



'''simple docstring'''


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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







@require_tokenizers
class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


	'''simple docstring'''




	__magic_name__					: Any  =      LEDTokenizer
	__magic_name__					: Dict  =      LEDTokenizerFast
	__magic_name__					: Union[str, Any]  =      True




	def       lowercase_  (   self) ->      int:



						"""simple docstring"""







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

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




	def       lowercase_  (   self							,  **lowerCAmelCase_) ->      int:



						"""simple docstring"""







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




	def       lowercase_  (   self							,  **lowerCAmelCase_) ->      List[Any]:



						"""simple docstring"""







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




	def       lowercase_  (   self							,  lowerCAmelCase_) ->      Dict:



						"""simple docstring"""







						return "lower newer", "lower newer"




	@cached_property
	def       lowercase_  (   self) ->      str:



						"""simple docstring"""







						return LEDTokenizer.from_pretrained("allenai/led-base-16384")




	@cached_property
	def       lowercase_  (   self) ->      Optional[int]:



						"""simple docstring"""







						return LEDTokenizerFast.from_pretrained("allenai/led-base-16384")




	@require_torch
	def       lowercase_  (   self) ->      str:



						"""simple docstring"""







						a_       		=["A long paragraph for summarization.", "Another paragraph for summarization."]
						a_       		=[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_       		=tokenizer(A__							,  max_length=len(A__)							,  padding=A__							,  return_tensors="pt")
											self.assertIsInstance(A__							,  A__)

											self.assertEqual((2, 9)							,  batch.input_ids.shape)
											self.assertEqual((2, 9)							,  batch.attention_mask.shape)
											a_       		=batch.input_ids.tolist()[0]
											self.assertListEqual(A__							,  A__)




	@require_torch
	def       lowercase_  (   self) ->      Any:



						"""simple docstring"""







						a_       		=["A long paragraph for summarization.", "Another paragraph for summarization."]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											a_       		=tokenizer(A__							,  padding=A__							,  return_tensors="pt")
											self.assertIn("input_ids"							,  A__)
											self.assertIn("attention_mask"							,  A__)
											self.assertNotIn("labels"							,  A__)
											self.assertNotIn("decoder_attention_mask"							,  A__)




	@require_torch
	def       lowercase_  (   self) ->      Optional[Any]:



						"""simple docstring"""







						a_       		=[
						    "Summary of the text.",
						    "Another summary.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											a_       		=tokenizer(text_target=A__							,  max_length=3_2							,  padding="max_length"							,  return_tensors="pt")
											self.assertEqual(3_2							,  targets["input_ids"].shape[1])




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



						"""simple docstring"""







						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											a_       		=tokenizer(
											    ["I am a small frog" * 1_0_2_4, "I am a small frog"]							,  padding=A__							,  truncation=A__							,  return_tensors="pt")
											self.assertIsInstance(A__							,  A__)
											self.assertEqual(batch.input_ids.shape							,  (2, 5_1_2_2))




	@require_torch
	def       lowercase_  (   self) ->      Union[str, Any]:



						"""simple docstring"""







						a_       		=["A long paragraph for summarization."]
						a_       		=[
						    "Summary of the text.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											a_       		=tokenizer(A__							,  return_tensors="pt")
											a_       		=tokenizer(text_target=A__							,  return_tensors="pt")
											a_       		=inputs["input_ids"]
											a_       		=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())




	@require_torch
	def       lowercase_  (   self) ->      int:



						"""simple docstring"""







						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											a_       		=["Summary of the text.", "Another summary."]
											a_       		=[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

											a_       		=tokenizer(A__							,  padding=A__)
											a_       		=[[0] * len(A__) for x in encoded_output["input_ids"]]
											a_       		=tokenizer.pad(A__)
											self.assertSequenceEqual(outputs["global_attention_mask"]							,  A__)




	def       lowercase_  (   self) ->      Tuple:



						"""simple docstring"""







						pass




	def       lowercase_  (   self) ->      Dict:



						"""simple docstring"""







						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
																a_       		=self.rust_tokenizer_class.from_pretrained(A__							,  **A__)
																a_       		=self.tokenizer_class.from_pretrained(A__							,  **A__)
																a_       		="A, <mask> AllenNLP sentence."
																a_       		=tokenizer_r.encode_plus(A__							,  add_special_tokens=A__							,  return_token_type_ids=A__)
																a_       		=tokenizer_p.encode_plus(A__							,  add_special_tokens=A__							,  return_token_type_ids=A__)
																self.assertEqual(sum(tokens_r["token_type_ids"])							,  sum(tokens_p["token_type_ids"]))
																self.assertEqual(
																    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])							,  sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])							,  )

																a_       		=tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
																a_       		=tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
																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(
																    A__							,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
																self.assertSequenceEqual(
																    A__							,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])







'''simple docstring'''


from __future__ import annotations






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		=str(lowercase__       )
     return len(lowercase__       ) == 9 and set(lowercase__       ) == set("123456789"       )






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     for base_num in range(9_9_9_9       ,	4_9_9_9       ,	-1       ):
          a_       		=1_0_0_0_0_2 * base_num
          if is_9_pandigital(lowercase__       ):
               return candidate

     for base_num in range(3_3_3       ,	9_9       ,	-1       ):
          a_       		=1_0_0_2_0_0_3 * base_num
          if is_9_pandigital(lowercase__       ):
               return candidate

     return None


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


