






'''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 _lowercase      (							_UpperCAmelCase							, unittest.TestCase						):
							a							 =			BartTokenizer
							a							 =			BartTokenizerFast
							a							 =			True
							a							 =			filter_roberta_detectors




							def 						lowerCamelCase_							(				self:							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(__UpperCAmelCase     ,				range(len(__UpperCAmelCase	)	)	)	)
														lowerCamelCase__   :      List[str]			    =			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
														lowerCamelCase__   :      List[str]			    =			{'unk_token': '<unk>'}

														lowerCamelCase__   :      Union[str, 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(__UpperCAmelCase	) + """\n"""	)
														with open(self.merges_file     ,				"""w"""     ,				encoding="""utf-8"""	) as fp:
																					fp.write("""\n""".join(__UpperCAmelCase	)	)




							def 						lowerCamelCase_							(				self:							Any     ,				**UpperCamelCase__:							int	):
														kwargs.update(self.special_tokens_map	)
														return self.tokenizer_class.from_pretrained(self.tmpdirname     ,				**__UpperCAmelCase	)




							def 						lowerCamelCase_							(				self:							Dict     ,				**UpperCamelCase__:							int	):
														kwargs.update(self.special_tokens_map	)
														return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,				**__UpperCAmelCase	)




							def 						lowerCamelCase_							(				self:							List[Any]     ,				UpperCamelCase__:							str	):
														return "lower newer", "lower newer"




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




							@cached_property
							def 						lowerCamelCase_							(				self:							List[str]	):
														return BartTokenizerFast.from_pretrained("""facebook/bart-large"""	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														lowerCamelCase__   :      int			    =			['A long paragraph for summarization.', 'Another paragraph for summarization.']
														lowerCamelCase__   :      List[str]			    =			[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Dict			    =			tokenizer(__UpperCAmelCase     ,				max_length=len(__UpperCAmelCase	)     ,				padding=__UpperCAmelCase     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(__UpperCAmelCase     ,				__UpperCAmelCase	)

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




							@require_torch
							def 						lowerCamelCase_							(				self:							Dict	):
														lowerCamelCase__   :      str			    =			['A long paragraph for summarization.', 'Another paragraph for summarization.']
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      List[str]			    =			tokenizer(__UpperCAmelCase     ,				padding=__UpperCAmelCase     ,				return_tensors="""pt"""	)
																					# check if input_ids are returned and no labels
																					self.assertIn("""input_ids"""     ,				__UpperCAmelCase	)
																					self.assertIn("""attention_mask"""     ,				__UpperCAmelCase	)
																					self.assertNotIn("""labels"""     ,				__UpperCAmelCase	)
																					self.assertNotIn("""decoder_attention_mask"""     ,				__UpperCAmelCase	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														lowerCamelCase__   :      Any			    =			[
														    'Summary of the text.',
														    'Another summary.',
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      str			    =			tokenizer(text_target=__UpperCAmelCase     ,				max_length=32     ,				padding="""max_length"""     ,				return_tensors="""pt"""	)
																					self.assertEqual(32     ,				targets["""input_ids"""].shape[1]	)




							@require_torch
							def 						lowerCamelCase_							(				self:							List[str]	):
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Tuple			    =			tokenizer(
																					    ["""I am a small frog""" * 1_024, """I am a small frog"""]     ,				padding=__UpperCAmelCase     ,				truncation=__UpperCAmelCase     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(__UpperCAmelCase     ,				__UpperCAmelCase	)
																					self.assertEqual(batch.input_ids.shape     ,				(2, 1_024)	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														lowerCamelCase__   :      List[Any]			    =			['A long paragraph for summarization.']
														lowerCamelCase__   :      Dict			    =			[
														    'Summary of the text.',
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Dict			    =			tokenizer(__UpperCAmelCase     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      Optional[Any]			    =			tokenizer(text_target=__UpperCAmelCase     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      Tuple			    =			inputs['input_ids']
																					lowerCamelCase__   :      Dict			    =			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 						lowerCamelCase_							(				self:							Union[str, Any]	):
														pass




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

																												# 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__   :      Union[str, Any]			    =			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, 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(
																												    __UpperCAmelCase     ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)
																												self.assertSequenceEqual(
																												    __UpperCAmelCase     ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)





"""simple docstring"""






from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest


@custom_tokenizers
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					int:
     return True






  def 							lowercase_     (						self						)				->					int:
     a   :	Optional[int]						=    CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'						)
     a   :	Any						=    'Hugging Face大法好，谁用谁知道。'
     a   :	Tuple						=    'Hugging Face大法好,谁用谁知道。<unk>'
     a   :	Optional[int]						=    '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

     a   :	Optional[int]						=    tokenizer.tokenize(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Optional[Any]						=    tokens + [tokenizer.unk_token]

     a   :	str						=    [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase						)  ,					__UpperCAmelCase						)

     a   :	List[Any]						=    tokenizer.decode(__UpperCAmelCase						)
     self.assertEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)





