



import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 						lowerCamelCase_     (			__UpperCamelCase    ,unittest.TestCase    ):






						'''simple docstring'''
						a__ =	BartTokenizer
						a__ =	BartTokenizerFast
						a__ =	True
						a__ =	filter_roberta_detectors







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Optional[Any]:
									super().setUp()
									A				:       Dict														=    [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''<unk>''',
									]
									A				:       Any														=    dict(zip(a_						,						range(len(a_			)			)			)			)
									A				:       Optional[Any]														=    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									A				:       str														=    {'''unk_token''': '''<unk>'''}

									A				:       Optional[int]														=    os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES["vocab_file"]			)
									A				:       List[Any]														=    os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES["merges_file"]			)
									with open(self.vocab_file						,						"w"						,						encoding="utf-8"			) as fp:
												fp.write(json.dumps(a_			) + "\n"			)
									with open(self.merges_file						,						"w"						,						encoding="utf-8"			) as fp:
												fp.write("\n".join(a_			)			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						**__lowerCamelCase   :      List[Any]			)   ->					Any:
									kwargs.update(self.special_tokens_map			)
									return self.tokenizer_class.from_pretrained(self.tmpdirname						,						**a_			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]						,						**__lowerCamelCase   :      Dict			)   ->					Union[str, Any]:
									kwargs.update(self.special_tokens_map			)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,						**a_			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      Any			)   ->					int:
									return "lower newer", "lower newer"







						@cached_property
						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					Dict:
									return BartTokenizer.from_pretrained("facebook/bart-large"			)







						@cached_property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					List[Any]:
									return BartTokenizerFast.from_pretrained("facebook/bart-large"			)







						@require_torch
						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Any:
									A				:       List[str]														=    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									A				:       Any														=    [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												A				:       List[str]														=    tokenizer(a_						,						max_length=len(a_			)						,						padding=a_						,						return_tensors="pt"			)
												self.assertIsInstance(a_						,						a_			)

												self.assertEqual((2, 9)						,						batch.input_ids.shape			)
												self.assertEqual((2, 9)						,						batch.attention_mask.shape			)
												A				:       List[Any]														=    batch.input_ids.tolist()[0]
												self.assertListEqual(a_						,						a_			)
												# Test that special tokens are reset







						@require_torch
						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					int:
									A				:       Tuple														=    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												A				:       List[Any]														=    tokenizer(a_						,						padding=a_						,						return_tensors="pt"			)
												# check if input_ids are returned and no labels
												self.assertIn("input_ids"						,						a_			)
												self.assertIn("attention_mask"						,						a_			)
												self.assertNotIn("labels"						,						a_			)
												self.assertNotIn("decoder_attention_mask"						,						a_			)







						@require_torch
						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Tuple:
									A				:       Dict														=    [
									    '''Summary of the text.''',
									    '''Another summary.''',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												A				:       str														=    tokenizer(text_target=a_						,						max_length=32						,						padding="max_length"						,						return_tensors="pt"			)
												self.assertEqual(32						,						targets["input_ids"].shape[1]			)







						@require_torch
						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Optional[Any]:





									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												A				:       Any														=    tokenizer(
												    ["I am a small frog" * 10_24, "I am a small frog"]						,						padding=a_						,						truncation=a_						,						return_tensors="pt"			)
												self.assertIsInstance(a_						,						a_			)
												self.assertEqual(batch.input_ids.shape						,						(2, 10_24)			)







						@require_torch
						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Optional[Any]:
									A				:       Optional[Any]														=    ['''A long paragraph for summarization.''']
									A				:       str														=    [
									    '''Summary of the text.''',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												A				:       Optional[Any]														=    tokenizer(a_						,						return_tensors="pt"			)
												A				:       str														=    tokenizer(text_target=a_						,						return_tensors="pt"			)
												A				:       Dict														=    inputs['''input_ids''']
												A				:       Tuple														=    targets['''input_ids''']
												self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()			)
												self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()			)
												self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()			)
												self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Tuple:
									pass







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Optional[Any]:





									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
															A				:       Dict														=    self.rust_tokenizer_class.from_pretrained(a_						,						**a_			)
															A				:       Optional[Any]														=    self.tokenizer_class.from_pretrained(a_						,						**a_			)
															A				:       str														=    '''A, <mask> AllenNLP sentence.'''
															A				:       Optional[int]														=    tokenizer_r.encode_plus(a_						,						add_special_tokens=a_						,						return_token_type_ids=a_			)
															A				:       Dict														=    tokenizer_p.encode_plus(a_						,						add_special_tokens=a_						,						return_token_type_ids=a_			)

															# token_type_ids should put 0 everywhere
															self.assertEqual(sum(tokens_r["token_type_ids"]			)						,						sum(tokens_p["token_type_ids"]			)			)

															# attention_mask should put 1 everywhere, so sum over length should be 1
															self.assertEqual(
															    sum(tokens_r["attention_mask"]			) / len(tokens_r["attention_mask"]			)						,						sum(tokens_p["attention_mask"]			) / len(tokens_p["attention_mask"]			)						,						)

															A				:       Optional[int]														=    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]			)
															A				:       int														=    tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]			)

															# Rust correctly handles the space before the mask while python doesnt
															self.assertSequenceEqual(tokens_p["input_ids"]						,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]			)
															self.assertSequenceEqual(tokens_r["input_ids"]						,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]			)

															self.assertSequenceEqual(
															    a_						,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]			)
															self.assertSequenceEqual(
															    a_						,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]			)




class 						UpperCAmelCase__     :




			'''simple docstring'''






			def __init__(  self				:					List[Any]			,			a_				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      data
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None

def 			a		( _UpperCAmelCase				:   BinaryTreeNode | None					,  _UpperCAmelCase				:   int		):







		'''simple docstring'''





		if node is None:
				__UpperCAmelCase       :	List[Any]						=      BinaryTreeNode(_UpperCAmelCase		)
				return node

		# binary search tree is not empty,
		# so we will insert it into the tree
		# if new_value is less than value of data in node,
		#  add it to left subtree and proceed recursively
		if new_value < node.data:
				__UpperCAmelCase       :	int						=      insert(node.left_child					,  _UpperCAmelCase		)
		else:
				# if new_value is greater than value of data in node,
				#  add it to right subtree and proceed recursively
				__UpperCAmelCase       :	str						=      insert(node.right_child					,  _UpperCAmelCase		)
		return node

def 			a		( _UpperCAmelCase				:   None | BinaryTreeNode		):  # if node is None,return







		'''simple docstring'''





		if node:
				__UpperCAmelCase       :	Optional[int]						=      inorder(node.left_child		)
				__UpperCAmelCase       :	int						=      [*inorder_array, node.data]
				__UpperCAmelCase       :	Optional[Any]						=      inorder_array + inorder(node.right_child		)
		else:
				__UpperCAmelCase       :	List[Any]						=      []
		return inorder_array

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Any						=      insert(_UpperCAmelCase					,  15		)
		insert(_UpperCAmelCase					,  10		)
		insert(_UpperCAmelCase					,  25		)
		insert(_UpperCAmelCase					,  6		)
		insert(_UpperCAmelCase					,  14		)
		insert(_UpperCAmelCase					,  20		)
		insert(_UpperCAmelCase					,  60		)
		return root

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      make_tree()
		print('''Printing values of binary search tree in Inorder Traversal.'''		)
		inorder(_UpperCAmelCase		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()





