
import os

import numpy
import onnx






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= a.name
					SCREAMING_SNAKE_CASE__													= b.name

					SCREAMING_SNAKE_CASE__													= ''
					SCREAMING_SNAKE_CASE__													= ''

					SCREAMING_SNAKE_CASE__													= a == b

					SCREAMING_SNAKE_CASE__													= name_a
					SCREAMING_SNAKE_CASE__													= name_b

					return res






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					for i, input_name in enumerate(node_proto.input		):
										if input_name == name:
															node_proto.input.insert(UpperCamelCase_	,	UpperCamelCase_		)
															node_proto.input.pop(i + 1		)

					if node_proto.op_type == "If":
										_graph_replace_input_with(node_proto.attribute[0].g	,	UpperCamelCase_	,	UpperCamelCase_		)
										_graph_replace_input_with(node_proto.attribute[1].g	,	UpperCamelCase_	,	UpperCamelCase_		)
					if node_proto.op_type == "Loop":
										_graph_replace_input_with(node_proto.attribute[0].g	,	UpperCamelCase_	,	UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		Optional[Any]:
					'''simple docstring'''


					for n in graph_proto.node:
										_node_replace_input_with(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= list(model.graph.initializer		)
					SCREAMING_SNAKE_CASE__													= list(model_without_ext.graph.initializer		)
					for i, ref_i in ind_to_replace:
										assert inits_with_data[i].name == inits[i].name
										assert inits_with_data[ref_i].name == inits[ref_i].name
										assert i > ref_i

										SCREAMING_SNAKE_CASE__													= inits[i].name
										SCREAMING_SNAKE_CASE__													= inits[ref_i].name

										model_without_ext.graph.initializer.remove(inits[i]		)

										# for n in model.graph.node:
										_graph_replace_input_with(model_without_ext.graph	,	UpperCamelCase_	,	UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_		)		->		Optional[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= os.path.dirname(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= os.path.basename(UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= onnx.load(os.path.join(UpperCamelCase_	,	UpperCamelCase_		)		)

					SCREAMING_SNAKE_CASE__													= list(model.graph.initializer		)

					SCREAMING_SNAKE_CASE__													= set()
					SCREAMING_SNAKE_CASE__													= {}
					SCREAMING_SNAKE_CASE__													= []

					SCREAMING_SNAKE_CASE__													= 0

					for i in range(len(UpperCamelCase_		)		):
										if i in dup_set:
															continue

										for j in range(i + 1	,	len(UpperCamelCase_		)		):
															if j in dup_set:
																				continue
															if _is_equal_tensor_proto(inits[i]	,	inits[j]		):
																				dup_set.add(UpperCamelCase_		)
																				dup_set.add(UpperCamelCase_		)

																				SCREAMING_SNAKE_CASE__													= inits[j].data_type
																				SCREAMING_SNAKE_CASE__													= numpy.prod(inits[j].dims		)
																				if dtype == 1:
																									mem_size *= 4
																				elif dtype == 6:
																									mem_size *= 4
																				elif dtype == 7 or dtype == 11:
																									mem_size *= 8
																				else:
																									print('unexpected data type: '	,	UpperCamelCase_		)
																				total_reduced_size += mem_size

																				SCREAMING_SNAKE_CASE__													= inits[i].name
																				SCREAMING_SNAKE_CASE__													= inits[j].name

																				if name_i in dup_map:
																									dup_map[name_i].append(UpperCamelCase_		)
																				else:
																									SCREAMING_SNAKE_CASE__													= [name_j]
																				ind_to_replace.append((j, i)		)

					print('total reduced size: '	,	total_reduced_size / 1024 / 1024 / 1024	,	'GB'		)

					SCREAMING_SNAKE_CASE__													= sorted(UpperCamelCase_		)
					_remove_dup_initializers_from_model(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= 'optimized_' + model_file_name
					SCREAMING_SNAKE_CASE__													= os.path.join(UpperCamelCase_	,	UpperCamelCase_		)
					onnx.save(UpperCamelCase_	,	UpperCamelCase_		)

					return new_model







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
				__snake_case								=  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_12,
				    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 							_lowercase							(     UpperCamelCase_		)		->		Dict:
									'''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)

				__snake_case								=  parser.parse_args()

				__snake_case								=  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)





