






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor







class  _lowercase   (   unittest.TestCase						):
						def __init__(     self				,			a				,			a=7				,			a=3				,			a=3_0				,			a=4_0_0				,			a=True				,			a=None				,			a=True				,			a=[0.5, 0.5, 0.5]				,			a=[0.5, 0.5, 0.5]				,			a=True				,			a=1 / 2_5_5				,			a=True				,			):
							snake_case__  :    Dict							       =size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
							snake_case__  :    str							       =parent
							snake_case__  :    Optional[Any]							       =batch_size
							snake_case__  :    Any							       =num_channels
							snake_case__  :    str							       =min_resolution
							snake_case__  :    Union[str, Any]							       =max_resolution
							snake_case__  :    str							       =do_resize
							snake_case__  :    List[str]							       =size
							snake_case__  :    List[Any]							       =do_normalize
							snake_case__  :    int							       =image_mean
							snake_case__  :    Optional[Any]							       =image_std
							snake_case__  :    str							       =do_rescale
							snake_case__  :    Dict							       =rescale_factor
							snake_case__  :    Union[str, Any]							       =do_pad
						def 						lowercase__					(     self   ):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}



						def 						lowercase__					(     self				,			a				,			a=False   ):




							if not batched:
								snake_case__  :    Tuple							       =image_inputs[0]
								if isinstance(A_				,			Image.Image   ):
									snake_case__  :    Union[str, Any]							       =image.size
								else:
									snake_case__  :    Optional[Any]							       =image.shape[1], image.shape[2]
								if w < h:
									snake_case__  :    Dict							       =int(self.size["""shortest_edge"""] * h / w   )
									snake_case__  :    List[str]							       =self.size["shortest_edge"]
								elif w > h:
									snake_case__  :    List[str]							       =self.size["shortest_edge"]
									snake_case__  :    Optional[Any]							       =int(self.size["""shortest_edge"""] * w / h   )
								else:
									snake_case__  :    Any							       =self.size["shortest_edge"]
									snake_case__  :    str							       =self.size["shortest_edge"]

							else:
								snake_case__  :    Optional[int]							       =[]
								for image in image_inputs:
									snake_case__  :    Tuple							       =self.get_expected_values([image]   )
									expected_values.append((expected_height, expected_width)   )
								snake_case__  :    Tuple							       =max(A_				,			key=lambda a   : item[0]   )[0]
								snake_case__  :    List[str]							       =max(A_				,			key=lambda a   : item[1]   )[1]

							return expected_height, expected_width







@require_torch
@require_vision
class  _lowercase   (   __snake_case						,				unittest.TestCase						):
						_a							:						List[str]										=							DetaImageProcessor if is_vision_available() else None
						def 						lowercase__					(     self   ):
							snake_case__  :    str							       =DetaImageProcessingTester(self   )
						@property
						def 						lowercase__					(     self   ):
							return self.image_processor_tester.prepare_image_processor_dict()
						def 						lowercase__					(     self   ):
							snake_case__  :    Dict							       =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_				,			"""do_rescale"""   )   )
							self.assertTrue(hasattr(A_				,			"""do_pad"""   )   )
							self.assertTrue(hasattr(A_				,			"""size"""   )   )
						def 						lowercase__					(     self   ):
							snake_case__  :    Tuple							       =self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size				,			{"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}   )
							self.assertEqual(image_processor.do_pad				,			A_   )
						def 						lowercase__					(     self   ):
							pass
						def 						lowercase__					(     self   ):
							snake_case__  :    Any							       =self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							snake_case__  :    List[str]							       =prepare_image_inputs(self.image_processor_tester				,			equal_resolution=A_   )
							for image in image_inputs:
								self.assertIsInstance(A_				,			Image.Image   )

							# Test not batched input
							snake_case__  :    Union[str, Any]							       =image_processing(image_inputs[0]				,			return_tensors="""pt"""   ).pixel_values

							snake_case__  :    Union[str, Any]							       =self.image_processor_tester.get_expected_values(A_   )

							self.assertEqual(
							    encoded_images.shape				,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,			)

