


'''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__						:   Union[str, Any]				=    logging.get_logger(__name__)


A__						:   Union[str, Any]				=    {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any] ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Any				)	->						Union[str, Any]:
     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:
          __lowerCamelCase  :      List[str]														=						TOKENIZER_CLASSES
     else:
          __lowerCamelCase  :      List[Any]														=						{tokenizer_name: getattr(UpperCAmelCase_ ,       tokenizer_name + 'Fast'				)}

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

     for tokenizer_name in tokenizer_names:
          __lowerCamelCase  :      Dict														=						TOKENIZER_CLASSES[tokenizer_name]

          __lowerCamelCase  :      Optional[Any]														=						True
          if checkpoint_name is None:
               __lowerCamelCase  :      Dict														=						list(tokenizer_class.max_model_input_sizes.keys()				)
          else:
               __lowerCamelCase  :      int														=						[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
               __lowerCamelCase  :      Any														=						tokenizer_class.from_pretrained(UpperCAmelCase_ ,       force_download=UpperCAmelCase_				)

               # 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:
                    __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						checkpoint.split('/'				)
                    __lowerCamelCase  :      List[Any]														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
               elif add_prefix:
                    __lowerCamelCase  :      int														=						checkpoint
                    __lowerCamelCase  :      Tuple														=						dump_path
               else:
                    __lowerCamelCase  :      List[str]														=						None
                    __lowerCamelCase  :      str														=						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]:
                    __lowerCamelCase  :      Any														=						list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
                    __lowerCamelCase  :      List[Any]														=						file_path.split(UpperCAmelCase_				)[-1][0]
                    if next_char == "/":
                         __lowerCamelCase  :      Tuple														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
                         __lowerCamelCase  :      str														=						None

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

               __lowerCamelCase  :      Union[str, Any]														=						tokenizer.save_pretrained(
                   UpperCAmelCase_ ,       legacy_format=UpperCAmelCase_ ,       filename_prefix=UpperCAmelCase_				)
               logger.info(F'=> File names {file_names}'				)

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


if __name__ == "__main__":
      A__						:   int				=    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__						:   List[str]				=    parser.parse_args()

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









"""simple docstring"""


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def   _A					(	*_a	:						int				):



      """simple docstring"""






      if not isinstance(_a     ,   _a				):
            A				  =		list(_a				)
      for i in range(len(_a				)				):
            A				  =		None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects



def   _A					(	_a	:						Exception				):



      """simple docstring"""






      A				  =		[
          """CUDA out of memory.""",  # CUDA OOM
          """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
          """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
      ]
      if isinstance(_a     ,   _a				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False





def   _A					(	_a	:						callable = None     ,   _a	:						int = 1_2_8				):



      """simple docstring"""






      if function is None:
            return functools.partial(_a     ,   starting_batch_size=_a				)

      A				  =		starting_batch_size

      def decorator(*_a	:						Union[str, Any]     ,   **_a	:						List[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            A				  =		list(inspect.signature(_a				).parameters.keys()				)
            # Guard against user error
            if len(_a				) < (len(_a				) + 1):
                  A				  =		""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]     ,   args[1:]				)]				)
                  raise TypeError(
                      f'Batch size was passed into `{function.__name__}` as the first argument when called.'
                      f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError("""No executable batch size found, reached zero."""				)
                  try:
                        return function(_a     ,   *_a     ,   **_a				)
                  except Exception as e:
                        if should_reduce_batch_size(_a				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator




