



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 DetrImageProcessor






class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__=7,		lowerCamelCase__=3,		lowerCamelCase__=30,		lowerCamelCase__=400,		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__=1 / 255,		lowerCamelCase__=True,		lowerCamelCase__=[0.5, 0.5, 0.5],		lowerCamelCase__=[0.5, 0.5, 0.5],		lowerCamelCase__=True,		):
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        A :       Optional[int]          =   size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
        A :       Any          =   parent
        A :       Optional[Any]          =   batch_size
        A :       Optional[int]          =   num_channels
        A :       str          =   min_resolution
        A :       Any          =   max_resolution
        A :       Dict          =   do_resize
        A :       Optional[int]          =   size
        A :       List[Any]          =   do_rescale
        A :       Tuple          =   rescale_factor
        A :       List[Any]          =   do_normalize
        A :       Dict          =   image_mean
        A :       Optional[int]          =   image_std
        A :       Tuple          =   do_pad






    def 			_lowerCAmelCase					(       self		):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_pad": self.do_pad,
        }






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

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

        return expected_height, expected_width






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: List[Any]					=      DetrImageProcessor if is_vision_available() else None






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   DetrImageProcessingTester(self		)






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.image_processor_tester.prepare_image_processor_dict()






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""image_mean"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""image_std"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""do_normalize"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""do_rescale"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""rescale_factor"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""do_resize"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""size"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""do_pad"""		)		)






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size,		{"""shortest_edge""": 18, """longest_edge""": 1333}		)
        self.assertEqual(image_processor.do_pad,		lowerCamelCase__		)

        A :       int          =   self.image_processing_class.from_dict(
            self.image_processor_dict,		size=42,		max_size=84,		pad_and_return_pixel_mask=lowerCamelCase__		)
        self.assertEqual(image_processor.size,		{"""shortest_edge""": 42, """longest_edge""": 84}		)
        self.assertEqual(image_processor.do_pad,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        pass






    def 			_lowerCAmelCase					(       self		):
        # Initialize image_processing
        A :       int          =   self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A :       Any          =   prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCamelCase__		)
        for image in image_inputs:
            self.assertIsInstance(lowerCamelCase__,		Image.Image		)

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

        A     ,							A :       str          =   self.image_processor_tester.get_expected_values(lowerCamelCase__		)

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

        # Test batched
        A     ,							A :       Optional[int]          =   self.image_processor_tester.get_expected_values(lowerCamelCase__,		batched=lowerCamelCase__		)

        A :       Optional[int]          =   image_processing(lowerCamelCase__,		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 			_lowerCAmelCase					(       self		):
        # Initialize image_processing
        A :       List[Any]          =   self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A :       int          =   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
        A :       str          =   image_processing(image_inputs[0],		return_tensors="""pt"""		).pixel_values

        A     ,							A :       Tuple          =   self.image_processor_tester.get_expected_values(lowerCamelCase__		)

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

        # Test batched
        A :       Optional[int]          =   image_processing(lowerCamelCase__,		return_tensors="""pt"""		).pixel_values

        A     ,							A :       List[Any]          =   self.image_processor_tester.get_expected_values(lowerCamelCase__,		batched=lowerCamelCase__		)

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






    def 			_lowerCAmelCase					(       self		):
        # Initialize image_processing
        A :       Dict          =   self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A :       List[str]          =   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
        A :       Union[str, Any]          =   image_processing(image_inputs[0],		return_tensors="""pt"""		).pixel_values

        A     ,							A :       Optional[int]          =   self.image_processor_tester.get_expected_values(lowerCamelCase__		)

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

        # Test batched
        A :       Tuple          =   image_processing(lowerCamelCase__,		return_tensors="""pt"""		).pixel_values

        A     ,							A :       List[str]          =   self.image_processor_tester.get_expected_values(lowerCamelCase__,		batched=lowerCamelCase__		)

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






    @slow
    def 			_lowerCAmelCase					(       self		):
        # prepare image and target
        A :       Tuple          =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
        with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""",		"""r"""		) as f:
            A :       Any          =   json.loads(f.read()		)

        A :       Dict          =   {"""image_id""": 3_9769, """annotations""": target}

        # encode them
        A :       Optional[int]          =   DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""		)
        A :       Dict          =   image_processing(images=lowerCamelCase__,		annotations=lowerCamelCase__,		return_tensors="""pt"""		)

        # verify pixel values
        A :       Union[str, Any]          =   torch.Size([1, 3, 800, 1066]		)
        self.assertEqual(encoding["""pixel_values"""].shape,		lowerCamelCase__		)

