





import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

UpperCAmelCase		:  List[str]         =	logging.get_logger(__name__)


UpperCAmelCase		:  Any         =	{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a   ,		a   ,		a    ) -> Optional[int]:
					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:
										__A      :		str         =							TOKENIZER_CLASSES
					else:
										__A      :		Optional[Any]         =							{tokenizer_name: getattr(__snake_case   ,		tokenizer_name + 'Fast'    )}

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

					for tokenizer_name in tokenizer_names:
										__A      :		Optional[int]         =							TOKENIZER_CLASSES[tokenizer_name]

										__A      :		str         =							True
										if checkpoint_name is None:
															__A      :		Union[str, Any]         =							list(tokenizer_class.max_model_input_sizes.keys()    )
										else:
															__A      :		Any         =							[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
															__A      :		List[Any]         =							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:
																				__A       ,							__A      :		Optional[int]         =							checkpoint.split('/'    )
																				__A      :		List[Any]         =							os.path.join(__snake_case   ,		__snake_case    )
															elif add_prefix:
																				__A      :		Union[str, Any]         =							checkpoint
																				__A      :		Union[str, Any]         =							dump_path
															else:
																				__A      :		List[str]         =							None
																				__A      :		List[Any]         =							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]:
																				__A      :		List[Any]         =							list(tokenizer.pretrained_vocab_files_map.values()    )[0][checkpoint]
																				__A      :		Tuple         =							file_path.split(__snake_case    )[-1][0]
																				if next_char == "/":
																									__A      :		int         =							os.path.join(__snake_case   ,		__snake_case    )
																									__A      :		List[Any]         =							None

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

															__A      :		Tuple         =							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__":
				UpperCAmelCase		:  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.''',
				)
				UpperCAmelCase		:  int         =	parser.parse_args()

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





'''simple docstring'''






import os
from pathlib import Path





def  _SCREAMING_SNAKE_CASE		(      ):
     from torch.utils.cpp_extension import load

     _A    		=  Path(__snake_case				).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
     _A    		=  [
         root / filename
         for filename in [
             'vision.cpp',
             os.path.join('cpu'   ,		'ms_deform_attn_cpu.cpp'				),
             os.path.join('cuda'   ,		'ms_deform_attn_cuda.cu'				),
         ]
     ]

     load(
         'MultiScaleDeformableAttention'   ,		__snake_case   ,		with_cuda=__snake_case   ,		extra_include_paths=[str(__snake_case				)]   ,		extra_cflags=['-DWITH_CUDA=1']   ,		extra_cuda_cflags=[
             '-DCUDA_HAS_FP16=1',
             '-D__CUDA_NO_HALF_OPERATORS__',
             '-D__CUDA_NO_HALF_CONVERSIONS__',
             '-D__CUDA_NO_HALF2_OPERATORS__',
         ]   ,		)

     import MultiScaleDeformableAttention as MSDA

     return MSDA



