

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							(			lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					Dict			     =     CanineTokenizer
				__SCREAMING_SNAKE_CASE   :					int			     =     False




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




				@cached_property
				def        a				(   self			):
											return CanineTokenizer.from_pretrained('google/canine-s'			)




				def        a				(   self ,  **snake_case			):
											snake_case_     							=       self.tokenizer_class.from_pretrained(self.tmpdirname ,  **snake_case			)
											snake_case_     							=       1024
											return tokenizer




				@require_torch
				def        a				(   self			):
											snake_case_     							=       self.canine_tokenizer
											snake_case_     							=       ['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
											# fmt: off
											snake_case_     							=       [5_7344, 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, 5_7345, 0, 0, 0, 0]
											# fmt: on
											snake_case_     							=       tokenizer(snake_case ,  padding=snake_case ,  return_tensors='pt'			)
											self.assertIsInstance(snake_case ,  snake_case			)

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

											self.assertListEqual(snake_case ,  snake_case			)

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




				@require_torch
				def        a				(   self			):
											snake_case_     							=       self.canine_tokenizer
											snake_case_     							=       ['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
											snake_case_     							=       tokenizer(snake_case ,  padding=snake_case ,  return_tensors='pt'			)
											# check if input_ids, attention_mask and token_type_ids are returned
											self.assertIn('input_ids' ,  snake_case			)
											self.assertIn('attention_mask' ,  snake_case			)
											self.assertIn('token_type_ids' ,  snake_case			)




				@require_torch
				def        a				(   self			):
											snake_case_     							=       self.canine_tokenizer
											snake_case_     							=       [
											    'What\'s the weater?',
											    'It\'s about 25 degrees.',
											]
											snake_case_     							=       tokenizer(
											    text_target=snake_case ,  max_length=32 ,  padding='max_length' ,  truncation=snake_case ,  return_tensors='pt'			)
											self.assertEqual(32 ,  targets['input_ids'].shape[1]			)




				def        a				(   self			):
											# safety check on max_len default value so we are sure the test works
											snake_case_     							=       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
											snake_case_     							=       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
																									snake_case_     							=       tempfile.mkdtemp()

																									snake_case_     							=       ' He is very happy, UNwant\u00E9d,running'
																									snake_case_     							=       tokenizer.encode(snake_case ,  add_special_tokens=snake_case			)
																									tokenizer.save_pretrained(snake_case			)

																									snake_case_     							=       tokenizer.__class__.from_pretrained(snake_case			)
																									snake_case_     							=       after_tokenizer.encode(snake_case ,  add_special_tokens=snake_case			)
																									self.assertListEqual(snake_case ,  snake_case			)

																									shutil.rmtree(snake_case			)

											snake_case_     							=       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
																									snake_case_     							=       tempfile.mkdtemp()

																									snake_case_     							=       ' He is very happy, UNwant\u00E9d,running'

																									snake_case_     							=       tokenizer.additional_special_tokens

																									# We can add a new special token for Canine as follows:
																									snake_case_     							=       chr(0xE007			)
																									additional_special_tokens.append(snake_case			)
																									tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}			)
																									snake_case_     							=       tokenizer.encode(snake_case ,  add_special_tokens=snake_case			)
																									tokenizer.save_pretrained(snake_case			)

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

																									snake_case_     							=       tokenizer.__class__.from_pretrained(snake_case ,  model_max_length=43			)
																									self.assertEqual(tokenizer.model_max_length ,  43			)

																									shutil.rmtree(snake_case			)




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

																									# a special token for Canine can be defined as follows:
																									snake_case_     							=       0xE005
																									snake_case_     							=       chr(snake_case			)

																									tokenizer.add_special_tokens({'cls_token': special_token}			)
																									snake_case_     							=       tokenizer.encode(snake_case ,  add_special_tokens=snake_case			)
																									self.assertEqual(len(snake_case			) ,  1			)

																									snake_case_     							=       tokenizer.decode(ids + encoded_special_token ,  clean_up_tokenization_spaces=snake_case			)
																									snake_case_     							=       tokenizer.encode(snake_case ,  add_special_tokens=snake_case			)

