




'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a       :						Optional[int]								=		logging.get_logger(__name__)

a       :						Tuple								=		{
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase				,     _UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			"""focalnet"""





			def __init__(      self						:    int      ,		a_						:    Optional[int]=224      ,		a_						:    int=4      ,		a_						:    Dict=3      ,		a_						:    Any=96      ,		a_						:    str=False      ,		a_						:    Optional[Any]=[192, 384, 768, 768]      ,		a_						:    Tuple=[2, 2, 6, 2]      ,		a_						:    Dict=[2, 2, 2, 2]      ,		a_						:    List[Any]=[3, 3, 3, 3]      ,		a_						:    Union[str, Any]="gelu"      ,		a_						:    Union[str, Any]=4.0      ,		a_						:    List[str]=0.0      ,		a_						:    int=0.1      ,		a_						:    List[Any]=False      ,		a_						:    Tuple=1e-4      ,		a_						:    Tuple=False      ,		a_						:    Dict=False      ,		a_						:    List[Any]=False      ,		a_						:    List[str]=0.02      ,		a_						:    Dict=1e-5      ,		a_						:    Union[str, Any]=32      ,		a_						:    List[str]=None      ,		a_						:    str=None      ,		**a_						:    Union[str, Any]      ,		):



						"""simple docstring"""




						super().__init__(**a_							)

						__snake_case			=		image_size
						__snake_case			=		patch_size
						__snake_case			=		num_channels
						__snake_case			=		embed_dim
						__snake_case			=		use_conv_embed
						__snake_case			=		hidden_sizes
						__snake_case			=		depths
						__snake_case			=		focal_levels
						__snake_case			=		focal_windows
						__snake_case			=		hidden_act
						__snake_case			=		mlp_ratio
						__snake_case			=		hidden_dropout_prob
						__snake_case			=		drop_path_rate
						__snake_case			=		use_layerscale
						__snake_case			=		layerscale_value
						__snake_case			=		use_post_layernorm
						__snake_case			=		use_post_layernorm_in_modulation
						__snake_case			=		normalize_modulator
						__snake_case			=		initializer_range
						__snake_case			=		layer_norm_eps
						__snake_case			=		encoder_stride
						__snake_case			=		["stem"] + [f'''stage{idx}''' for idx in range(1      ,		len(self.depths							) + 1							)]
						__snake_case		,     __snake_case			=		get_aligned_output_features_output_indices(
						    out_features=a_      ,		out_indices=a_      ,		stage_names=self.stage_names							)










import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase		       =      logging.get_logger(__name__)


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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Dict:
				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__	:							int      						= {tokenizer_name: getattr(__UpperCamelCase	,				tokenizer_name + "Fast" )}

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

				for tokenizer_name in tokenizer_names:
								a__	:							Any      						= TOKENIZER_CLASSES[tokenizer_name]

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

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

												a__	:							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__":
						lowerCamelCase		       =      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		       =      parser.parse_args()

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


