




"""simple docstring"""






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
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__      (	lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     MvpTokenizer
			lowerCamelCase__ =     MvpTokenizerFast
			lowerCamelCase__ =     True
			lowerCamelCase__ =     filter_roberta_detectors
			def    A_       (    self    ):
							super().setUp()
							_lowerCamelCase	:     List[Any]       =       [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							_lowerCamelCase	:     Dict       =       dict(zip(lowercase    , range(len(lowercase    )    )    )    )
							_lowerCamelCase	:     Tuple       =       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							_lowerCamelCase	:     Dict       =       {'unk_token': '<unk>'}

							_lowerCamelCase	:     Optional[Any]       =       os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES['vocab_file']    )
							_lowerCamelCase	:     Optional[int]       =       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(lowercase    ) + '\n'    )
							with open(self.merges_file    , 'w'    , encoding='utf-8'    ) as fp:
											fp.write('\n'.join(lowercase    )    )
			def    A_       (    self    , **lowercase    ):
							kwargs.update(self.special_tokens_map    )
							return self.tokenizer_class.from_pretrained(self.tmpdirname    , **lowercase    )
			def    A_       (    self    , **lowercase    ):
							kwargs.update(self.special_tokens_map    )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    , **lowercase    )
			def    A_       (    self    , lowercase    ):
							return "lower newer", "lower newer"
			@cached_property
			def    A_       (    self    ):
							return MvpTokenizer.from_pretrained('RUCAIBox/mvp'    )
			@cached_property
			def    A_       (    self    ):
							return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'    )
			@require_torch
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							_lowerCamelCase	:     List[Any]       =       [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_lowerCamelCase	:     Optional[Any]       =       tokenizer(lowercase    , max_length=len(lowercase    )    , padding=lowercase    , return_tensors='pt'    )
											self.assertIsInstance(lowercase    , lowercase    )

											self.assertEqual((2, 9)    , batch.input_ids.shape    )
											self.assertEqual((2, 9)    , batch.attention_mask.shape    )
											_lowerCamelCase	:     str       =       batch.input_ids.tolist()[0]
											self.assertListEqual(lowercase    , lowercase    )
											# Test that special tokens are reset
			@require_torch
			def    A_       (    self    ):
							_lowerCamelCase	:     Any       =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_lowerCamelCase	:     Optional[int]       =       tokenizer(lowercase    , padding=lowercase    , return_tensors='pt'    )
											# check if input_ids are returned and no labels
											self.assertIn('input_ids'    , lowercase    )
											self.assertIn('attention_mask'    , lowercase    )
											self.assertNotIn('labels'    , lowercase    )
											self.assertNotIn('decoder_attention_mask'    , lowercase    )
			@require_torch
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       [
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_lowerCamelCase	:     List[str]       =       tokenizer(text_target=lowercase    , max_length=32    , padding='max_length'    , return_tensors='pt'    )
											self.assertEqual(32    , targets['input_ids'].shape[1]    )
			@require_torch
			def    A_       (    self    ):
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_lowerCamelCase	:     Dict       =       tokenizer(
											    ['I am a small frog' * 1024, 'I am a small frog']    , padding=lowercase    , truncation=lowercase    , return_tensors='pt'    )
											self.assertIsInstance(lowercase    , lowercase    )
											self.assertEqual(batch.input_ids.shape    , (2, 1024)    )
			@require_torch
			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       ['A long paragraph for summarization.']
							_lowerCamelCase	:     List[str]       =       [
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_lowerCamelCase	:     Any       =       tokenizer(lowercase    , text_target=lowercase    , return_tensors='pt'    )
											_lowerCamelCase	:     List[str]       =       inputs['input_ids']
											_lowerCamelCase	:     str       =       inputs['labels']
											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    A_       (    self    ):
							pass






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

															# 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']    )    , )

															_lowerCamelCase	:     Dict       =       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']    )
															_lowerCamelCase	:     Union[str, Any]       =       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, 50264, 3823, 487, 21992, 3645, 4, 2]    )
															self.assertSequenceEqual(tokens_r['input_ids']    , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]    )

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





"""simple docstring"""






import re







def 	_snake_case     (							lowercase__						):

				if len(re.findall('[ATCG]'				,       lowercase__						)						) != len(lowercase__						):
								raise ValueError('Invalid Strand'						)

				return dna.translate(dna.maketrans('ATCG'				,       'TAGC'						)						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()