



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   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				MvpTokenizer
       _lowerCamelCase			    =				MvpTokenizerFast
       _lowerCamelCase			    =				True
       _lowerCamelCase			    =				filter_roberta_detectors



       def  A_   (     self):

             """simple docstring"""





             super().setUp()
             UpperCAmelCase_  :	str				      =		[
                 "l",
                 "o",
                 "w",
                 "e",
                 "r",
                 "s",
                 "t",
                 "i",
                 "d",
                 "n",
                 "\u0120",
                 "\u0120l",
                 "\u0120n",
                 "\u0120lo",
                 "\u0120low",
                 "er",
                 "\u0120lowest",
                 "\u0120newer",
                 "\u0120wider",
                 "<unk>",
             ]
             UpperCAmelCase_  :	int				      =		dict(zip(lowercase     ,range(len(lowercase))))
             UpperCAmelCase_  :	Union[str, Any]				      =		["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
             UpperCAmelCase_  :	str				      =		{"unk_token": "<unk>"}

             UpperCAmelCase_  :	int				      =		os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["vocab_file"])
             UpperCAmelCase_  :	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):

             """simple docstring"""





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



       def  A_   (     self     ,**lowercase):

             """simple docstring"""





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



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return "lower newer", "lower newer"



       @cached_property
       def  A_   (     self):

             """simple docstring"""





             return MvpTokenizer.from_pretrained("RUCAIBox/mvp")



       @cached_property
       def  A_   (     self):

             """simple docstring"""





             return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")



       @require_torch
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		["A long paragraph for summarization.", "Another paragraph for summarization."]
             UpperCAmelCase_  :	str				      =		[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   UpperCAmelCase_  :	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)
                   UpperCAmelCase_  :	List[str]				      =		batch.input_ids.tolist()[0]
                   self.assertListEqual(lowercase     ,lowercase)
                   # Test that special tokens are reset



       @require_torch
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		["A long paragraph for summarization.", "Another paragraph for summarization."]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   UpperCAmelCase_  :	Any				      =		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):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		[
                 "Summary of the text.",
                 "Another summary.",
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   UpperCAmelCase_  :	List[Any]				      =		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):

             """simple docstring"""





             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   UpperCAmelCase_  :	int				      =		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):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		["A long paragraph for summarization."]
             UpperCAmelCase_  :	Any				      =		[
                 "Summary of the text.",
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   UpperCAmelCase_  :	str				      =		tokenizer(lowercase     ,text_target=lowercase     ,return_tensors="pt")
                   UpperCAmelCase_  :	Optional[int]				      =		inputs["input_ids"]
                   UpperCAmelCase_  :	Optional[Any]				      =		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):

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





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

                         UpperCAmelCase_  :	Tuple				      =		tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
                         UpperCAmelCase_  :	Tuple				      =		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>"])





def    _snake_case (       __snake_case					,  __snake_case    )     ->     int:






      '''simple docstring'''







      return int((input_a, input_a).count(0    ) != 0    )







def    _snake_case (       )     ->     None:






      '''simple docstring'''







      assert nand_gate(0					,  0    ) == 1
      assert nand_gate(0					,  1    ) == 1
      assert nand_gate(1					,  0    ) == 1
      assert nand_gate(1					,  1    ) == 0


if __name__ == "__main__":
   print(nand_gate(0, 0))
   print(nand_gate(0, 1))
   print(nand_gate(1, 0))
   print(nand_gate(1, 1))