							# Test batched
							snake_case__  :    Union[str, Any]							       =self.image_processor_tester.get_expected_values(A_				,			batched=A_   )

							snake_case__  :    Optional[int]							       =image_processing(A_				,			return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,			(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,			)
						def 						lowercase__					(     self   ):
							snake_case__  :    Dict							       =self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							snake_case__  :    int							       =prepare_image_inputs(self.image_processor_tester				,			equal_resolution=A_				,			numpify=A_   )
							for image in image_inputs:
								self.assertIsInstance(A_				,			np.ndarray   )

							# Test not batched input
							snake_case__  :    Union[str, Any]							       =image_processing(image_inputs[0]				,			return_tensors="""pt"""   ).pixel_values

							snake_case__  :    Any							       =self.image_processor_tester.get_expected_values(A_   )

							self.assertEqual(
							    encoded_images.shape				,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,			)

							# Test batched
							snake_case__  :    List[str]							       =image_processing(A_				,			return_tensors="""pt"""   ).pixel_values

							snake_case__  :    Union[str, Any]							       =self.image_processor_tester.get_expected_values(A_				,			batched=A_   )

							self.assertEqual(
							    encoded_images.shape				,			(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,			)
						def 						lowercase__					(     self   ):
							snake_case__  :    Optional[Any]							       =self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							snake_case__  :    List[str]							       =prepare_image_inputs(self.image_processor_tester				,			equal_resolution=A_				,			torchify=A_   )
							for image in image_inputs:
								self.assertIsInstance(A_				,			torch.Tensor   )

							# Test not batched input
							snake_case__  :    List[str]							       =image_processing(image_inputs[0]				,			return_tensors="""pt"""   ).pixel_values

							snake_case__  :    List[str]							       =self.image_processor_tester.get_expected_values(A_   )

							self.assertEqual(
							    encoded_images.shape				,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,			)

							# Test batched
							snake_case__  :    int							       =image_processing(A_				,			return_tensors="""pt"""   ).pixel_values

							snake_case__  :    str							       =self.image_processor_tester.get_expected_values(A_				,			batched=A_   )

							self.assertEqual(
							    encoded_images.shape				,			(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,			)
						@slow
						def 						lowercase__					(     self   ):
							snake_case__  :    Union[str, Any]							       =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""				,			"""r"""   ) as f:
								snake_case__  :    Union[str, Any]							       =json.loads(f.read()   )

							snake_case__  :    Optional[int]							       ={"image_id": 3_9_7_6_9, "annotations": target}

							# encode them
							snake_case__  :    Dict							       =DetaImageProcessor()
							snake_case__  :    List[str]							       =image_processing(images=A_				,			annotations=A_				,			return_tensors="""pt"""   )

							# verify pixel values
							snake_case__  :    Optional[Any]							       =torch.Size([1, 3, 8_0_0, 1_0_6_6]   )
							self.assertEqual(encoding["""pixel_values"""].shape				,			A_   )

							snake_case__  :    List[str]							       =torch.tensor([0.2796, 0.3138, 0.3481]   )
							self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				,			A_				,			atol=1e-4   )   )

							# verify area
							snake_case__  :    Optional[int]							       =torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,			A_   )   )
							# verify boxes
							snake_case__  :    Optional[Any]							       =torch.Size([6, 4]   )
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,			A_   )
							snake_case__  :    List[Any]							       =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,			A_				,			atol=1e-3   )   )
							# verify image_id
							snake_case__  :    Any							       =torch.tensor([3_9_7_6_9]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,			A_   )   )
							# verify is_crowd
							snake_case__  :    List[str]							       =torch.tensor([0, 0, 0, 0, 0, 0]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,			A_   )   )
							# verify class_labels
							snake_case__  :    Optional[int]							       =torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,			A_   )   )
							# verify orig_size
							snake_case__  :    Any							       =torch.tensor([4_8_0, 6_4_0]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,			A_   )   )
							# verify size
							snake_case__  :    Dict							       =torch.tensor([8_0_0, 1_0_6_6]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,			A_   )   )



						@slow
						def 						lowercase__					(     self   ):
							snake_case__  :    Optional[Any]							       =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""				,			"""r"""   ) as f:
								snake_case__  :    Optional[int]							       =json.loads(f.read()   )

							snake_case__  :    int							       ={"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

							snake_case__  :    Tuple							       =pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

