
"""simple docstring"""





import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_a								=						logging.get_logger(__name__)


_a								=						{name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
										raise ValueError(F'''Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()      )}.'''      )

					if tokenizer_name is None:
										_UpperCamelCase									=    TOKENIZER_CLASSES
					else:
										_UpperCamelCase									=    {tokenizer_name: getattr(__snake_case,	tokenizer_name + '''Fast'''      )}

					logger.info(F'''Loading tokenizer classes: {tokenizer_names}'''      )

					for tokenizer_name in tokenizer_names:
										_UpperCamelCase									=    TOKENIZER_CLASSES[tokenizer_name]

										_UpperCamelCase									=    True
										if checkpoint_name is None:
															_UpperCamelCase									=    list(tokenizer_class.max_model_input_sizes.keys()      )
										else:
															_UpperCamelCase									=    [checkpoint_name]

										logger.info(F'''For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'''      )

										for checkpoint in checkpoint_names:
															logger.info(F'''Loading {tokenizer_class.__class__.__name__} {checkpoint}'''      )

															# Load tokenizer
															_UpperCamelCase									=    tokenizer_class.from_pretrained(__snake_case,	force_download=__snake_case      )

															# Save fast tokenizer
															logger.info(F'''Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'''      )

															# For organization names we create sub-directories
															if "/" in checkpoint:
																				_UpperCamelCase					,    _UpperCamelCase									=    checkpoint.split('''/'''      )
																				_UpperCamelCase									=    os.path.join(__snake_case,	__snake_case      )
															elif add_prefix:
																				_UpperCamelCase									=    checkpoint
																				_UpperCamelCase									=    dump_path
															else:
																				_UpperCamelCase									=    None
																				_UpperCamelCase									=    dump_path

															logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''      )

															if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()      )[0]:
																				_UpperCamelCase									=    list(tokenizer.pretrained_vocab_files_map.values()      )[0][checkpoint]
																				_UpperCamelCase									=    file_path.split(__snake_case      )[-1][0]
																				if next_char == "/":
																									_UpperCamelCase									=    os.path.join(__snake_case,	__snake_case      )
																									_UpperCamelCase									=    None

																				logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'''      )

															_UpperCamelCase									=    tokenizer.save_pretrained(
															    __snake_case,	legacy_format=__snake_case,	filename_prefix=__snake_case      )
															logger.info(F'''=> File names {file_names}'''      )

															for file_name in file_names:
																				if not file_name.endswith('''tokenizer.json'''      ):
																									os.remove(__snake_case      )
																									logger.info(F'''=> removing {file_name}'''      )


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
				)
				parser.add_argument(
				    """--tokenizer_name""",
				    default=None,
				    type=str,
				    help=(
				        F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
				        """download and convert all the checkpoints from AWS."""
				    ),
				)
				parser.add_argument(
				    """--checkpoint_name""",
				    default=None,
				    type=str,
				    help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
				)
				parser.add_argument(
				    """--force_download""",
				    action="""store_true""",
				    help="""Re-download checkpoints.""",
				)
				_a								=						parser.parse_args()

				convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)








"""simple docstring"""





from __future__ import annotations

from functools import lru_cache
from math import ceil

_a								=						100

_a								=						set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a								=						42

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=1_00      )
def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					if number_to_partition < 0:
										return set()
					elif number_to_partition == 0:
										return {1}

					_UpperCamelCase									=    set()
					_UpperCamelCase									=    42
					_UpperCamelCase									=    42

					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  lowerCamelCase__						(     __snake_case = 50_00      )				->						int | None:


					"""simple docstring"""





					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() = }""")