																									snake_case_     							=       tokenizer.encode(snake_case ,  add_special_tokens=snake_case			)
																									snake_case_     							=       tokenizer.encode(snake_case ,  add_special_tokens=snake_case			)
																									self.assertEqual(snake_case ,  input_encoded + special_token_id			)

																									snake_case_     							=       tokenizer.decode(snake_case ,  skip_special_tokens=snake_case			)
																									self.assertTrue(special_token not in decoded			)




				def        a				(   self			):
											snake_case_     							=       self.get_tokenizers(do_lower_case=snake_case			)
											for tokenizer in tokenizers:
																		with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																									snake_case_     							=       chr(0xE005			)
																									snake_case_     							=       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=snake_case			)
																									# `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]}			)

																									snake_case_     							=       tokenizer.tokenize(snake_case			)
																									snake_case_     							=       tokenizer.tokenize(snake_case			)

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




				@require_tokenizers
				def        a				(   self			):
											snake_case_     							=       self.get_tokenizers(do_lower_case=snake_case			)
											for tokenizer in tokenizers:
																		with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																									# a special token for Canine can be defined as follows:
																									snake_case_     							=       0xE006
																									snake_case_     							=       chr(snake_case			)

																									snake_case_     							=       AddedToken(snake_case ,  lstrip=snake_case			)
																									tokenizer.add_special_tokens({'additional_special_tokens': [new_token]}			)

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




				def        a				(   self			):
											snake_case_     							=       []
											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(snake_case			)

																									with open(os.path.join(snake_case ,  'special_tokens_map.json'			) ,  encoding='utf-8'			) as json_file:
																																snake_case_     							=       json.load(snake_case			)

																									with open(os.path.join(snake_case ,  'tokenizer_config.json'			) ,  encoding='utf-8'			) as json_file:
																																snake_case_     							=       json.load(snake_case			)

																									# a special token for Canine can be defined as follows:
																									snake_case_     							=       0xE006
																									snake_case_     							=       chr(snake_case			)

																									snake_case_     							=       [new_token_a]
																									snake_case_     							=       [new_token_a]

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

																									# 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
																									snake_case_     							=       tokenizer_class.from_pretrained(snake_case ,  extra_ids=0			)
																									self.assertIn(snake_case ,  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]			)			) ,  )

																									snake_case_     							=       0xE007
																									snake_case_     							=       chr(snake_case			)
																									# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																									snake_case_     							=       [AddedToken(snake_case ,  lstrip=snake_case			)]
																									snake_case_     							=       tokenizer_class.from_pretrained(
																									    snake_case ,  additional_special_tokens=snake_case ,  extra_ids=0			)

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




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

											snake_case_     							=       'a'
											snake_case_     							=       ord(snake_case			)

											for attr in attributes_list:
																		setattr(snake_case ,  attr + '_id' ,  snake_case			)
																		self.assertEqual(getattr(snake_case ,  snake_case			) ,  snake_case			)
																		self.assertEqual(getattr(snake_case ,  attr + '_id'			) ,  snake_case			)

																		setattr(snake_case ,  attr + '_id' ,  snake_case			)
																		self.assertEqual(getattr(snake_case ,  snake_case			) ,  snake_case			)
																		self.assertEqual(getattr(snake_case ,  attr + '_id'			) ,  snake_case			)

											setattr(snake_case ,  'additional_special_tokens_ids' ,  []			)
											self.assertListEqual(getattr(snake_case ,  'additional_special_tokens'			) ,  []			)
											self.assertListEqual(getattr(snake_case ,  'additional_special_tokens_ids'			) ,  []			)

											snake_case_     							=       0xE006
											snake_case_     							=       chr(snake_case			)
											setattr(snake_case ,  'additional_special_tokens_ids' ,  [additional_special_token_id]			)
											self.assertListEqual(getattr(snake_case ,  'additional_special_tokens'			) ,  [additional_special_token]			)
											self.assertListEqual(getattr(snake_case ,  'additional_special_tokens_ids'			) ,  [additional_special_token_id]			)




