

import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

class 						__UpperCamelCase    (					lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     CanineTokenizer
	SCREAMING_SNAKE_CASE__    				=     False




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		super().setUp()
		UpperCAmelCase_       				=			CanineTokenizer()
		tokenizer.save_pretrained(self.tmpdirname							)




	@cached_property
	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return CanineTokenizer.from_pretrained("google/canine-s"							)




	def       __A					(		self	:						List[str]		,	**lowerCAmelCase	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.tokenizer_class.from_pretrained(self.tmpdirname		,	**lowerCAmelCase							)
		UpperCAmelCase_       				=			1_024
		return tokenizer




	@require_torch
	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.canine_tokenizer
		UpperCAmelCase_       				=			["Life is like a box of chocolates.", "You never know what you're gonna get."]
		# fmt: off
		UpperCAmelCase_       				=			[57_344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57_345, 0, 0, 0, 0]
		# fmt: on
		UpperCAmelCase_       				=			tokenizer(lowerCAmelCase		,	padding=lowerCAmelCase		,	return_tensors="pt"							)
		self.assertIsInstance(lowerCAmelCase		,	lowerCAmelCase							)

		UpperCAmelCase_       				=			list(batch.input_ids.numpy()[0]							)

		self.assertListEqual(lowerCAmelCase		,	lowerCAmelCase							)

		self.assertEqual((2, 39)		,	batch.input_ids.shape							)
		self.assertEqual((2, 39)		,	batch.attention_mask.shape							)




	@require_torch
	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.canine_tokenizer
		UpperCAmelCase_       				=			["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
		UpperCAmelCase_       				=			tokenizer(lowerCAmelCase		,	padding=lowerCAmelCase		,	return_tensors="pt"							)
		# check if input_ids, attention_mask and token_type_ids are returned
		self.assertIn("input_ids"		,	lowerCAmelCase							)
		self.assertIn("attention_mask"		,	lowerCAmelCase							)
		self.assertIn("token_type_ids"		,	lowerCAmelCase							)




	@require_torch
	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.canine_tokenizer
		UpperCAmelCase_       				=			[
		    "What's the weater?",
		    "It's about 25 degrees.",
		]
		UpperCAmelCase_       				=			tokenizer(
		    text_target=lowerCAmelCase		,	max_length=32		,	padding="max_length"		,	truncation=lowerCAmelCase		,	return_tensors="pt"							)
		self.assertEqual(32		,	targets["input_ids"].shape[1]							)




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


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_tokenizers()
		for tokenizer in tokenizers:
			with self.subTest(F"{tokenizer.__class__.__name__}"							):
				self.assertNotEqual(tokenizer.model_max_length		,	42							)

        # Now let's start the test
		UpperCAmelCase_       				=			self.get_tokenizers()
		for tokenizer in tokenizers:
			with self.subTest(F"{tokenizer.__class__.__name__}"							):
				# Isolate this from the other tests because we save additional tokens/etc
				UpperCAmelCase_       				=			tempfile.mkdtemp()

				UpperCAmelCase_       				=			" He is very happy, UNwant\u00E9d,running"
				UpperCAmelCase_       				=			tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
				tokenizer.save_pretrained(lowerCAmelCase							)

				UpperCAmelCase_       				=			tokenizer.__class__.from_pretrained(lowerCAmelCase							)
				UpperCAmelCase_       				=			after_tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
				self.assertListEqual(lowerCAmelCase		,	lowerCAmelCase							)

				shutil.rmtree(lowerCAmelCase							)

		UpperCAmelCase_       				=			self.get_tokenizers(model_max_length=42							)
		for tokenizer in tokenizers:
			with self.subTest(F"{tokenizer.__class__.__name__}"							):
				# Isolate this from the other tests because we save additional tokens/etc
				UpperCAmelCase_       				=			tempfile.mkdtemp()

				UpperCAmelCase_       				=			" He is very happy, UNwant\u00E9d,running"

				UpperCAmelCase_       				=			tokenizer.additional_special_tokens

				# We can add a new special token for Canine as follows:
				UpperCAmelCase_       				=			chr(0XE007							)
				additional_special_tokens.append(lowerCAmelCase							)
				tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}							)
				UpperCAmelCase_       				=			tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
				tokenizer.save_pretrained(lowerCAmelCase							)

				UpperCAmelCase_       				=			tokenizer.__class__.from_pretrained(lowerCAmelCase							)
				UpperCAmelCase_       				=			after_tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
				self.assertListEqual(lowerCAmelCase		,	lowerCAmelCase							)
				self.assertIn(lowerCAmelCase		,	after_tokenizer.additional_special_tokens							)
				self.assertEqual(after_tokenizer.model_max_length		,	42							)

