





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        _a(    UpperCAmelCase__	,   unittest.TestCase    ):
		lowerCamelCase__     :Any   				=		LEDTokenizer
		lowerCamelCase__     :int   				=		LEDTokenizerFast
		lowerCamelCase__     :Dict   				=		True







		def        lowercase       (    self				)      ->      List[str]:


							'''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       :					Tuple				       =   ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							_snake_case       :					Optional[int]				       =   {"unk_token": "<unk>"}

							_snake_case       :					Tuple				       =   os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES["vocab_file"]				)
							_snake_case       :					str				       =   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        lowercase       (    self ,						**__snake_case				)      ->      Any:


							'''simple docstring'''

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







		def        lowercase       (    self ,						**__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

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







		def        lowercase       (    self ,						__snake_case				)      ->      List[Any]:


							'''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				)      ->      Any:


							'''simple docstring'''

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







		@require_torch
		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					int				       =   ["A long paragraph for summarization.", "Another paragraph for summarization."]
							_snake_case       :					Dict				       =   [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]:
												_snake_case       :					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__				)







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


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   ["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"				)
												self.assertIn("input_ids" ,						lowerCamelCase__				)
												self.assertIn("attention_mask" ,						lowerCamelCase__				)
												self.assertNotIn("labels" ,						lowerCamelCase__				)
												self.assertNotIn("decoder_attention_mask" ,						lowerCamelCase__				)







		@require_torch
		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					str				       =   [
							    "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=3_2 ,						padding="max_length" ,						return_tensors="pt"				)
												self.assertEqual(3_2 ,						targets["input_ids"].shape[1]				)







		@require_torch
		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												_snake_case       :					Union[str, Any]				       =   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, 5_1_2_2)				)







		@require_torch
		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

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


							'''simple docstring'''

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

												_snake_case       :					List[str]				       =   tokenizer(lowerCamelCase__ ,						padding=lowerCamelCase__				)
												_snake_case       :					List[Any]				       =   [[0] * len(lowerCamelCase__				) for x in encoded_output["input_ids"]]
												_snake_case       :					Tuple				       =   tokenizer.pad(lowerCamelCase__				)
												self.assertSequenceEqual(outputs["global_attention_mask"] ,						lowerCamelCase__				)







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							pass







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


							'''simple docstring'''

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

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






from math import factorial

__lowerCAmelCase :dict[str, int]     =    {str(digit): factorial(digit) for digit in range(10)}







def    A				(							UpperCAmelCase				):
					if not isinstance(UpperCAmelCase  ,  UpperCAmelCase				):
										raise TypeError("Parameter number must be int"				)

					if number < 0:
										raise ValueError("Parameter number must be greater than or equal to 0"				)

					# Converts number in string to iterate on its digits and adds its factorial.
					return sum(DIGIT_FACTORIAL[digit] for digit in str(UpperCAmelCase				)				)







def    A				(							UpperCAmelCase = 60  ,  UpperCAmelCase = 1_000_000				):

					if not isinstance(UpperCAmelCase  ,  UpperCAmelCase				) or not isinstance(UpperCAmelCase  ,  UpperCAmelCase				):
										raise TypeError("Parameters chain_length and number_limit must be int"				)

					if chain_length <= 0 or number_limit <= 0:
										raise ValueError(
										    "Parameters chain_length and number_limit must be greater than 0"				)

					# the counter for the chains with the exact desired length
					_snake_case       :					Optional[int]				       =   0
					# the cached sizes of the previous chains
					_snake_case       :					dict[int, int]				       =   {}

					for start_chain_element in range(1  ,  UpperCAmelCase				):
										# The temporary set will contain the elements of the chain
										_snake_case       :					Union[str, Any]				       =   set()
										_snake_case       :					Tuple				       =   0

										# Stop computing the chain when you find a cached size, a repeating item or the
										# length is greater then the desired one.
										_snake_case       :					str				       =   start_chain_element
										while (
										    chain_element not in chain_sets_lengths
										    and chain_element not in chain_set
										    and chain_set_length <= chain_length
										):
															chain_set.add(UpperCAmelCase				)
															chain_set_length += 1
															_snake_case       :					Optional[Any]				       =   digit_factorial_sum(UpperCAmelCase				)

										if chain_element in chain_sets_lengths:
															chain_set_length += chain_sets_lengths[chain_element]

										_snake_case       :					List[Any]				       =   chain_set_length

										# If chain contains the exact amount of elements increase the counter
										if chain_set_length == chain_length:
															chains_counter += 1

					return chains_counter


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(F'''{solution()}''')