

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:    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}')




import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_a:    str 							=			get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
@require_tokenizers
class 						__UpperCamelCase    (					lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     ReformerTokenizer
	SCREAMING_SNAKE_CASE__    				=     ReformerTokenizerFast
	SCREAMING_SNAKE_CASE__    				=     True
	SCREAMING_SNAKE_CASE__    				=     False
	SCREAMING_SNAKE_CASE__    				=     True




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		super().setUp()

		UpperCAmelCase_       				=			ReformerTokenizer(lowerCAmelCase		,	keep_accents=lowerCAmelCase							)
		tokenizer.save_pretrained(self.tmpdirname							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"<s>"
		UpperCAmelCase_       				=			1

		self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase							)		,	lowerCAmelCase							)
		self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase							)		,	lowerCAmelCase							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			list(self.get_tokenizer().get_vocab().keys()							)

		self.assertEqual(vocab_keys[0]		,	"<unk>"							)
		self.assertEqual(vocab_keys[1]		,	"<s>"							)
		self.assertEqual(vocab_keys[-1]		,	"j"							)
		self.assertEqual(len(lowerCAmelCase							)		,	1_000							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		self.assertEqual(self.get_tokenizer().vocab_size		,	1_000							)




	def       __A					(		self	:						str							):


		'''simple docstring'''







		if not self.test_rust_tokenizer:
			return

		UpperCAmelCase_       				=			self.get_tokenizer()
		UpperCAmelCase_       				=			self.get_rust_tokenizer()

		UpperCAmelCase_       				=			"I was born in 92000, and this is falsé."

		UpperCAmelCase_       				=			tokenizer.tokenize(lowerCAmelCase							)
		UpperCAmelCase_       				=			rust_tokenizer.tokenize(lowerCAmelCase							)
		self.assertListEqual(lowerCAmelCase		,	lowerCAmelCase							)

		UpperCAmelCase_       				=			tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
		UpperCAmelCase_       				=			rust_tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
		self.assertListEqual(lowerCAmelCase		,	lowerCAmelCase							)

		UpperCAmelCase_       				=			self.get_rust_tokenizer()
		UpperCAmelCase_       				=			tokenizer.encode(lowerCAmelCase							)
		UpperCAmelCase_       				=			rust_tokenizer.encode(lowerCAmelCase							)
		self.assertListEqual(lowerCAmelCase		,	lowerCAmelCase							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						Optional[int]=15							):


		'''simple docstring'''







		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
				UpperCAmelCase_       				=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase		,	**lowerCAmelCase							)

				# Simple input
				UpperCAmelCase_       				=			"This is a simple input"
				UpperCAmelCase_       				=			["This is a simple input 1", "This is a simple input 2"]
				UpperCAmelCase_       				=			("This is a simple input", "This is a pair")
				UpperCAmelCase_       				=			[
				    ("This is a simple input 1", "This is a simple input 2"),
				    ("This is a simple pair 1", "This is a simple pair 2"),
				]

				# Simple input tests
				self.assertRaises(lowerCAmelCase		,	tokenizer_r.encode		,	lowerCAmelCase		,	max_length=lowerCAmelCase		,	padding="max_length"							)

				# Simple input
				self.assertRaises(lowerCAmelCase		,	tokenizer_r.encode_plus		,	lowerCAmelCase		,	max_length=lowerCAmelCase		,	padding="max_length"							)

				# Simple input
				self.assertRaises(
				    lowerCAmelCase		,	tokenizer_r.batch_encode_plus		,	lowerCAmelCase		,	max_length=lowerCAmelCase		,	padding="max_length"		,	)

				# Pair input
				self.assertRaises(lowerCAmelCase		,	tokenizer_r.encode		,	lowerCAmelCase		,	max_length=lowerCAmelCase		,	padding="max_length"							)

				# Pair input
				self.assertRaises(lowerCAmelCase		,	tokenizer_r.encode_plus		,	lowerCAmelCase		,	max_length=lowerCAmelCase		,	padding="max_length"							)

				# Pair input
				self.assertRaises(
				    lowerCAmelCase		,	tokenizer_r.batch_encode_plus		,	lowerCAmelCase		,	max_length=lowerCAmelCase		,	padding="max_length"		,	)




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


		'''simple docstring'''







		pass




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			ReformerTokenizer(lowerCAmelCase		,	keep_accents=lowerCAmelCase							)

		UpperCAmelCase_       				=			tokenizer.tokenize("This is a test"							)
		self.assertListEqual(lowerCAmelCase		,	["▁This", "▁is", "▁a", "▁t", "est"]							)

		self.assertListEqual(
		    tokenizer.convert_tokens_to_ids(lowerCAmelCase							)		,	[285, 46, 10, 170, 382]		,	)

