


"""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 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Optional[Any]				  =  BartTokenizer
							__magic_name__					:     Dict				  =  BartTokenizerFast
							__magic_name__					:     List[Any]				  =  True
							__magic_name__					:     Union[str, Any]				  =  filter_roberta_detectors




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




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

								snake_case__  :						int		=      os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES["vocab_file"])
								snake_case__  :						Tuple		=      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	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	Any:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								return "lower newer", "lower newer"




							@cached_property
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								return BartTokenizer.from_pretrained("facebook/bart-large")




							@cached_property
							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								return BartTokenizerFast.from_pretrained("facebook/bart-large")




							@require_torch
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						str		=      ["A long paragraph for summarization.", "Another paragraph for summarization."]
								snake_case__  :						Dict		=      [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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




							@require_torch
							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						str		=      ["A long paragraph for summarization.", "Another paragraph for summarization."]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									snake_case__  :						Optional[Any]		=      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)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Any		=      [
								    "Summary of the text.",
								    "Another summary.",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									snake_case__  :						Dict		=      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)   ->	Any:







								'''simple docstring'''




								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									snake_case__  :						Optional[Any]		=      tokenizer(
									    ["I am a small frog" * 1_024, "I am a small frog"]	,					padding=lowerCamelCase__	,					truncation=lowerCamelCase__	,					return_tensors="pt")
									self.assertIsInstance(lowerCamelCase__	,					lowerCamelCase__)
									self.assertEqual(batch.input_ids.shape	,					(2, 1_024))




							@require_torch
							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Tuple		=      ["A long paragraph for summarization."]
								snake_case__  :						Optional[Any]		=      [
								    "Summary of the text.",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									snake_case__  :						Optional[int]		=      tokenizer(lowerCamelCase__	,					return_tensors="pt")
									snake_case__  :						List[Any]		=      tokenizer(text_target=lowerCamelCase__	,					return_tensors="pt")
									snake_case__  :						Any		=      inputs["input_ids"]
									snake_case__  :						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        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								pass







							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




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

										snake_case__  :						Tuple		=      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
										snake_case__  :						Optional[int]		=      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(
										    lowerCamelCase__	,					["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
										self.assertSequenceEqual(
										    lowerCamelCase__	,					["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])










'''simple docstring'''





def     UpperCAmelCase     (    a_ = 9_9_8_0_0_1		)   ->       int:



   """simple docstring"""






   for number in range(n - 1      ,		9_9_9_9      ,		-1		):
      A_  :		Optional[Any]						=					str(a_		)

      # checks whether 'str_number' is a palindrome.
      if str_number == str_number[::-1]:
         A_  :		int						=					9_9_9

         # if 'number' is a product of two 3-digit numbers
         # then number is the answer otherwise fetch next number.
         while divisor != 9_9:
            if (number % divisor == 0) and (len(str(number // divisor		)		) == 3.0):
               return number
            divisor -= 1
   raise ValueError("""That number is larger than our acceptable range."""		)


if __name__ == "__main__":
  print(f'{solution() = }')
