



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 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							CanineTokenizer
	__A							=							False




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	@cached_property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.tokenizer_class.from_pretrained(self.tmpdirname		,       **lowerCamelCase_		)
						a					     =   1024
						return tokenizer




	@require_torch
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.canine_tokenizer
						a					     =   ["Life is like a box of chocolates.", "You never know what you're gonna get."]
						# fmt: off
						a					     =   [57344, 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, 57345, 0, 0, 0, 0]
						# fmt: on
						a					     =   tokenizer(lowerCamelCase_		,       padding=lowerCamelCase_		,       return_tensors="pt"		)
						self.assertIsInstance(lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.canine_tokenizer
						a					     =   ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
						a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   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
						a					     =   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
																a					     =   tempfile.mkdtemp()

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

																a					     =   tokenizer.__class__.from_pretrained(lowerCamelCase_		)
																a					     =   after_tokenizer.encode(lowerCamelCase_		,       add_special_tokens=lowerCamelCase_		)
																self.assertListEqual(lowerCamelCase_		,       lowerCamelCase_		)

																shutil.rmtree(lowerCamelCase_		)

						a					     =   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
																a					     =   tempfile.mkdtemp()

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

																a					     =   tokenizer.additional_special_tokens

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

																a					     =   tokenizer.__class__.from_pretrained(lowerCamelCase_		)
																a					     =   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		)

																a					     =   tokenizer.__class__.from_pretrained(lowerCamelCase_		,       model_max_length=43		)
																self.assertEqual(tokenizer.model_max_length		,       43		)

																shutil.rmtree(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_tokenizers(do_lower_case=lowerCamelCase_		)
						for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''		):
																a     ,				a					     =   self.get_clean_sequence(lowerCamelCase_		)

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

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

																a					     =   tokenizer.decode(ids + encoded_special_token		,       clean_up_tokenization_spaces=lowerCamelCase_		)
																a					     =   tokenizer.encode(lowerCamelCase_		,       add_special_tokens=lowerCamelCase_		)

																a					     =   tokenizer.encode(lowerCamelCase_		,       add_special_tokens=lowerCamelCase_		)
																a					     =   tokenizer.encode(lowerCamelCase_		,       add_special_tokens=lowerCamelCase_		)
																self.assertEqual(lowerCamelCase_		,       input_encoded + special_token_id		)

																a					     =   tokenizer.decode(lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		)
																self.assertTrue(special_token not in decoded		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_tokenizers(do_lower_case=lowerCamelCase_		)
						for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''		):
																a					     =   chr(0XE005		)
																a					     =   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]}		)

																a					     =   tokenizer.tokenize(lowerCamelCase_		)
																a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   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:
																a					     =   0XE006
																a					     =   chr(lowerCamelCase_		)

																a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   []
						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:
																					a					     =   json.load(lowerCamelCase_		)

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

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

																a					     =   [new_token_a]
																a					     =   [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
																a					     =   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]		)		)		,       )

																a					     =   0XE007
																a					     =   chr(lowerCamelCase_		)
																# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																a					     =   [AddedToken(lowerCamelCase_		,       lstrip=lowerCamelCase_		)]
																a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

						a					     =   "a"
						a					     =   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"		)		,       []		)

						a					     =   0XE006
						a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass





def     a(      A       :    int       )		->       str:
					"""simple docstring"""





					if number > 0:
										raise ValueError("input must be a negative integer"       )
					a					     =   len(bin(A       )[3:]       )
					a					     =   bin(abs(A       ) - (1 << binary_number_length)       )[3:]
					a					     =   (
					    (
					        "1"
					        + "0" * (binary_number_length - len(A       ))
					        + twos_complement_number
					    )
					    if number < 0
					    else "0"
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()
