"""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()

lowerCAmelCase_:					Any     =					logging.get_logger(__name__)


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







def 					__a   (		A    ,				A    ,				A    ,				A      ):






     '''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:
          lowercase__				 =							TOKENIZER_CLASSES
     else:
          lowercase__				 =							{tokenizer_name: getattr(A    ,				tokenizer_name + "Fast"      )}

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

     for tokenizer_name in tokenizer_names:
          lowercase__				 =							TOKENIZER_CLASSES[tokenizer_name]

          lowercase__				 =							True
          if checkpoint_name is None:
               lowercase__				 =							list(tokenizer_class.max_model_input_sizes.keys()      )
          else:
               lowercase__				 =							[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
               lowercase__				 =							tokenizer_class.from_pretrained(A    ,				force_download=A      )

               # 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:
                    lowercase__ ,						lowercase__				 =							checkpoint.split("/"      )
                    lowercase__				 =							os.path.join(A    ,				A      )
               elif add_prefix:
                    lowercase__				 =							checkpoint
                    lowercase__				 =							dump_path
               else:
                    lowercase__				 =							None
                    lowercase__				 =							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]:
                    lowercase__				 =							list(tokenizer.pretrained_vocab_files_map.values()      )[0][checkpoint]
                    lowercase__				 =							file_path.split(A      )[-1][0]
                    if next_char == "/":
                         lowercase__				 =							os.path.join(A    ,				A      )
                         lowercase__				 =							None

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

               lowercase__				 =							tokenizer.save_pretrained(
                   A    ,				legacy_format=A    ,				filename_prefix=A      )
               logger.info(f'''=> File names {file_names}'''      )

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


if __name__ == "__main__":
 lowerCAmelCase_:					List[str]     =					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.",
 )
 lowerCAmelCase_:					Union[str, Any]     =					parser.parse_args()

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





"""simple docstring"""


from typing import Any

import numpy as np







def 					__a   (		A      ):






     '''simple docstring'''



     return np.array_equal(A    ,				matrix.conjugate().T      )







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							v.conjugate().T
     lowercase__				 =							v_star.dot(A      )
     assert isinstance(A    ,				np.ndarray      )
     return (v_star_dot.dot(A      )) / (v_star.dot(A      ))







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]      )
     lowercase__				 =							np.array([[1], [2], [3]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     print(rayleigh_quotient(A    ,				A      )      )

     lowercase__				 =							np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     assert rayleigh_quotient(A    ,				A      ) == float(3      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 tests()




