[str, Any]:
	A__							:					List[Any]       =	np.mean([c.isalnum() for c in example["""content"""]]      )
	return {"alpha_frac": alpha_frac}

def 		UpperCamelCase      (lowercase_:				Optional[Any]					,   lowercase_:				str      )  ->   int:
	if example["hash"] in uniques:
		uniques.remove(example["""hash"""]      )
		return True
	else:
		return False

def 		UpperCamelCase      (lowercase_:				Union[str, Any]					,   lowercase_:				Any=5      )  ->   List[str]:
	A__							:					Optional[int]       =	["""auto-generated""", """autogenerated""", """automatically generated"""]
	A__							:					Any       =	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 		UpperCamelCase      (lowercase_:				Optional[int]					,   lowercase_:				Tuple=5					,   lowercase_:				int=0.05      )  ->   Union[str, Any]:
	A__							:					List[Any]       =	["""unit tests""", """test file""", """configuration file"""]
	A__							:					str       =	example["""content"""].splitlines()
	A__							:					Optional[int]       =	0
	A__							:					Any       =	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
	A__							:					List[Any]       =	example["""content"""].count("""\n"""      )
	A__							:					Any       =	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      (lowercase_:				Optional[Any]      )  ->   List[str]:
	A__							:					Union[str, Any]       =	["""def """, """class """, """for """, """while """]
	A__							:					Tuple       =	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      (lowercase_:				Any					,   lowercase_:				str=4      )  ->   List[str]:
	A__							:					List[str]       =	example["""content"""].splitlines()
	A__							:					Union[str, 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      (lowercase_:				int      )  ->   int:
	A__							:					str       =	tokenizer(example["""content"""]					,   truncation=lowercase_      )["""input_ids"""]
	A__							:					int       =	len(example["""content"""]      ) / len(lowercase_      )
	return {"ratio": ratio}

def 		UpperCamelCase      (lowercase_:				Tuple      )  ->   Optional[Any]:
	A__							:					Dict       =	{}
	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 		UpperCamelCase      (lowercase_:				int					,   lowercase_:				Any					,   lowercase_:				Optional[int]      )  ->   Tuple:
	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 		UpperCamelCase      (lowercase_:				Optional[int]      )  ->   Optional[int]:
	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_ :				Any						 =    HfArgumentParser(PreprocessingArguments)
A_ :				Union[str, Any]						 =    parser.parse_args()
if args.num_workers is None:
	A_ :				Union[str, Any]						 =    multiprocessing.cpu_count()
A_ :				Any						 =    AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

# Deduplicate hashes
A_ :				Optional[int]						 =    set(ds.unique('hash'))
A_ :				Optional[Any]						 =    len(uniques) / len(ds)
print(f'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
A_ :				Optional[int]						 =    time.time()
A_ :				List[str]						 =    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_ :				Any						 =    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_ :				Dict						 =    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_ :				List[Any]						 =    output_dir / 'data'
data_dir.mkdir(exist_ok=True)

A_ :				Union[str, Any]						 =    time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
	A_ :				Any						 =    str(data_dir / f'''file-{file_number+1:012}.json''')
	A_ :				Union[str, 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}''')






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
	from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments



@require_torch
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def 						__A    (				self					, A__    ):
						for model_result in results.values():
							for batch_size, sequence_length in zip(model_result["""bs"""]					, model_result["""ss"""]    ):
								A__							:					str       =	model_result["""result"""][batch_size][sequence_length]
								self.assertIsNotNone(A__    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sshleifer/tiny-gpt2"""
						A__							:					Tuple       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__    )
						A__							:					List[Any]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sgugger/tiny-distilbert-classification"""
						A__							:					Dict       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, only_pretrain_model=A__					, )
						A__							:					str       =	PyTorchBenchmark(A__    )
						A__							:					List[str]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Any       =	"""sshleifer/tiny-gpt2"""
						A__							:					List[Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, torchscript=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Tuple       =	PyTorchBenchmark(A__    )
						A__							:					str       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					@unittest.skipIf(torch_device == """cpu"""					, """Cant do half precision"""    )
					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, fpaa=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					str       =	PyTorchBenchmark(A__    )
						A__							:					Any       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Tuple       =	AutoConfig.from_pretrained(A__    )
						# set architectures equal to `None`
						A__							:					List[Any]       =	None
						A__							:					str       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					List[str]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Any       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[int]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Any       =	PyTorchBenchmark(A__    )
						A__							:					Dict       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					@unittest.skipIf(torch_device == """cpu"""					, """Can't do half precision"""    )
					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					List[str]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, fpaa=A__					, multi_process=A__					, )
						A__							:					Dict       =	PyTorchBenchmark(A__    )
						A__							:					Optional[int]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					int       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[int]       =	AutoConfig.from_pretrained(A__    )
						A__							:					str       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Tuple       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					List[str]       =	"""sshleifer/tinier_bart"""
						A__							:					List[str]       =	AutoConfig.from_pretrained(A__    )
						A__							:					List[str]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Union[str, Any]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					str       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Union[str, Any]       =	AutoConfig.from_pretrained(A__    )
						A__							:					Tuple       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Tuple       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sshleifer/tinier_bart"""
						A__							:					int       =	AutoConfig.from_pretrained(A__    )
						A__							:					Union[str, Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					List[Any]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Optional[Any]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					int       =	"""sshleifer/tiny-gpt2"""
						with tempfile.TemporaryDirectory() as tmp_dir:
							A__							:					Dict       =	PyTorchBenchmarkArguments(
							    models=[MODEL_ID]					, training=A__					, inference=A__					, save_to_csv=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, inference_time_csv_file=os.path.join(A__					, """inf_time.csv"""    )					, train_memory_csv_file=os.path.join(A__					, """train_mem.csv"""    )					, inference_memory_csv_file=os.path.join(A__					, """inf_mem.csv"""    )					, train_time_csv_file=os.path.join(A__					, """train_time.csv"""    )					, env_info_csv_file=os.path.join(A__					, """env.csv"""    )					, multi_process=A__					, )
							A__							:					Optional[Any]       =	PyTorchBenchmark(A__    )
							benchmark.run()
							self.assertTrue(Path(os.path.join(A__					, """inf_time.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """train_time.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """inf_mem.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """train_mem.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """env.csv"""    )    ).exists()    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""

						def _check_summary_is_not_empty(A__    ):
							self.assertTrue(hasattr(A__					, """sequential"""    )    )
							self.assertTrue(hasattr(A__					, """cumulative"""    )    )
							self.assertTrue(hasattr(A__					, """current"""    )    )
							self.assertTrue(hasattr(A__					, """total"""    )    )

						with tempfile.TemporaryDirectory() as tmp_dir:
							A__							:					Dict       =	PyTorchBenchmarkArguments(
							    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, log_filename=os.path.join(A__					, """log.txt"""    )					, log_print=A__					, trace_memory_line_by_line=A__					, multi_process=A__					, )
							A__							:					Dict       =	PyTorchBenchmark(A__    )
							A__							:					str       =	benchmark.run()
							_check_summary_is_not_empty(result.inference_summary    )
							_check_summary_is_not_empty(result.train_summary    )
							self.assertTrue(Path(os.path.join(A__					, """log.txt"""    )    ).exists()    )