				def        a				(   self			):
											pass




				def        a				(   self			):
											pass




				def        a				(   self			):
											pass




				def        a				(   self			):
											pass




				def        a				(   self			):
											pass




				def        a				(   self			):
											pass




				def        a				(   self			):
											pass




				def        a				(   self			):
											pass







from collections.abc import Callable







class 					lowercase							:




				def __init__(   self ,  snake_case = None			):
											# Stores actual heap items.
											snake_case_     							=       []
											# Stores indexes of each item for supporting updates and deletion.
											snake_case_     							=       {}
											# Stores current size of heap.
											snake_case_     							=       0
											# Stores function used to evaluate the score of an item on which basis ordering
											# will be done.
											snake_case_     							=       key or (lambda snake_case			: x)




				def        a				(   self ,  snake_case			):
											return int((i - 1) / 2			) if i > 0 else None




				def        a				(   self ,  snake_case			):
											snake_case_     							=       int(2 * i + 1			)
											return left if 0 < left < self.size else None




				def        a				(   self ,  snake_case			):
											snake_case_     							=       int(2 * i + 2			)
											return right if 0 < right < self.size else None




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_							,       snake_case_     							=       (
											    self.pos_map[self.arr[j][0]],
											    self.pos_map[self.arr[i][0]],
											)
											# Then swap the items in the list.
											snake_case_							,       snake_case_     							=       self.arr[j], self.arr[i]




				def        a				(   self ,  snake_case ,  snake_case			):
											return self.arr[i][1] < self.arr[j][1]




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._left(snake_case			)
											snake_case_     							=       self._right(snake_case			)
											snake_case_     							=       i

											if left is not None and not self._cmp(snake_case ,  snake_case			):
																		snake_case_     							=       left
											if right is not None and not self._cmp(snake_case ,  snake_case			):
																		snake_case_     							=       right

											return valid_parent




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._parent(snake_case			)
											while parent is not None and not self._cmp(snake_case ,  snake_case			):
																		self._swap(snake_case ,  snake_case			)
																		snake_case_							,       snake_case_     							=       parent, self._parent(snake_case			)




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._get_valid_parent(snake_case			)
											while valid_parent != index:
																		self._swap(snake_case ,  snake_case			)
																		snake_case_							,       snake_case_     							=       valid_parent, self._get_valid_parent(snake_case			)




				def        a				(   self ,  snake_case ,  snake_case			):
											if item not in self.pos_map:
																		return
											snake_case_     							=       self.pos_map[item]
											snake_case_     							=       [item, self.key(snake_case			)]
											# Make sure heap is right in both up and down direction.
											# Ideally only one of them will make any change.
											self._heapify_up(snake_case			)
											self._heapify_down(snake_case			)




				def        a				(   self ,  snake_case			):
											if item not in self.pos_map:
																		return
											snake_case_     							=       self.pos_map[item]
											del self.pos_map[item]
											snake_case_     							=       self.arr[self.size - 1]
											snake_case_     							=       index
											self.size -= 1
											# Make sure heap is right in both up and down direction. Ideally only one
											# of them will make any change- so no performance loss in calling both.
											if self.size > index:
																		self._heapify_up(snake_case			)
																		self._heapify_down(snake_case			)




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_     							=       len(self.arr			)
											if arr_len == self.size:
																		self.arr.append([item, self.key(snake_case			)]			)
											else:
																		snake_case_     							=       [item, self.key(snake_case			)]
											snake_case_     							=       self.size
											self.size += 1
											self._heapify_up(self.size - 1			)




				def        a				(   self			):
											return self.arr[0] if self.size else None




				def        a				(   self			):
											snake_case_     							=       self.get_top()
											if top_item_tuple:
																		self.delete_item(top_item_tuple[0]			)
											return top_item_tuple





def    __lowerCamelCase      (					):







							'''simple docstring'''








if __name__ == "__main__":
					import doctest

					doctest.testmod()




