




import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     UpperCamelCase__         =      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      _a				(						SCREAMING_SNAKE_CASE_							:    List[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)

     UpperCamelCase__         =      parser.parse_args()

     UpperCamelCase__         =      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 hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image
else:
					class       a__   :






											@staticmethod
											def     __SCREAMING_SNAKE_CASE(       *_A		,      **_A      ):




																"""simple docstring"""



																pass







def      _a				(						SCREAMING_SNAKE_CASE_							:    Image					):
					__lowerCAmelCase           =	hashlib.mda(image.tobytes()					)
					return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       a__   ( unittest.TestCase				):
						_a							:   Tuple =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	DepthEstimationPipeline(model=_A		,      image_processor=_A      )
											return depth_estimator, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
											self.assertEqual({"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )}		,      _A      )
											import datasets

											__lowerCAmelCase           =	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"		,      "image"		,      split="test"      )
											__lowerCAmelCase           =	depth_estimator(
											    [
											        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      ),
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											        # RGBA
											        dataset[0]["file"],
											        # LA
											        dataset[1]["file"],
											        # L
											        dataset[2]["file"],
											    ]      )
											self.assertEqual(
											    [
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											    ]		,      _A		,      )






						@require_tf
						@unittest.skip("Depth estimation is not implemented in TF"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@slow
						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"Intel/dpt-large"
											__lowerCAmelCase           =	pipeline("depth-estimation"		,      model=_A      )
											__lowerCAmelCase           =	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"      )
											__lowerCAmelCase           =	hashimage(outputs["depth"]      )

											# This seems flaky.
											# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
											self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()      )		,      29.3_04      )
											self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()      )		,      2.6_62      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"      )




