






'''simple docstring'''





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class       snake_case__	( a_			,							unittest.TestCase):
					a_								= BlenderbotSmallTokenizer
					a_								= False




					def 						A	(   self   :						List[Any]	)  ->				List[Any]:
								super().setUp()

								UpperCAmelCase_	:    str	  =    ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
								UpperCAmelCase_	:    Optional[int]	  =    dict(zip(_a							,      range(len(_a	)	)	)	)

								UpperCAmelCase_	:    int	  =    ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
								UpperCAmelCase_	:    Dict	  =    {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

								UpperCAmelCase_	:    Optional[Any]	  =    os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES['''vocab_file''']	)
								UpperCAmelCase_	:    Optional[Any]	  =    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 						A	(   self   :						List[str]							,      **_A   :						Tuple	)  ->				Any:
								kwargs.update(self.special_tokens_map	)
								return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname							,      **_a	)




					def 						A	(   self   :						Optional[Any]							,      _A   :						Tuple	)  ->				List[Any]:
								UpperCAmelCase_	:    Optional[Any]	  =    "adapt act apte"
								UpperCAmelCase_	:    Any	  =    "adapt act apte"
								return input_text, output_text




					def 						A	(   self   :						Tuple	)  ->				Union[str, Any]:
								UpperCAmelCase_	:    Union[str, Any]	  =    BlenderbotSmallTokenizer(self.vocab_file							,      self.merges_file							,      **self.special_tokens_map	)
								UpperCAmelCase_	:    Any	  =    "adapt act apte"
								UpperCAmelCase_	:    int	  =    ["adapt", "act", "ap@@", "te"]
								UpperCAmelCase_	:    Optional[Any]	  =    tokenizer.tokenize(_a	)
								self.assertListEqual(_a							,      _a	)

								UpperCAmelCase_	:    Dict	  =    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

								UpperCAmelCase_	:    List[Any]	  =    [0, 1, 2, 3, 4, 5]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_a	)							,      _a	)




					def 						A	(   self   :						Optional[int]	)  ->				List[Any]:
								UpperCAmelCase_	:    List[str]	  =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''	)
								assert tok('''sam'''	).input_ids == [13_84]
								UpperCAmelCase_	:    Any	  =    "I am a small frog."
								UpperCAmelCase_	:    Tuple	  =    tok([src_text]							,      padding=_a							,      truncation=_a	)["input_ids"]
								UpperCAmelCase_	:    List[Any]	  =    tok.batch_decode(_a							,      skip_special_tokens=_a							,      clean_up_tokenization_spaces=_a	)[0]
								assert src_text != decoded  # I wish it did!
								assert decoded == "i am a small frog ."





					def 						A	(   self   :						List[str]	)  ->				Dict:
								UpperCAmelCase_	:    List[Any]	  =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''	)
								UpperCAmelCase_	:    Union[str, Any]	  =    "I am a small frog ."
								UpperCAmelCase_	:    int	  =    "."
								UpperCAmelCase_	:    Optional[Any]	  =    tok(_a	)["input_ids"]
								UpperCAmelCase_	:    Optional[int]	  =    tok(_a	)["input_ids"]

								assert encoded[-1] == encoded_dot[0]




"""simple docstring"""




from __future__ import annotations

from functools import lru_cache
from math import ceil

_A			:       Optional[Any]   		=       1_00

_A			:       Optional[int]   		=       set(range(3, NUM_PRIMES, 2))
primes.add(2)
_A			:       int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
   if prime not in primes:
      continue
   primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))


@lru_cache(maxsize=100  )
def   __magic_name__	(      __snake_case				:       int  ) ->				set[int]:
       if number_to_partition < 0:
              return set()
       elif number_to_partition == 0:
              return {1}

       lowercase    :				set[int]       		=							set()
       lowercase    :				int
       lowercase    :				int

       for prime in primes:
              if prime > number_to_partition:
                     continue
              for sub in partition(number_to_partition - prime  ):
                     ret.add(sub * prime  )

       return ret


def   __magic_name__	(      __snake_case				:       int = 5000  ) ->				int | None:
       for number_to_partition in range(1     , __snake_case  ):
              if len(partition(__snake_case  )  ) > number_unique_partitions:
                     return number_to_partition
       return None


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


