

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__												=						logging.get_logger(__name__)

a__												=						{
    '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
    '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json''',
    '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/config.json''',
    '''funnel-transformer/medium-base''': '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json''',
    '''funnel-transformer/intermediate''': (
        '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'''
    ),
    '''funnel-transformer/intermediate-base''': (
        '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'''
    ),
    '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/config.json''',
    '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json''',
    '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json''',
    '''funnel-transformer/xlarge-base''': '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json''',
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[int]             =       "funnel"
							UpperCAmelCase__			:		Tuple             =       {
							    "hidden_size": "d_model",
							    "num_attention_heads": "n_head",
							}


							def __init__(     self			,				_a=3_0_5_2_2			,				_a=[4, 4, 4]			,				_a=None			,				_a=2			,				_a=7_6_8			,				_a=1_2			,				_a=6_4			,				_a=3_0_7_2			,				_a="gelu_new"			,				_a=0.1			,				_a=0.1			,				_a=0.0			,				_a=0.1			,				_a=None			,				_a=1e-9			,				_a="mean"			,				_a="relative_shift"			,				_a=True			,				_a=True			,				_a=True			,				**_a			,				)       ->       List[Any]:
												_a      :       Optional[int]   =						vocab_size
												_a      :       Dict   =						block_sizes
												_a      :       Optional[int]   =						[1] * len(_a			) if block_repeats is None else block_repeats
												assert len(_a			) == len(
												    self.block_repeats			), "`block_sizes` and `block_repeats` should have the same length."
												_a      :       int   =						num_decoder_layers
												_a      :       List[str]   =						d_model
												_a      :       Optional[Any]   =						n_head
												_a      :       Tuple   =						d_head
												_a      :       Dict   =						d_inner
												_a      :       List[str]   =						hidden_act
												_a      :       int   =						hidden_dropout
												_a      :       Union[str, Any]   =						attention_dropout
												_a      :       Tuple   =						activation_dropout
												_a      :       Optional[Any]   =						initializer_range
												_a      :       Dict   =						initializer_std
												_a      :       Union[str, Any]   =						layer_norm_eps
												assert pooling_type in [
												    "mean",
												    "max",
												], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
												_a      :       Any   =						pooling_type
												assert attention_type in [
												    "relative_shift",
												    "factorized",
												], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
												_a      :       Optional[Any]   =						attention_type
												_a      :       int   =						separate_cls
												_a      :       Tuple   =						truncate_seq
												_a      :       List[Any]   =						pool_q_only

												super().__init__(**_a			)


							@property
							def 							__lowercase							(     self			)       ->       Tuple:
												return sum(self.block_sizes			)


							@num_hidden_layers.setter
							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												raise NotImplementedError(
												    '''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'''			)


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return len(self.block_sizes			)




							@num_blocks.setter
							def 							__lowercase							(     self			,				_a			)       ->       Dict:
												raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.'''			)






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()

			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
			)
			parser.add_argument(
			    '''--original_config_file''',
			    type=str,
			    required=True,
			    help='''The YAML config file corresponding to the original architecture.''',
			)
			parser.add_argument(
			    '''--num_in_channels''',
			    default=None,
			    type=int,
			    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
			)
			parser.add_argument(
			    '''--image_size''',
			    default=512,
			    type=int,
			    help=(
			        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
			        ''' Base. Use 768 for Stable Diffusion v2.'''
			    ),
			)
			parser.add_argument(
			    '''--extract_ema''',
			    action='''store_true''',
			    help=(
			        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
			        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
			        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
			    ),
			)
			parser.add_argument(
			    '''--upcast_attention''',
			    action='''store_true''',
			    help=(
			        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
			        ''' diffusion 2.1.'''
			    ),
			)
			parser.add_argument(
			    '''--from_safetensors''',
			    action='''store_true''',
			    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
			)
			parser.add_argument(
			    '''--to_safetensors''',
			    action='''store_true''',
			    help='''Whether to store pipeline in safetensors format or not.''',
			)
			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
			parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')



			def   __UpperCAmelCase					(      __a :     Any      )     ->	List[Any]:

								"""simple docstring"""

								if string == "True":
													return True
								elif string == "False":
													return False
								else:
													raise ValueError(F"""could not parse string as bool {string}"""      )

			parser.add_argument(
			    '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
			)

			parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

			a__												=						parser.parse_args()

			a__												=						download_controlnet_from_original_ckpt(
			    checkpoint_path=args.checkpoint_path,
			    original_config_file=args.original_config_file,
			    image_size=args.image_size,
			    extract_ema=args.extract_ema,
			    num_in_channels=args.num_in_channels,
			    upcast_attention=args.upcast_attention,
			    from_safetensors=args.from_safetensors,
			    device=args.device,
			    use_linear_projection=args.use_linear_projection,
			    cross_attention_dim=args.cross_attention_dim,
			)







			controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



