


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 						snake_case			(       lowerCAmelCase__	, unittest.TestCase	):
			'''simple docstring'''

			UpperCAmelCase       :       Any       = BartTokenizer
			UpperCAmelCase       :       str       = BartTokenizerFast
			UpperCAmelCase       :       Any       = True
			UpperCAmelCase       :       Optional[int]       = filter_roberta_detectors

			def _lowercase (	self     :     Tuple					)					->					Dict:




								"""simple docstring"""






								super().setUp()
								SCREAMING_SNAKE_CASE_	      =       [
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''\u0120''',
								    '''\u0120l''',
								    '''\u0120n''',
								    '''\u0120lo''',
								    '''\u0120low''',
								    '''er''',
								    '''\u0120lowest''',
								    '''\u0120newer''',
								    '''\u0120wider''',
								    '''<unk>''',
								]
								SCREAMING_SNAKE_CASE_	      =       dict(zip(lowerCAmelCase_							,							range(len(lowerCAmelCase_					)					)					)					)
								SCREAMING_SNAKE_CASE_	      =       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								SCREAMING_SNAKE_CASE_	      =       {'''unk_token''': '''<unk>'''}

								SCREAMING_SNAKE_CASE_	      =       os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES['''vocab_file''']					)
								SCREAMING_SNAKE_CASE_	      =       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(lowerCAmelCase_					) + '''\n'''					)
								with open(self.merges_file							,							'''w'''							,							encoding='''utf-8'''					) as fp:
													fp.write('''\n'''.join(lowerCAmelCase_					)					)

			def _lowercase (	self     :     List[str]							,							**lowerCAmelCase_     :     Optional[int]					)					->					Union[str, Any]:




								"""simple docstring"""






								kwargs.update(self.special_tokens_map					)
								return self.tokenizer_class.from_pretrained(self.tmpdirname							,							**lowerCAmelCase_					)

			def _lowercase (	self     :     Any							,							**lowerCAmelCase_     :     Optional[int]					)					->					int:




								"""simple docstring"""






								kwargs.update(self.special_tokens_map					)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,							**lowerCAmelCase_					)

			def _lowercase (	self     :     List[str]							,							lowerCAmelCase_     :     List[str]					)					->					int:




								"""simple docstring"""






								return "lower newer", "lower newer"

			@cached_property
			def _lowercase (	self     :     List[str]					)					->					List[str]:




								"""simple docstring"""






								return BartTokenizer.from_pretrained('''facebook/bart-large'''					)

			@cached_property
			def _lowercase (	self     :     str					)					->					Dict:




								"""simple docstring"""






								return BartTokenizerFast.from_pretrained('''facebook/bart-large'''					)

			@require_torch
			def _lowercase (	self     :     Optional[int]					)					->					Optional[int]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								SCREAMING_SNAKE_CASE_	      =       [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													SCREAMING_SNAKE_CASE_	      =       tokenizer(lowerCAmelCase_							,							max_length=len(lowerCAmelCase_					)							,							padding=lowerCAmelCase_							,							return_tensors='''pt'''					)
													self.assertIsInstance(lowerCAmelCase_							,							lowerCAmelCase_					)

													self.assertEqual((2, 9)							,							batch.input_ids.shape					)
													self.assertEqual((2, 9)							,							batch.attention_mask.shape					)
													SCREAMING_SNAKE_CASE_	      =       batch.input_ids.tolist()[0]
													self.assertListEqual(lowerCAmelCase_							,							lowerCAmelCase_					)
													# Test that special tokens are reset

			@require_torch
			def _lowercase (	self     :     Dict					)					->					List[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													SCREAMING_SNAKE_CASE_	      =       tokenizer(lowerCAmelCase_							,							padding=lowerCAmelCase_							,							return_tensors='''pt'''					)
													# check if input_ids are returned and no labels
													self.assertIn('''input_ids'''							,							lowerCAmelCase_					)
													self.assertIn('''attention_mask'''							,							lowerCAmelCase_					)
													self.assertNotIn('''labels'''							,							lowerCAmelCase_					)
													self.assertNotIn('''decoder_attention_mask'''							,							lowerCAmelCase_					)

			@require_torch
			def _lowercase (	self     :     List[Any]					)					->					Dict:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       [
								    '''Summary of the text.''',
								    '''Another summary.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													SCREAMING_SNAKE_CASE_	      =       tokenizer(text_target=lowerCAmelCase_							,							max_length=32							,							padding='''max_length'''							,							return_tensors='''pt'''					)
													self.assertEqual(32							,							targets['''input_ids'''].shape[1]					)

			@require_torch
			def _lowercase (	self     :     Optional[int]					)					->					List[str]:




								"""simple docstring"""






								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													SCREAMING_SNAKE_CASE_	      =       tokenizer(
													    ['''I am a small frog''' * 1_024, '''I am a small frog''']							,							padding=lowerCAmelCase_							,							truncation=lowerCAmelCase_							,							return_tensors='''pt'''					)
													self.assertIsInstance(lowerCAmelCase_							,							lowerCAmelCase_					)
													self.assertEqual(batch.input_ids.shape							,							(2, 1_024)					)

			@require_torch
			def _lowercase (	self     :     Any					)					->					Union[str, Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       ['''A long paragraph for summarization.''']
								SCREAMING_SNAKE_CASE_	      =       [
								    '''Summary of the text.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													SCREAMING_SNAKE_CASE_	      =       tokenizer(lowerCAmelCase_							,							return_tensors='''pt'''					)
													SCREAMING_SNAKE_CASE_	      =       tokenizer(text_target=lowerCAmelCase_							,							return_tensors='''pt'''					)
													SCREAMING_SNAKE_CASE_	      =       inputs['''input_ids''']
													SCREAMING_SNAKE_CASE_	      =       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 _lowercase (	self     :     List[Any]					)					->					Union[str, Any]:




								"""simple docstring"""






								pass




			def _lowercase (	self     :     List[str]					)					->					int:




								"""simple docstring"""






								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
																		SCREAMING_SNAKE_CASE_	      =       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_							,							**lowerCAmelCase_					)
																		SCREAMING_SNAKE_CASE_	      =       self.tokenizer_class.from_pretrained(lowerCAmelCase_							,							**lowerCAmelCase_					)
																		SCREAMING_SNAKE_CASE_	      =       '''A, <mask> AllenNLP sentence.'''
																		SCREAMING_SNAKE_CASE_	      =       tokenizer_r.encode_plus(lowerCAmelCase_							,							add_special_tokens=lowerCAmelCase_							,							return_token_type_ids=lowerCAmelCase_					)
																		SCREAMING_SNAKE_CASE_	      =       tokenizer_p.encode_plus(lowerCAmelCase_							,							add_special_tokens=lowerCAmelCase_							,							return_token_type_ids=lowerCAmelCase_					)

																		# 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''']					)							,							)

																		SCREAMING_SNAKE_CASE_	      =       tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']					)
																		SCREAMING_SNAKE_CASE_	      =       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, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]					)
																		self.assertSequenceEqual(tokens_r['''input_ids''']							,							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]					)

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






def 							UpperCAmelCase				(     UpperCAmelCase      )->		bool:







					'''simple docstring'''



					if not isinstance(UpperCAmelCase				,UpperCAmelCase      ):
										SCREAMING_SNAKE_CASE_	      =       f'''Input value of [number={number}] must be an integer'''
										raise TypeError(UpperCAmelCase      )
					if number < 0 or number % 2 == 1:
										return False
					SCREAMING_SNAKE_CASE_	      =       int(number**0.5      )
					return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
		import doctest

		doctest.testmod()