				UpperCAmelCase_       				=			tokenizer.__class__.from_pretrained(lowerCAmelCase		,	model_max_length=43							)
				self.assertEqual(tokenizer.model_max_length		,	43							)

				shutil.rmtree(lowerCAmelCase							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_tokenizers(do_lower_case=lowerCAmelCase							)
		for tokenizer in tokenizers:
			with self.subTest(F"{tokenizer.__class__.__name__}"							):
				UpperCAmelCase_				,   UpperCAmelCase_       				=			self.get_clean_sequence(lowerCAmelCase							)

				# a special token for Canine can be defined as follows:
				UpperCAmelCase_       				=			0XE005
				UpperCAmelCase_       				=			chr(lowerCAmelCase							)

				tokenizer.add_special_tokens({"cls_token": special_token}							)
				UpperCAmelCase_       				=			tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
				self.assertEqual(len(lowerCAmelCase							)		,	1							)

				UpperCAmelCase_       				=			tokenizer.decode(ids + encoded_special_token		,	clean_up_tokenization_spaces=lowerCAmelCase							)
				UpperCAmelCase_       				=			tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)

				UpperCAmelCase_       				=			tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
				UpperCAmelCase_       				=			tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
				self.assertEqual(lowerCAmelCase		,	input_encoded + special_token_id							)

				UpperCAmelCase_       				=			tokenizer.decode(lowerCAmelCase		,	skip_special_tokens=lowerCAmelCase							)
				self.assertTrue(special_token not in decoded							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_tokenizers(do_lower_case=lowerCAmelCase							)
		for tokenizer in tokenizers:
			with self.subTest(F"{tokenizer.__class__.__name__}"							):
				UpperCAmelCase_       				=			chr(0XE005							)
				UpperCAmelCase_       				=			chr(0XE006							)

				# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
				tokenizer.add_tokens([SPECIAL_TOKEN_1]		,	special_tokens=lowerCAmelCase							)
				# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
				# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
				tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]}							)

				UpperCAmelCase_       				=			tokenizer.tokenize(lowerCAmelCase							)
				UpperCAmelCase_       				=			tokenizer.tokenize(lowerCAmelCase							)

				self.assertEqual(len(lowerCAmelCase							)		,	1							)
				self.assertEqual(len(lowerCAmelCase							)		,	1							)
				self.assertEqual(token_a[0]		,	lowerCAmelCase							)
				self.assertEqual(token_a[0]		,	lowerCAmelCase							)




	@require_tokenizers
	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_tokenizers(do_lower_case=lowerCAmelCase							)
		for tokenizer in tokenizers:
			with self.subTest(F"{tokenizer.__class__.__name__}"							):
				# a special token for Canine can be defined as follows:
				UpperCAmelCase_       				=			0XE006
				UpperCAmelCase_       				=			chr(lowerCAmelCase							)

				UpperCAmelCase_       				=			AddedToken(lowerCAmelCase		,	lstrip=lowerCAmelCase							)
				tokenizer.add_special_tokens({"additional_special_tokens": [new_token]}							)

				with tempfile.TemporaryDirectory() as tmp_dir_name:
					tokenizer.save_pretrained(lowerCAmelCase							)
					tokenizer.from_pretrained(lowerCAmelCase							)




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		if self.test_slow_tokenizer:
			tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())							)

		if self.test_rust_tokenizer:
			tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())							)

		for tokenizer_class, tokenizer_utils in tokenizer_list:
			with tempfile.TemporaryDirectory() as tmp_dir:
				tokenizer_utils.save_pretrained(lowerCAmelCase							)

				with open(os.path.join(lowerCAmelCase		,	"special_tokens_map.json"							)		,	encoding="utf-8"							) as json_file:
					UpperCAmelCase_       				=			json.load(lowerCAmelCase							)

				with open(os.path.join(lowerCAmelCase		,	"tokenizer_config.json"							)		,	encoding="utf-8"							) as json_file:
					UpperCAmelCase_       				=			json.load(lowerCAmelCase							)

				# a special token for Canine can be defined as follows:
				UpperCAmelCase_       				=			0XE006
				UpperCAmelCase_       				=			chr(lowerCAmelCase							)