							# encode them
							snake_case__  :    Dict							       =DetaImageProcessor(format="""coco_panoptic"""   )
							snake_case__  :    Dict							       =image_processing(images=A_				,			annotations=A_				,			masks_path=A_				,			return_tensors="""pt"""   )

							# verify pixel values
							snake_case__  :    List[Any]							       =torch.Size([1, 3, 8_0_0, 1_0_6_6]   )
							self.assertEqual(encoding["""pixel_values"""].shape				,			A_   )

							snake_case__  :    Optional[int]							       =torch.tensor([0.2796, 0.3138, 0.3481]   )
							self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]				,			A_				,			atol=1e-4   )   )

							# verify area
							snake_case__  :    List[str]							       =torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,			A_   )   )
							# verify boxes
							snake_case__  :    int							       =torch.Size([6, 4]   )
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,			A_   )
							snake_case__  :    Tuple							       =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]				,			A_				,			atol=1e-3   )   )
							# verify image_id
							snake_case__  :    List[Any]							       =torch.tensor([3_9_7_6_9]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,			A_   )   )
							# verify is_crowd
							snake_case__  :    int							       =torch.tensor([0, 0, 0, 0, 0, 0]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,			A_   )   )
							# verify class_labels
							snake_case__  :    str							       =torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,			A_   )   )
							# verify masks
							snake_case__  :    Optional[int]							       =8_2_2_8_7_3
							self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()				,			A_   )
							# verify orig_size
							snake_case__  :    Dict							       =torch.tensor([4_8_0, 6_4_0]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,			A_   )   )
							# verify size
							snake_case__  :    str							       =torch.tensor([8_0_0, 1_0_6_6]   )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,			A_   )   )













import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
       __lowerCamelCase	:		List[str]										=   argparse.ArgumentParser()

       parser.add_argument(
           """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
       )
       # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
       parser.add_argument(
           """--original_config_file""",
           default=None,
           type=str,
           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(
           """--scheduler_type""",
           default="""pndm""",
           type=str,
           help="""Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']""",
       )
       parser.add_argument(
           """--pipeline_type""",
           default=None,
           type=str,
           help=(
               """The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"""
               """. If `None` pipeline will be automatically inferred."""
           ),
       )
       parser.add_argument(
           """--image_size""",
           default=None,
           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(
           """--prediction_type""",
           default=None,
           type=str,
           help=(
               """The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"""
               """ Diffusion v2 Base. Use 'v_prediction' 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.)""")
       parser.add_argument(
           """--stable_unclip""",
           type=str,
           default=None,
           required=False,
           help="""Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.""",
       )
       parser.add_argument(
           """--stable_unclip_prior""",
           type=str,
           default=None,
           required=False,
           help="""Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.""",
       )
       parser.add_argument(
           """--clip_stats_path""",
           type=str,
           help="""Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.""",
           required=False,
       )
       parser.add_argument(
           """--controlnet""", action="""store_true""", default=None, help="""Set flag if this is a controlnet checkpoint."""
       )
       parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
       parser.add_argument(
           """--vae_path""",
           type=str,
           default=None,
           required=False,
           help="""Set to a path, hub id to an already converted vae to not convert it again.""",
       )
       __lowerCamelCase	:		Optional[Any]										=   parser.parse_args()

       __lowerCamelCase	:		Optional[int]										=   download_from_original_stable_diffusion_ckpt(
           checkpoint_path=args.checkpoint_path,
           original_config_file=args.original_config_file,
           image_size=args.image_size,
           prediction_type=args.prediction_type,
           model_type=args.pipeline_type,
           extract_ema=args.extract_ema,
           scheduler_type=args.scheduler_type,
           num_in_channels=args.num_in_channels,
           upcast_attention=args.upcast_attention,
           from_safetensors=args.from_safetensors,
           device=args.device,
           stable_unclip=args.stable_unclip,
           stable_unclip_prior=args.stable_unclip_prior,
           clip_stats_path=args.clip_stats_path,
           controlnet=args.controlnet,
           vae_path=args.vae_path,
       )

       if args.half:
              pipe.to(torch_dtype=torch.floataa)

       if args.controlnet:
              # only save the controlnet model
              pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
       else:
              pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





