
'''simple docstring'''







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					lowercase					:							Optional[int]   =      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        SCREAMING_SNAKE_CASE__			(     __A			) ->			Union[str, Any]:
						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)

					lowercase					:							Tuple   =      parser.parse_args()

					lowercase					:							Tuple   =      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)



'''simple docstring'''







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 GLPNImageProcessor


class 		__UpperCAmelCase   (      unittest.TestCase		):


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=7					,     lowerCAmelCase_=3					,     lowerCAmelCase_=18					,     lowerCAmelCase_=30					,     lowerCAmelCase_=4_00					,     lowerCAmelCase_=True					,     lowerCAmelCase_=32					,     lowerCAmelCase_=True					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	num_channels
								_snake_case     		=	image_size
								_snake_case     		=	min_resolution
								_snake_case     		=	max_resolution
								_snake_case     		=	do_resize
								_snake_case     		=	size_divisor
								_snake_case     		=	do_rescale




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 		__UpperCAmelCase   (      _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= GLPNImageProcessor if is_vision_available() else None


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	GLPNImageProcessingTester(self						)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_resize'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'size_divisor'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'resample'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_rescale'						)						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     Image.Image						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_					,     numpify=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     np.ndarray						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_					,     torchify=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     torch.Tensor						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)

