




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor




class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							def __init__(					self       :	Optional[Any]       ,     a       :	int       ,     a       :	str=13       ,     a       :	int=3       ,     a       :	Union[str, Any]=224       ,     a       :	Union[str, Any]=30       ,     a       :	Union[str, Any]=400       ,     a       :	Optional[Any]=True       ,     a       :	Tuple=None       ,     a       :	List[Any]=True       ,     a       :	Optional[Any]=[0.5, 0.5, 0.5]       ,     a       :	List[Any]=[0.5, 0.5, 0.5]       ,     )			->					str:
										SCREAMING_SNAKE_CASE           =					size if size is not None else {"""height""": 18, """width""": 18}
										SCREAMING_SNAKE_CASE           =					parent
										SCREAMING_SNAKE_CASE           =					batch_size
										SCREAMING_SNAKE_CASE           =					num_channels
										SCREAMING_SNAKE_CASE           =					image_size
										SCREAMING_SNAKE_CASE           =					min_resolution
										SCREAMING_SNAKE_CASE           =					max_resolution
										SCREAMING_SNAKE_CASE           =					do_resize
										SCREAMING_SNAKE_CASE           =					size
										SCREAMING_SNAKE_CASE           =					do_normalize
										SCREAMING_SNAKE_CASE           =					image_mean
										SCREAMING_SNAKE_CASE           =					image_std







							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[int]:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}





@require_torch
@require_vision
class 			UpperCAmelCase_      (   A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     ViTImageProcessor if is_vision_available() else None





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					EfficientFormerImageProcessorTester(self			)





							@property
							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[Any]:
										return self.image_proc_tester.prepare_image_processor_dict()





							def    _UpperCAmelCase     (					self       :	Any			)			->					Any:
										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(a       ,     """image_mean"""			)			)
										self.assertTrue(hasattr(a       ,     """image_std"""			)			)
										self.assertTrue(hasattr(a       ,     """do_normalize"""			)			)
										self.assertTrue(hasattr(a       ,     """do_resize"""			)			)
										self.assertTrue(hasattr(a       ,     """size"""			)			)





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Optional[int]:
										pass





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					List[str]:
										# Initialize image_processor
										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										SCREAMING_SNAKE_CASE           =					prepare_image_inputs(self.image_proc_tester       ,     equal_resolution=a			)
										for image in image_inputs:
													self.assertIsInstance(a       ,     Image.Image			)

										# Test not batched input
										SCREAMING_SNAKE_CASE           =					image_processor(image_inputs[0]       ,     return_tensors="""pt"""			).pixel_values
										self.assertEqual(
										    encoded_images.shape       ,     (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )       ,     )

										# Test batched
										SCREAMING_SNAKE_CASE           =					image_processor(a       ,     return_tensors="""pt"""			).pixel_values
										self.assertEqual(
										    encoded_images.shape       ,     (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )       ,     )





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					List[str]:
										# Initialize image_processor
										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										SCREAMING_SNAKE_CASE           =					prepare_image_inputs(self.image_proc_tester       ,     equal_resolution=a       ,     numpify=a			)
										for image in image_inputs:
													self.assertIsInstance(a       ,     np.ndarray			)

										# Test not batched input
										SCREAMING_SNAKE_CASE           =					image_processor(image_inputs[0]       ,     return_tensors="""pt"""			).pixel_values
										self.assertEqual(
										    encoded_images.shape       ,     (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )       ,     )

										# Test batched
										SCREAMING_SNAKE_CASE           =					image_processor(a       ,     return_tensors="""pt"""			).pixel_values
										self.assertEqual(
										    encoded_images.shape       ,     (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )       ,     )







							def    _UpperCAmelCase     (					self       :	str			)			->					int:
										# Initialize image_processor
										SCREAMING_SNAKE_CASE           =					self.image_processing_class(**self.image_processor_dict			)
										# create random PyTorch tensors
										SCREAMING_SNAKE_CASE           =					prepare_image_inputs(self.image_proc_tester       ,     equal_resolution=a       ,     torchify=a			)
										for image in image_inputs:
													self.assertIsInstance(a       ,     torch.Tensor			)

										# Test not batched input
										SCREAMING_SNAKE_CASE           =					image_processor(image_inputs[0]       ,     return_tensors="""pt"""			).pixel_values
										self.assertEqual(
										    encoded_images.shape       ,     (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )       ,     )

										# Test batched
										SCREAMING_SNAKE_CASE           =					image_processor(a       ,     return_tensors="""pt"""			).pixel_values
										self.assertEqual(
										    encoded_images.shape       ,     (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["""height"""],
										        self.image_proc_tester.size["""width"""],
										    )       ,     )












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)






