


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 					lowerCAmelCase__	(    a					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			BartTokenizer
     __SCREAMING_SNAKE_CASE        =			BartTokenizerFast
     __SCREAMING_SNAKE_CASE        =			True
     __SCREAMING_SNAKE_CASE        =			filter_roberta_detectors


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

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


     def 			_lowerCamelCase			(						self			,						**__lowerCamelCase)      ->    str:
            kwargs.update(self.special_tokens_map)
            return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**__lowerCamelCase)


     def 			_lowerCamelCase			(						self			,						**__lowerCamelCase)      ->    Tuple:
            kwargs.update(self.special_tokens_map)
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,						**__lowerCamelCase)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    str:
            return "lower newer", "lower newer"


     @cached_property
     def 			_lowerCamelCase			(						self)      ->    Tuple:
            return BartTokenizer.from_pretrained("facebook/bart-large")


     @cached_property
     def 			_lowerCamelCase			(						self)      ->    str:
            return BartTokenizerFast.from_pretrained("facebook/bart-large")


     @require_torch
     def 			_lowerCamelCase			(						self)      ->    str:
            _A				:	Optional[int]       =			["A long paragraph for summarization.", "Another paragraph for summarization."]
            _A				:	Any       =			[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

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


     @require_torch
     def 			_lowerCamelCase			(						self)      ->    Tuple:
            _A				:	Tuple       =			["A long paragraph for summarization.", "Another paragraph for summarization."]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _A				:	Tuple       =			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 			_lowerCamelCase			(						self)      ->    List[Any]:
            _A				:	Union[str, Any]       =			[
                "Summary of the text.",
                "Another summary.",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _A				:	str       =			tokenizer(text_target=__lowerCamelCase			,						max_length=3_2			,						padding="max_length"			,						return_tensors="pt")
                   self.assertEqual(3_2			,						targets["input_ids"].shape[1])


     @require_torch
     def 			_lowerCamelCase			(						self)      ->    List[str]:
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _A				:	Dict       =			tokenizer(
                       ["I am a small frog" * 1_0_2_4, "I am a small frog"]			,						padding=__lowerCamelCase			,						truncation=__lowerCamelCase			,						return_tensors="pt")
                   self.assertIsInstance(__lowerCamelCase			,						__lowerCamelCase)
                   self.assertEqual(batch.input_ids.shape			,						(2, 1_0_2_4))


     @require_torch
     def 			_lowerCamelCase			(						self)      ->    Tuple:
            _A				:	Optional[int]       =			["A long paragraph for summarization."]
            _A				:	Any       =			[
                "Summary of the text.",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _A				:	List[str]       =			tokenizer(__lowerCamelCase			,						return_tensors="pt")
                   _A				:	Optional[Any]       =			tokenizer(text_target=__lowerCamelCase			,						return_tensors="pt")
                   _A				:	Optional[Any]       =			inputs["input_ids"]
                   _A				:	List[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 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            pass


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

                          _A				:	Optional[Any]       =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
                          _A				:	str       =			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, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])
                          self.assertSequenceEqual(tokens_r["input_ids"]			,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])

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




from itertools import permutations
def 							_UpperCAmelCase    ():
       _A				:	List[Any]       =			list(map("".join    ,							permutations("0123456789"						)						)						)
       return result[999999]


if __name__ == "__main__":
    print(solution())
