


"""simple docstring"""


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 	__UpperCamelCase    (    A__      ,    unittest.TestCase	):
					lowercase_						:							List[str]			=     BartTokenizer
					lowercase_						:							Optional[Any]			=     BartTokenizerFast
					lowercase_						:							Dict			=     True
					lowercase_						:							int			=     filter_roberta_detectors
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					Union[str, Any]:
											super().setUp()
											lowerCAmelCase      :Optional[int]				  = [
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											lowerCAmelCase      :List[str]				  = dict(zip(_lowerCAmelCase    ,   range(len(_lowerCAmelCase      )      )      )      )
											lowerCAmelCase      :Any				  = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											lowerCAmelCase      :List[Any]				  = {'unk_token': '<unk>'}

											lowerCAmelCase      :Optional[Any]				  = os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES['vocab_file']      )
											lowerCAmelCase      :Dict				  = 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 					UpperCAmelCase__   (       self       :  Union[str, Any]    ,   **UpperCAmelCase       :  Any      )  ->					Any:
											kwargs.update(self.special_tokens_map      )
											return self.tokenizer_class.from_pretrained(self.tmpdirname    ,   **_lowerCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Tuple    ,   **UpperCAmelCase       :  Optional[int]      )  ->					Optional[int]:
											kwargs.update(self.special_tokens_map      )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,   **_lowerCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]    ,   UpperCAmelCase       :  Any      )  ->					Optional[int]:
											return "lower newer", "lower newer"
					@cached_property
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					List[str]:
											return BartTokenizer.from_pretrained('facebook/bart-large'      )
					@cached_property
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					Any:
											return BartTokenizerFast.from_pretrained('facebook/bart-large'      )
					@require_torch
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					List[Any]:
											lowerCAmelCase      :Union[str, Any]				  = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											lowerCAmelCase      :Tuple				  = [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowerCAmelCase      :Optional[Any]				  = 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      )
																	lowerCAmelCase      :Dict				  = batch.input_ids.tolist()[0]
																	self.assertListEqual(_lowerCAmelCase    ,   _lowerCAmelCase      )
																	# Test that special tokens are reset
					@require_torch
					def 					UpperCAmelCase__   (       self       :  List[str]      )  ->					Union[str, Any]:
											lowerCAmelCase      :Dict				  = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowerCAmelCase      :Optional[int]				  = 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 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					int:
											lowerCAmelCase      :Union[str, Any]				  = [
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowerCAmelCase      :Optional[Any]				  = tokenizer(text_target=_lowerCAmelCase    ,   max_length=32    ,   padding='max_length'    ,   return_tensors='pt'      )
																	self.assertEqual(32    ,   targets['input_ids'].shape[1]      )
					@require_torch
					def 					UpperCAmelCase__   (       self       :  int      )  ->					Optional[Any]:
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowerCAmelCase      :int				  = tokenizer(
																	    ['I am a small frog' * 1024, 'I am a small frog']    ,   padding=_lowerCAmelCase    ,   truncation=_lowerCAmelCase    ,   return_tensors='pt'      )
																	self.assertIsInstance(_lowerCAmelCase    ,   _lowerCAmelCase      )
																	self.assertEqual(batch.input_ids.shape    ,   (2, 1024)      )
					@require_torch
					def 					UpperCAmelCase__   (       self       :  str      )  ->					List[Any]:
											lowerCAmelCase      :List[Any]				  = ['A long paragraph for summarization.']
											lowerCAmelCase      :str				  = [
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowerCAmelCase      :int				  = tokenizer(_lowerCAmelCase    ,   return_tensors='pt'      )
																	lowerCAmelCase      :Union[str, Any]				  = tokenizer(text_target=_lowerCAmelCase    ,   return_tensors='pt'      )
																	lowerCAmelCase      :Optional[Any]				  = inputs['input_ids']
																	lowerCAmelCase      :Union[str, Any]				  = 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 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					Tuple:
											pass
					def 					UpperCAmelCase__   (       self       :  Tuple      )  ->					Optional[Any]:
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																							lowerCAmelCase      :Optional[Any]				  = self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase    ,   **_lowerCAmelCase      )
																							lowerCAmelCase      :List[str]				  = self.tokenizer_class.from_pretrained(_lowerCAmelCase    ,   **_lowerCAmelCase      )
																							lowerCAmelCase      :Dict				  = 'A, <mask> AllenNLP sentence.'
																							lowerCAmelCase      :int				  = tokenizer_r.encode_plus(_lowerCAmelCase    ,   add_special_tokens=_lowerCAmelCase    ,   return_token_type_ids=_lowerCAmelCase      )
																							lowerCAmelCase      :Optional[int]				  = 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']      )    ,   )

																							lowerCAmelCase      :int				  = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
																							lowerCAmelCase      :List[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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )
																							self.assertSequenceEqual(tokens_r['input_ids']    ,   [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )

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







"""simple docstring"""


import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir




@dataclass
class 	__UpperCamelCase    :
					lowercase_						:							str




class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :List[str]				  = get_tests_dir('fixtures/test_sentencepiece.model'      )
											lowerCAmelCase      :Tuple				  = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'      )

											lowerCAmelCase      :List[str]				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :Dict				  = SpmConverter(UpperCAmelCase      )
											self.assertEqual(len(UpperCAmelCase      )    ,   0      )

											lowerCAmelCase      :int				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :List[str]				  = SpmConverter(UpperCAmelCase      )
											self.assertEqual(len(UpperCAmelCase      )    ,   1      )

											self.assertIn(
											    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
											    ' which is not implemented in the fast tokenizers.'    ,   str(w[0].message      )    ,   )