

def       __lowerCAmelCase      (						A    ):
	if not numbers:
		return 0

	if not isinstance(lowerCAmelCase__       ,     (list, tuple)    ) or not all(
	    isinstance(lowerCAmelCase__       ,     lowerCAmelCase__    ) for number in numbers    ):
		raise ValueError("numbers must be an iterable of integers"    )

	UpperCAmelCase_       				=			UpperCAmelCase_       				=			UpperCAmelCase_       				=			numbers[0]

	for i in range(1       ,     len(lowerCAmelCase__    )    ):
		# update the maximum and minimum subarray products
		UpperCAmelCase_       				=			numbers[i]
		if number < 0:
			UpperCAmelCase_				,   UpperCAmelCase_       				=			min_till_now, max_till_now
		UpperCAmelCase_       				=			max(lowerCAmelCase__       ,     max_till_now * number    )
		UpperCAmelCase_       				=			min(lowerCAmelCase__       ,     min_till_now * number    )

		# update the maximum product found till now
		UpperCAmelCase_       				=			max(lowerCAmelCase__       ,     lowerCAmelCase__    )

	return max_prod


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