




import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
__A						:							Optional[int]							=  logging.get_logger("""transformers.models.speecht5""")

__A						:							Optional[Any]							=  {
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
__A						:							List[Any]							=  {
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
__A						:							List[str]							=  {
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
__A						:							int							=  {
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
__A						:							Optional[int]							=  {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
__A						:							Optional[int]							=  {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
__A						:							Optional[Any]							=  {
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
__A						:							Dict							=  {
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
__A						:							List[str]							=  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__A						:							Dict							=  {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__A						:							Optional[int]							=  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__A						:							List[Any]							=  []
__A						:							Tuple							=  [
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
__A						:							str							=  IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
__A						:							Dict							=  IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
__A						:							Union[str, Any]							=  IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''



			for attribute in key.split("""."""   ):
						SCREAMING_SNAKE_CASE           =					getattr(_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE   )

			if weight_type is not None:
						SCREAMING_SNAKE_CASE           =					getattr(_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE   ).shape
			else:
						SCREAMING_SNAKE_CASE           =					hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
						    f""" {value.shape} for {full_name}"""   )

			if weight_type == "weight":
						SCREAMING_SNAKE_CASE           =					value
			elif weight_type == "weight_g":
						SCREAMING_SNAKE_CASE           =					value
			elif weight_type == "weight_v":
						SCREAMING_SNAKE_CASE           =					value
			elif weight_type == "bias":
						SCREAMING_SNAKE_CASE           =					value
			elif weight_type == "running_mean":
						SCREAMING_SNAKE_CASE           =					value
			elif weight_type == "running_var":
						SCREAMING_SNAKE_CASE           =					value
			elif weight_type == "num_batches_tracked":
						SCREAMING_SNAKE_CASE           =					value
			else:
						SCREAMING_SNAKE_CASE           =					value

			logger.info(f"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}."""   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''



			for key in ignore_keys:
						if key.endswith(""".*"""   ):
									if name.startswith(key[:-1]   ):
												return True
						elif ".*." in key:
									SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					key.split(""".*."""   )
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[]

			if task == "s2t":
						SCREAMING_SNAKE_CASE           =					hf_model.speechta.encoder.prenet.feature_encoder
						SCREAMING_SNAKE_CASE           =					MAPPING_S2T
						SCREAMING_SNAKE_CASE           =					IGNORE_KEYS_S2T
			elif task == "t2s":
						SCREAMING_SNAKE_CASE           =					None
						SCREAMING_SNAKE_CASE           =					MAPPING_T2S
						SCREAMING_SNAKE_CASE           =					IGNORE_KEYS_T2S
			elif task == "s2s":
						SCREAMING_SNAKE_CASE           =					hf_model.speechta.encoder.prenet.feature_encoder
						SCREAMING_SNAKE_CASE           =					MAPPING_S2S
						SCREAMING_SNAKE_CASE           =					IGNORE_KEYS_S2S
			else:
						raise ValueError(f"""Unsupported task: {task}"""   )

			for name, value in fairseq_dict.items():
						if should_ignore(_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE   ):
									logger.info(f"""{name} was ignored"""   )
									continue

						SCREAMING_SNAKE_CASE           =					False
						if "conv_layers" in name:
									load_conv_layer(
									    _SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE					,	hf_model.config.feat_extract_norm == """group"""					,	)
									SCREAMING_SNAKE_CASE           =					True
						else:
									for key, mapped_key in MAPPING.items():
												# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

												if "*" in key:
															SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					key.split(""".*."""   )
															if prefix in name and suffix in name:
																		SCREAMING_SNAKE_CASE           =					suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
												if key in name:
															SCREAMING_SNAKE_CASE           =					True
															if "*" in mapped_key:
																		SCREAMING_SNAKE_CASE           =					name.split(_SCREAMING_SNAKE_CASE   )[0].split("""."""   )[-2]
																		SCREAMING_SNAKE_CASE           =					mapped_key.replace("""*"""					,	_SCREAMING_SNAKE_CASE   )
															if "weight_g" in name:
																		SCREAMING_SNAKE_CASE           =					"""weight_g"""
															elif "weight_v" in name:
																		SCREAMING_SNAKE_CASE           =					"""weight_v"""
															elif "bias" in name:
																		SCREAMING_SNAKE_CASE           =					"""bias"""
															elif "weight" in name:
																		SCREAMING_SNAKE_CASE           =					"""weight"""
															elif "running_mean" in name:
																		SCREAMING_SNAKE_CASE           =					"""running_mean"""
															elif "running_var" in name:
																		SCREAMING_SNAKE_CASE           =					"""running_var"""
															elif "num_batches_tracked" in name:
																		SCREAMING_SNAKE_CASE           =					"""num_batches_tracked"""
															else:
																		SCREAMING_SNAKE_CASE           =					None
															set_recursively(_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE   )
												continue
						if not is_used:
									unused_weights.append(_SCREAMING_SNAKE_CASE   )