		UpperCAmelCase_       				=			tokenizer.tokenize("I was born in 92000, and this is falsé."							)
		self.assertListEqual(
		    lowerCAmelCase		,	[
		        SPIECE_UNDERLINE + "I",
		        SPIECE_UNDERLINE + "was",
		        SPIECE_UNDERLINE + "b",
		        "or",
		        "n",
		        SPIECE_UNDERLINE + "in",
		        SPIECE_UNDERLINE + "",
		        "9",
		        "2",
		        "0",
		        "0",
		        "0",
		        ",",
		        SPIECE_UNDERLINE + "and",
		        SPIECE_UNDERLINE + "this",
		        SPIECE_UNDERLINE + "is",
		        SPIECE_UNDERLINE + "f",
		        "al",
		        "s",
		        "é",
		        ".",
		    ]		,	)
		UpperCAmelCase_       				=			tokenizer.convert_tokens_to_ids(lowerCAmelCase							)
		self.assertListEqual(
		    lowerCAmelCase		,	[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,	)

		UpperCAmelCase_       				=			tokenizer.convert_ids_to_tokens(lowerCAmelCase							)
		self.assertListEqual(
		    lowerCAmelCase		,	[
		        SPIECE_UNDERLINE + "I",
		        SPIECE_UNDERLINE + "was",
		        SPIECE_UNDERLINE + "b",
		        "or",
		        "n",
		        SPIECE_UNDERLINE + "in",
		        SPIECE_UNDERLINE + "",
		        "<unk>",
		        "2",
		        "0",
		        "0",
		        "0",
		        ",",
		        SPIECE_UNDERLINE + "and",
		        SPIECE_UNDERLINE + "this",
		        SPIECE_UNDERLINE + "is",
		        SPIECE_UNDERLINE + "f",
		        "al",
		        "s",
		        "<unk>",
		        ".",
		    ]		,	)




	@cached_property
	def       __A					(		self	:						int							):


		'''simple docstring'''







		return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"							)




	@slow
	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"Hello World!"
		UpperCAmelCase_       				=			[126, 32, 262, 152, 38, 72, 287]

		self.assertListEqual(lowerCAmelCase		,	self.big_tokenizer.encode(lowerCAmelCase							)							)




	@slow
	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			(
		    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
		    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
		)
		UpperCAmelCase_       				=			[
		    108,
		    265,
		    24,
		    111,
		    4,
		    258,
		    156,
		    35,
		    28,
		    275,
		    3,
		    259,
		    297,
		    260,
		    84,
		    4,
		    35,
		    110,
		    44,
		    8,
		    259,
		    91,
		    268,
		    21,
		    11,
		    209,
		    274,
		    109,
		    266,
		    277,
		    117,
		    86,
		    93,
		    315,
		    258,
		    278,
		    258,
		    277,
		    258,
		    0,
		    258,
		    288,
		    258,
		    319,
		    258,
		    0,
		    258,
		    0,
		    258,
		    0,
		    258,
		    0,
		    258,
		    287,
		    258,
		    315,
		    258,
		    289,
		    258,
		    278,
		    99,
		    269,
		    266,
		    262,
		    8,
		    259,
		    241,
		    4,
		    217,
		    230,
		    268,
		    266,
		    55,
		    168,
		    106,
		    75,
		    193,
		    266,
		    223,
		    27,
		    49,
		    26,
		    282,
		    25,
		    264,
		    299,
		    19,
		    26,
		    0,
		    258,
		    277,
		    117,
		    86,
		    93,
		    176,
		    183,
		    270,
		    11,
		    262,
		    42,
		    61,
		    265,
		]

		self.assertListEqual(lowerCAmelCase		,	self.big_tokenizer.encode(lowerCAmelCase							)							)




	@require_torch
	@slow
	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		import torch

		from transformers import ReformerConfig, ReformerModel

		# Build sequence
		UpperCAmelCase_       				=			list(self.big_tokenizer.get_vocab().keys()							)[:10]
		UpperCAmelCase_       				=			" ".join(lowerCAmelCase							)
		UpperCAmelCase_       				=			self.big_tokenizer.encode_plus(lowerCAmelCase		,	return_tensors="pt"							)
		UpperCAmelCase_       				=			self.big_tokenizer.batch_encode_plus([sequence, sequence]		,	return_tensors="pt"							)

		UpperCAmelCase_       				=			ReformerConfig()
		# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
		UpperCAmelCase_       				=			encoded_sequence["input_ids"].shape
		UpperCAmelCase_       				=			ReformerModel(lowerCAmelCase							)

		# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
		assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

		with torch.no_grad():
			model(**lowerCAmelCase							)
			model(**lowerCAmelCase							)




	@slow
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
		# fmt: on

		# This tokenizer does not know some characters like ")".
		# That is the reason why we use very simple texts here.
		# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
		UpperCAmelCase_       				=			[
		    "This is a very simple sentence.",
		    "The quick brown fox jumps over the lazy dog.",
		]

		self.tokenizer_integration_test_util(
		    expected_encoding=lowerCAmelCase		,	model_name="google/reformer-crime-and-punishment"		,	revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"		,	padding=lowerCAmelCase		,	sequences=lowerCAmelCase		,	)