				UpperCAmelCase_       				=			[new_token_a]
				UpperCAmelCase_       				=			[new_token_a]

				with open(os.path.join(lowerCAmelCase		,	"special_tokens_map.json"							)		,	"w"		,	encoding="utf-8"							) as outfile:
					json.dump(lowerCAmelCase		,	lowerCAmelCase							)
				with open(os.path.join(lowerCAmelCase		,	"tokenizer_config.json"							)		,	"w"		,	encoding="utf-8"							) as outfile:
					json.dump(lowerCAmelCase		,	lowerCAmelCase							)

				# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
				# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
				# "special_tokens_map.json" files
				UpperCAmelCase_       				=			tokenizer_class.from_pretrained(lowerCAmelCase		,	extra_ids=0							)
				self.assertIn(lowerCAmelCase		,	tokenizer_without_change_in_init.additional_special_tokens							)
				# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
				self.assertEqual(
				    [new_token_a]		,	tokenizer_without_change_in_init.convert_ids_to_tokens(
				        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]							)							)		,	)

				UpperCAmelCase_       				=			0XE007
				UpperCAmelCase_       				=			chr(lowerCAmelCase							)
				# Now we test that we can change the value of additional_special_tokens in the from_pretrained
				UpperCAmelCase_       				=			[AddedToken(lowerCAmelCase		,	lstrip=lowerCAmelCase							)]
				UpperCAmelCase_       				=			tokenizer_class.from_pretrained(
				    lowerCAmelCase		,	additional_special_tokens=lowerCAmelCase		,	extra_ids=0							)

				self.assertIn(lowerCAmelCase		,	tokenizer.additional_special_tokens							)
				# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
				self.assertEqual(
				    [new_token_a]		,	tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]							)							)							)




	@require_tokenizers
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_tokenizers(do_lower_case=lowerCAmelCase							)
		for tokenizer in tokenizers:
			with self.subTest(F"{tokenizer.__class__.__name__}"							):
				UpperCAmelCase_       				=			"hello world"
				if self.space_between_special_tokens:
					UpperCAmelCase_       				=			"[CLS] hello world [SEP]"
				else:
					UpperCAmelCase_       				=			input
				UpperCAmelCase_       				=			tokenizer.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							)
				UpperCAmelCase_       				=			tokenizer.decode(lowerCAmelCase		,	spaces_between_special_tokens=self.space_between_special_tokens							)
				self.assertIn(lowerCAmelCase		,	[output, output.lower()]							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_tokenizers()
		for tokenizer in tokenizers:
			with self.subTest(F"{tokenizer.__class__.__name__}"							):
				UpperCAmelCase_       				=			[
				    "bos_token",
				    "eos_token",
				    "unk_token",
				    "sep_token",
				    "pad_token",
				    "cls_token",
				    "mask_token",
				]

		UpperCAmelCase_       				=			"a"
		UpperCAmelCase_       				=			ord(lowerCAmelCase							)

		for attr in attributes_list:
			setattr(lowerCAmelCase		,	attr + "_id"		,	lowerCAmelCase							)
			self.assertEqual(getattr(lowerCAmelCase		,	lowerCAmelCase							)		,	lowerCAmelCase							)
			self.assertEqual(getattr(lowerCAmelCase		,	attr + "_id"							)		,	lowerCAmelCase							)

			setattr(lowerCAmelCase		,	attr + "_id"		,	lowerCAmelCase							)
			self.assertEqual(getattr(lowerCAmelCase		,	lowerCAmelCase							)		,	lowerCAmelCase							)
			self.assertEqual(getattr(lowerCAmelCase		,	attr + "_id"							)		,	lowerCAmelCase							)

		setattr(lowerCAmelCase		,	"additional_special_tokens_ids"		,	[]							)
		self.assertListEqual(getattr(lowerCAmelCase		,	"additional_special_tokens"							)		,	[]							)
		self.assertListEqual(getattr(lowerCAmelCase		,	"additional_special_tokens_ids"							)		,	[]							)

		UpperCAmelCase_       				=			0XE006
		UpperCAmelCase_       				=			chr(lowerCAmelCase							)
		setattr(lowerCAmelCase		,	"additional_special_tokens_ids"		,	[additional_special_token_id]							)
		self.assertListEqual(getattr(lowerCAmelCase		,	"additional_special_tokens"							)		,	[additional_special_token]							)
		self.assertListEqual(getattr(lowerCAmelCase		,	"additional_special_tokens_ids"							)		,	[additional_special_token_id]							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		pass




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		pass




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


		'''simple docstring'''







		pass




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		pass




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		pass




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		pass




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


		'''simple docstring'''







		pass




	def       __A					(		self	:						int							):


		'''simple docstring'''







		pass


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}')