





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
     import onnxruntime as ort


UpperCAmelCase	:	List[Any]		     =       logging.get_logger(__name__)

UpperCAmelCase	:	int		     =       {
    """tensor(bool)""": np.bool_,
    """tensor(int8)""": np.inta,
    """tensor(uint8)""": np.uinta,
    """tensor(int16)""": np.intaa,
    """tensor(uint16)""": np.uintaa,
    """tensor(int32)""": np.intaa,
    """tensor(uint32)""": np.uintaa,
    """tensor(int64)""": np.intaa,
    """tensor(uint64)""": np.uintaa,
    """tensor(float16)""": np.floataa,
    """tensor(float)""": np.floataa,
    """tensor(double)""": np.floataa,
}







class 				__lowerCAmelCase       :







      def __init__(    self		,      lowerCAmelCase__=None		,      **lowerCAmelCase__							)   ->						Dict:



        '''simple docstring'''




        logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."							)
        a__	:					str										=model
        a__	:					int										=kwargs.get("model_save_dir"		,      lowerCAmelCase__							)
        a__	:					int										=kwargs.get("latest_model_name"		,      lowerCAmelCase__							)







      def __call__(    self		,      **lowerCAmelCase__							)   ->						Dict:



        '''simple docstring'''




        a__	:					Dict										={k: np.array(lowerCAmelCase__							) for k, v in kwargs.items()}
        return self.model.run(lowerCAmelCase__		,      lowerCAmelCase__							)







      @staticmethod
      def    _lowercase  (    lowerCAmelCase__		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None							)   ->						Dict:



        '''simple docstring'''




        if provider is None:
          logger.info("No onnxruntime provider specified, using CPUExecutionProvider"							)
          a__	:					Any										="CPUExecutionProvider"

        return ort.InferenceSession(lowerCAmelCase__		,      providers=[provider]		,      sess_options=lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




        a__	:					Dict										=file_name if file_name is not None else ONNX_WEIGHTS_NAME

        a__	:					Dict										=self.model_save_dir.joinpath(self.latest_model_name							)
        a__	:					List[str]										=Path(lowerCAmelCase__							).joinpath(lowerCAmelCase__							)
        try:
          shutil.copyfile(lowerCAmelCase__		,      lowerCAmelCase__							)
        except shutil.SameFileError:
          pass

        # copy external weights (for models >2GB)
        a__	:					List[str]										=self.model_save_dir.joinpath(lowerCAmelCase__							)
        if src_path.exists():
          a__	:					Any										=Path(lowerCAmelCase__							).joinpath(lowerCAmelCase__							)
          try:
            shutil.copyfile(lowerCAmelCase__		,      lowerCAmelCase__							)
          except shutil.SameFileError:
            pass







      def    _lowercase  (    self		,      lowerCAmelCase__		,      **lowerCAmelCase__		,      )   ->						List[str]:



        '''simple docstring'''




        if os.path.isfile(lowerCAmelCase__							):
          logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''							)
          return

        os.makedirs(lowerCAmelCase__		,      exist_ok=lowerCAmelCase__							)

        # saving model weights/files
        self._save_pretrained(lowerCAmelCase__		,      **lowerCAmelCase__							)







      @classmethod
      def    _lowercase  (    cls		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__		,      )   ->						int:



        '''simple docstring'''




        a__	:					int										=file_name if file_name is not None else ONNX_WEIGHTS_NAME
        # load model from local directory
        if os.path.isdir(lowerCAmelCase__							):
          a__	:					Tuple										=OnnxRuntimeModel.load_model(
              os.path.join(lowerCAmelCase__		,      lowerCAmelCase__							)		,      provider=lowerCAmelCase__		,      sess_options=lowerCAmelCase__							)
          a__	:					Union[str, Any]										=Path(lowerCAmelCase__							)
        # load model from hub
        else:
          # download model
          a__	:					Tuple										=hf_hub_download(
              repo_id=lowerCAmelCase__		,      filename=lowerCAmelCase__		,      use_auth_token=lowerCAmelCase__		,      revision=lowerCAmelCase__		,      cache_dir=lowerCAmelCase__		,      force_download=lowerCAmelCase__		,      )
          a__	:					str										=Path(lowerCAmelCase__							).parent
          a__	:					List[Any]										=Path(lowerCAmelCase__							).name
          a__	:					str										=OnnxRuntimeModel.load_model(lowerCAmelCase__		,      provider=lowerCAmelCase__		,      sess_options=lowerCAmelCase__							)
        return cls(model=lowerCAmelCase__		,      **lowerCAmelCase__							)







      @classmethod
      def    _lowercase  (    cls		,      lowerCAmelCase__		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__		,      )   ->						int:



        '''simple docstring'''




        a__	:					int										=None
        if len(str(lowerCAmelCase__							).split("@"							)							) == 2:
          a__					,							a__	:					str										=model_id.split("@"							)

        return cls._from_pretrained(
            model_id=lowerCAmelCase__		,      revision=lowerCAmelCase__		,      cache_dir=lowerCAmelCase__		,      force_download=lowerCAmelCase__		,      use_auth_token=lowerCAmelCase__		,      **lowerCAmelCase__		,      )













import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase	:	int		     =       False







class 				__lowerCAmelCase       (    unittest.TestCase):
      pass







@nightly
@require_torch_gpu
class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)

        a__	:					Optional[Any]										=torch.manual_seed(0							)
        a__	:					Optional[Any]										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="numpy"		,      ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCAmelCase__							)
          a__	:					str										=VersatileDiffusionPipeline.from_pretrained(lowerCAmelCase__		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										=generator.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="numpy"		,      ).images

        assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										="cyberpunk 2077"
        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
        a__	:					Union[str, Any]										=torch.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt=lowerCAmelCase__		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"		,      ).images

        a__	:					int										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1

        a__	:					str										="A painting of a squirrel eating a burger "
        a__	:					Optional[int]										=torch.manual_seed(0							)
        a__	:					str										=pipe.text_to_image(
            prompt=lowerCAmelCase__		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"							).images

        a__	:					Any										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Optional[int]										=np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1

        a__	:					Optional[Any]										=pipe.image_variation(lowerCAmelCase__		,      generator=lowerCAmelCase__		,      output_type="numpy"							).images

        a__	:					Union[str, Any]										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1






