



def 	a						(				_UpperCAmelCase							: str						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[Any]													=     word.upper()
		__UpperCAmelCase   :					Any													=     0
		__UpperCAmelCase   :					Optional[Any]													=     {}
		__UpperCAmelCase   :					str													=     []

		for letter in word:
				if letter not in letter_nums:
						__UpperCAmelCase   :					List[Any]													=     str(_UpperCAmelCase						)
						next_num += 1
				word_pattern.append(letter_nums[letter]						)
		return ".".join(_UpperCAmelCase						)


if __name__ == "__main__":
				import pprint
				import time

				__A             =time.time()
				with open("dictionary.txt") as in_file:
								__A             =in_file.read().splitlines()

				__A             ={}
				for word in word_list:
								__A             =get_word_pattern(word)
								if pattern in all_patterns:
												all_patterns[pattern].append(word)
								else:
												__A             =[word]

				with open("word_patterns.txt", "w") as out_file:
								out_file.write(pprint.pformat(all_patterns))

				__A             =round(time.time() - start_time, 2)
				print(f'''Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.''')
				# Done!  9,581 word patterns found in 0.58 seconds.










import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir

@dataclass
class 		UpperCAmelCase__      :




			'''simple docstring'''






			UpperCamelCase      						=	42

class 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     get_tests_dir('''fixtures/test_sentencepiece.model'''			)
					__UpperCAmelCase   :					Union[str, Any]													=     get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''			)

					__UpperCAmelCase   :					List[str]													=     FakeOriginalTokenizer(vocab_file=a_			)

					with warnings.catch_warnings(record=a_			) as w:
							__UpperCAmelCase   :					Optional[int]													=     SpmConverter(a_			)
					self.assertEqual(len(a_			)	,	0			)

					__UpperCAmelCase   :					Optional[int]													=     FakeOriginalTokenizer(vocab_file=a_			)

					with warnings.catch_warnings(record=a_			) as w:
							__UpperCAmelCase   :					Dict													=     SpmConverter(a_			)
					self.assertEqual(len(a_			)	,	1			)

					self.assertIn(
					    '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
					    ''' which is not implemented in the fast tokenizers.'''	,	str(w[0].message			)	,	)