			logger.warning(f"""Unused weights: {unused_weights}"""   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					full_name.split("""conv_layers."""   )[-1]
			SCREAMING_SNAKE_CASE           =					name.split("""."""   )
			SCREAMING_SNAKE_CASE           =					int(items[0]   )
			SCREAMING_SNAKE_CASE           =					int(items[1]   )

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""   )
									SCREAMING_SNAKE_CASE           =					value
									logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""   )
									SCREAMING_SNAKE_CASE           =					value
									logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""   )
									SCREAMING_SNAKE_CASE           =					value
									logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""   )
									SCREAMING_SNAKE_CASE           =					value
									logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
			else:
						unused_weights.append(_SCREAMING_SNAKE_CASE   )
@torch.no_grad()
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=None					,	):
			'''simple docstring'''



			if config_path is not None:
						SCREAMING_SNAKE_CASE           =					SpeechTaConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
			else:
						SCREAMING_SNAKE_CASE           =					SpeechTaConfig()

			if task == "s2t":
						SCREAMING_SNAKE_CASE           =					config.max_text_positions
						SCREAMING_SNAKE_CASE           =					SpeechTaForSpeechToText(_SCREAMING_SNAKE_CASE   )
			elif task == "t2s":
						SCREAMING_SNAKE_CASE           =					18_76
						SCREAMING_SNAKE_CASE           =					6_00
						SCREAMING_SNAKE_CASE           =					config.max_speech_positions
						SCREAMING_SNAKE_CASE           =					SpeechTaForTextToSpeech(_SCREAMING_SNAKE_CASE   )
			elif task == "s2s":
						SCREAMING_SNAKE_CASE           =					18_76
						SCREAMING_SNAKE_CASE           =					config.max_speech_positions
						SCREAMING_SNAKE_CASE           =					SpeechTaForSpeechToSpeech(_SCREAMING_SNAKE_CASE   )
			else:
						raise ValueError(f"""Unknown task name: {task}"""   )

			if vocab_path:
						SCREAMING_SNAKE_CASE           =					SpeechTaTokenizer(_SCREAMING_SNAKE_CASE					,	model_max_length=config.max_text_positions   )

						# Mask token behaves like a normal word, i.e. include the space before it
						SCREAMING_SNAKE_CASE           =					AddedToken("""<mask>"""					,	lstrip=_SCREAMING_SNAKE_CASE					,	rstrip=_SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					mask_token
						tokenizer.add_special_tokens({"""mask_token""": mask_token}   )
						tokenizer.add_tokens(["""<ctc_blank>"""]   )

			SCREAMING_SNAKE_CASE           =					SpeechTaFeatureExtractor()
			SCREAMING_SNAKE_CASE           =					SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE					,	feature_extractor=_SCREAMING_SNAKE_CASE   )
			processor.save_pretrained(_SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					torch.load(_SCREAMING_SNAKE_CASE   )
			recursively_load_weights(fairseq_checkpoint["""model"""]					,	_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE   )

			model.save_pretrained(_SCREAMING_SNAKE_CASE   )

			if repo_id:
						print("""Pushing to the hub..."""   )
						processor.push_to_hub(_SCREAMING_SNAKE_CASE   )
						model.push_to_hub(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
		__A						:							Union[str, Any]							=  argparse.ArgumentParser()
		parser.add_argument(
		    """--task""",
		    default="""s2t""",
		    type=str,
		    help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
		)
		parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
		parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
		parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
		parser.add_argument(
		    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
		)
		parser.add_argument(
		    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
		)

		__A						:							List[Any]							=  parser.parse_args()
		convert_speechta_checkpoint(
		    args.task,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.vocab_path,
		    args.push_to_hub,
		)






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		__A						:							Tuple							=  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=5_1_2,
		    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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
					'''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						:							Union[str, Any]							=  parser.parse_args()

		__A						:							Any							=  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)






