



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=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        a						(      _UpperCAmelCase							:					str						):







						'''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)










import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
				import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class 		UpperCAmelCase__			(			unittest.TestCase						,__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	load_tool('''text-to-speech''' )
					self.tool.setup()




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     List[Any]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     Union[str, Any]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     Optional[int]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     List[str]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )





