


import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_lowercase							:   Dict						=						logging.get_logger(__name__)


_lowercase							:   Optional[int]						=						{name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}





def 	_lowerCAmelCase					( UpperCamelCase__:  int				,       UpperCamelCase__:  List[str]				,       UpperCamelCase__:  Tuple				,       UpperCamelCase__:  str    )			-> Dict:





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

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

	for tokenizer_name in tokenizer_names:
		A             =					TOKENIZER_CLASSES[tokenizer_name]

		A             =					True
		if checkpoint_name is None:
			A             =					list(tokenizer_class.max_model_input_sizes.keys()    )
		else:
			A             =					[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             =					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:
				A       ,      A             =					checkpoint.split("""/"""    )
				A             =					os.path.join(UpperCamelCase__				,       UpperCamelCase__    )
			elif add_prefix:
				A             =					checkpoint
				A             =					dump_path
			else:
				A             =					None
				A             =					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(tokenizer.pretrained_vocab_files_map.values()    )[0][checkpoint]
				A             =					file_path.split(UpperCamelCase__    )[-1][0]
				if next_char == "/":
					A             =					os.path.join(UpperCamelCase__				,       UpperCamelCase__    )
					A             =					None

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

			A             =					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__":
				_lowercase							:   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.",
				)
				_lowercase							:   int						=						parser.parse_args()

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







_lowercase							:   Dict						=						{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}



