



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 		lowerCAmelCase_ (			__snake_case ,       unittest.TestCase ):







		"""simple docstring"""
		UpperCAmelCase__													=       None
		UpperCAmelCase__													=       BloomTokenizerFast
		UpperCAmelCase__													=       BloomTokenizerFast
		UpperCAmelCase__													=       True
		UpperCAmelCase__													=       False
		UpperCAmelCase__													=       "tokenizer_file"
		UpperCAmelCase__													=       {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
		def 						__lowercase(		self			)     ->  Optional[int]:
									super().setUp()
									__UpperCamelCase          =  BloomTokenizerFast.from_pretrained('bigscience/tokenizer'			)
									tokenizer.save_pretrained(self.tmpdirname			)
		def 						__lowercase(		self     ,    **_SCREAMING_SNAKE_CASE			)     ->  int:
									kwargs.update(self.special_tokens_map			)
									return BloomTokenizerFast.from_pretrained(self.tmpdirname     ,    **__UpperCamelCase			)
		def 						__lowercase(		self			)     ->  str:
									__UpperCamelCase          =  self.get_rust_tokenizer()

									__UpperCamelCase          =  ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
									__UpperCamelCase          =  [[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

									__UpperCamelCase          =  tokenizer.batch_encode_plus(__UpperCamelCase			)['input_ids']
									self.assertListEqual(__UpperCamelCase     ,    __UpperCamelCase			)

									__UpperCamelCase          =  tokenizer.batch_decode(__UpperCamelCase			)
									self.assertListEqual(__UpperCamelCase     ,    __UpperCamelCase			)
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE=6			)     ->  Dict:


									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
																							__UpperCamelCase          =  self.rust_tokenizer_class.from_pretrained(__UpperCamelCase     ,    **__UpperCamelCase			)
																							# tokenizer_r.pad_token = None # Hotfixing padding = None
																							# Simple input
																							__UpperCamelCase          =  'This is a simple input'
																							__UpperCamelCase          =  ['This is a simple input 1', 'This is a simple input 2']
																							__UpperCamelCase          =  ('This is a simple input', 'This is a pair')
																							__UpperCamelCase          =  [
																							    ('This is a simple input 1', 'This is a simple input 2'),
																							    ('This is a simple pair 1', 'This is a simple pair 2'),
																							]

																							# Simple input tests
																							try:
																														tokenizer_r.encode(__UpperCamelCase     ,    max_length=__UpperCamelCase			)
																														tokenizer_r.encode_plus(__UpperCamelCase     ,    max_length=__UpperCamelCase			)

																														tokenizer_r.batch_encode_plus(__UpperCamelCase     ,    max_length=__UpperCamelCase			)
																														tokenizer_r.encode(__UpperCamelCase     ,    max_length=__UpperCamelCase			)
																														tokenizer_r.batch_encode_plus(__UpperCamelCase     ,    max_length=__UpperCamelCase			)
																							except ValueError:
																														self.fail('Bloom Tokenizer should be able to deal with padding'			)

																							__UpperCamelCase          =  None  # Hotfixing padding = None
																							self.assertRaises(__UpperCamelCase     ,    tokenizer_r.encode     ,    __UpperCamelCase     ,    max_length=__UpperCamelCase     ,    padding='max_length'			)

																							# Simple input
																							self.assertRaises(__UpperCamelCase     ,    tokenizer_r.encode_plus     ,    __UpperCamelCase     ,    max_length=__UpperCamelCase     ,    padding='max_length'			)

																							# Simple input
																							self.assertRaises(
																							    __UpperCamelCase     ,    tokenizer_r.batch_encode_plus     ,    __UpperCamelCase     ,    max_length=__UpperCamelCase     ,    padding='max_length'     ,    )

																							# Pair input
																							self.assertRaises(__UpperCamelCase     ,    tokenizer_r.encode     ,    __UpperCamelCase     ,    max_length=__UpperCamelCase     ,    padding='max_length'			)

																							# Pair input
																							self.assertRaises(__UpperCamelCase     ,    tokenizer_r.encode_plus     ,    __UpperCamelCase     ,    max_length=__UpperCamelCase     ,    padding='max_length'			)

																							# Pair input
																							self.assertRaises(
																							    __UpperCamelCase     ,    tokenizer_r.batch_encode_plus     ,    __UpperCamelCase     ,    max_length=__UpperCamelCase     ,    padding='max_length'     ,    )
		def 						__lowercase(		self			)     ->  int:
									__UpperCamelCase          =  self.get_rust_tokenizer()
									__UpperCamelCase          =  load_dataset('xnli'     ,    'all_languages'     ,    split='test'     ,    streaming=__UpperCamelCase			)

									__UpperCamelCase          =  next(iter(__UpperCamelCase			)			)['premise']  # pick up one data
									__UpperCamelCase          =  list(sample_data.values()			)

									__UpperCamelCase          =  list(map(tokenizer.encode     ,    __UpperCamelCase			)			)
									__UpperCamelCase          =  [tokenizer.decode(__UpperCamelCase     ,    clean_up_tokenization_spaces=__UpperCamelCase			) for x in output_tokens]
									self.assertListEqual(__UpperCamelCase     ,    __UpperCamelCase			)



		def 						__lowercase(		self			)     ->  Optional[int]:
									self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map			)     ,    1			)
									self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()			)[0]			)     ,    1			)






from itertools import count





def a(lowercase__ = 50						):





	'''simple docstring'''
	snake_case_   		=			[1] * min_block_length

	for n in count(lowercase__						):
		fill_count_functions.append(1						)

		for block_length in range(lowercase__      ,			n + 1						):
			for block_start in range(n - block_length						):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1000000:
			break

	return n


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