

import warnings

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


a__												=						logging.get_logger(__name__)

a__												=						{
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}

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







							UpperCAmelCase__			:		Tuple             =       "xlnet"
							UpperCAmelCase__			:		Dict             =       ["mems"]
							UpperCAmelCase__			:		str             =       {
							    "n_token": "vocab_size",  # Backward compatibility
							    "hidden_size": "d_model",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}


							def __init__(     self			,				_a=3_2_0_0_0			,				_a=1_0_2_4			,				_a=2_4			,				_a=1_6			,				_a=4_0_9_6			,				_a="gelu"			,				_a=True			,				_a="bi"			,				_a=0.02			,				_a=1e-1_2			,				_a=0.1			,				_a=5_1_2			,				_a=None			,				_a=True			,				_a=False			,				_a=False			,				_a=-1			,				_a=False			,				_a="last"			,				_a=True			,				_a="tanh"			,				_a=0.1			,				_a=5			,				_a=5			,				_a=5			,				_a=1			,				_a=2			,				**_a			,				)       ->       int:
												_a      :       Any   =						vocab_size
												_a      :       Any   =						d_model
												_a      :       Any   =						n_layer
												_a      :       Optional[Any]   =						n_head
												if d_model % n_head != 0:
																	raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""			)
												if "d_head" in kwargs:
																	if kwargs["d_head"] != d_model // n_head:
																						raise ValueError(
																						    F"""`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"""			)
												_a      :       Tuple   =						d_model // n_head
												_a      :       int   =						ff_activation
												_a      :       str   =						d_inner
												_a      :       int   =						untie_r
												_a      :       List[str]   =						attn_type

												_a      :       Union[str, Any]   =						initializer_range
												_a      :       Union[str, Any]   =						layer_norm_eps

												_a      :       Tuple   =						dropout
												_a      :       Optional[int]   =						mem_len
												_a      :       Union[str, Any]   =						reuse_len
												_a      :       Dict   =						bi_data
												_a      :       List[str]   =						clamp_len
												_a      :       Tuple   =						same_length

												_a      :       List[Any]   =						summary_type
												_a      :       Tuple   =						summary_use_proj
												_a      :       Dict   =						summary_activation
												_a      :       Dict   =						summary_last_dropout
												_a      :       int   =						start_n_top
												_a      :       int   =						end_n_top

												_a      :       Any   =						bos_token_id
												_a      :       Optional[Any]   =						pad_token_id
												_a      :       int   =						eos_token_id

												if "use_cache" in kwargs:
																	warnings.warn(
																	    '''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
																	    ''' instead.'''			,				_a			,				)
																	_a      :       Optional[Any]   =						kwargs['''use_cache''']

												_a      :       List[str]   =						use_mems_eval
												_a      :       List[Any]   =						use_mems_train
												super().__init__(pad_token_id=_a			,				bos_token_id=_a			,				eos_token_id=_a			,				**_a			)


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit."""			)
												return -1




							@max_position_embeddings.setter
							def 							__lowercase							(     self			,				_a			)       ->       Tuple:
												# Message copied from Transformer-XL documentation
												raise NotImplementedError(
												    F"""The model {self.model_type} is one of the few models that has no sequence length limit."""			)






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)