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

        # verify area
        A :       Union[str, Any]          =   torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""],		lowerCamelCase__		)		)
        # verify boxes
        A :       Any          =   torch.Size([6, 4]		)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,		lowerCamelCase__		)
        A :       Optional[int]          =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0],		lowerCamelCase__,		atol=1e-3		)		)
        # verify image_id
        A :       Dict          =   torch.tensor([3_9769]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],		lowerCamelCase__		)		)
        # verify is_crowd
        A :       Optional[Any]          =   torch.tensor([0, 0, 0, 0, 0, 0]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],		lowerCamelCase__		)		)
        # verify class_labels
        A :       Dict          =   torch.tensor([75, 75, 63, 65, 17, 17]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],		lowerCamelCase__		)		)
        # verify orig_size
        A :       Dict          =   torch.tensor([480, 640]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],		lowerCamelCase__		)		)
        # verify size
        A :       Union[str, Any]          =   torch.tensor([800, 1066]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],		lowerCamelCase__		)		)






    @slow
    def 			_lowerCAmelCase					(       self		):
        # prepare image, target and masks_path
        A :       Union[str, Any]          =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
        with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""",		"""r"""		) as f:
            A :       Optional[Any]          =   json.loads(f.read()		)

        A :       Any          =   {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

        A :       Any          =   pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""		)

        # encode them
        A :       int          =   DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""		)
        A :       int          =   image_processing(images=lowerCamelCase__,		annotations=lowerCamelCase__,		masks_path=lowerCamelCase__,		return_tensors="""pt"""		)

        # verify pixel values
        A :       Optional[int]          =   torch.Size([1, 3, 800, 1066]		)
        self.assertEqual(encoding["""pixel_values"""].shape,		lowerCamelCase__		)

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

        # verify area
        A :       List[str]          =   torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""],		lowerCamelCase__		)		)
        # verify boxes
        A :       Optional[Any]          =   torch.Size([6, 4]		)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,		lowerCamelCase__		)
        A :       int          =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0],		lowerCamelCase__,		atol=1e-3		)		)
        # verify image_id
        A :       Any          =   torch.tensor([3_9769]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],		lowerCamelCase__		)		)
        # verify is_crowd
        A :       Union[str, Any]          =   torch.tensor([0, 0, 0, 0, 0, 0]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],		lowerCamelCase__		)		)
        # verify class_labels
        A :       List[str]          =   torch.tensor([17, 17, 63, 75, 75, 93]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],		lowerCamelCase__		)		)
        # verify masks
        A :       Optional[Any]          =   82_2873
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item(),		lowerCamelCase__		)
        # verify orig_size
        A :       List[str]          =   torch.tensor([480, 640]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],		lowerCamelCase__		)		)
        # verify size
        A :       Tuple          =   torch.tensor([800, 1066]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],		lowerCamelCase__		)		)







import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


SCREAMING_SNAKE_CASE_:List[Any]             =						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=False      ,     )       ->  int:



    """simple docstring"""






    output_path.parent.mkdir(parents=_lowerCAmelCase      ,     exist_ok=_lowerCAmelCase		)
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            _lowerCAmelCase      ,     _lowerCAmelCase      ,     f=output_path.as_posix()      ,     input_names=_lowerCAmelCase      ,     output_names=_lowerCAmelCase      ,     dynamic_axes=_lowerCAmelCase      ,     do_constant_folding=_lowerCAmelCase      ,     use_external_data_format=_lowerCAmelCase      ,     enable_onnx_checker=_lowerCAmelCase      ,     opset_version=_lowerCAmelCase      ,     )
    else:
        export(
            _lowerCAmelCase      ,     _lowerCAmelCase      ,     f=output_path.as_posix()      ,     input_names=_lowerCAmelCase      ,     output_names=_lowerCAmelCase      ,     dynamic_axes=_lowerCAmelCase      ,     do_constant_folding=_lowerCAmelCase      ,     opset_version=_lowerCAmelCase      ,     )




@torch.no_grad()
def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = False		)       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A :       Union[str, Any]          =   """cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
    else:
        A :       Any          =   """cpu"""
    A :       Any          =   Path(_lowerCAmelCase		)

    # VAE DECODER
    A :       Union[str, Any]          =   AutoencoderKL.from_pretrained(model_path + """/vae"""		)
    A :       Any          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A :       Optional[int]          =   vae_decoder.decode
    onnx_export(
        _lowerCAmelCase      ,     model_args=(
            torch.randn(1      ,     _lowerCAmelCase      ,     25      ,     25		).to(device=_lowerCAmelCase      ,     dtype=_lowerCAmelCase		),
            False,
        )      ,     output_path=output_path / """vae_decoder""" / """model.onnx"""      ,     ordered_input_names=["""latent_sample""", """return_dict"""]      ,     output_names=["""sample"""]      ,     dynamic_axes={
            """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
        }      ,     opset=_lowerCAmelCase      ,     )
    del vae_decoder


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Tuple             =						argparse.ArgumentParser()

     parser.add_argument(
         """--model_path""",
         type=str,
         required=True,
         help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
     )

     parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
     parser.add_argument(
         """--opset""",
         default=14,
         type=int,
         help="""The version of the ONNX operator set to use.""",
     )
     parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

     SCREAMING_SNAKE_CASE_:Tuple             =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("""SD: Done: ONNX""")


