import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						ShapEImgaImgPipeline
  lowerCamelCase__     =						['''image''']
  lowerCamelCase__     =						['''image''']
  lowerCamelCase__     =						[
      '''num_images_per_prompt''',
      '''num_inference_steps''',
      '''generator''',
      '''latents''',
      '''guidance_scale''',
      '''frame_size''',
      '''output_type''',
      '''return_dict''',
  ]
  lowerCamelCase__     =						False







  @property
  def    __A     (							self :       Any   )  ->	Optional[int]:
        return 32







  @property
  def    __A     (							self :       Tuple   )  ->	Any:
        return 32







  @property
  def    __A     (							self :       int   )  ->	Union[str, Any]:
        return self.time_input_dim * 4







  @property
  def    __A     (							self :       Any   )  ->	Optional[Any]:
        return 8







  @property
  def    __A     (							self :       Optional[int]   )  ->	str:
        torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size  ,     image_size=64  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=37  ,     num_attention_heads=4  ,     num_channels=3  ,     num_hidden_layers=5  ,     patch_size=1  ,     )

        SCREAMING_SNAKE_CASE_          =  CLIPVisionModel(__magic_name__   )
        return model







  @property
  def    __A     (							self :       Union[str, Any]   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  CLIPImageProcessor(
            crop_size=224  ,     do_center_crop=__magic_name__  ,     do_normalize=__magic_name__  ,     do_resize=__magic_name__  ,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,     resample=3  ,     size=224  ,     )

        return image_processor







  @property
  def    __A     (							self :       Optional[int]   )  ->	Tuple:
        torch.manual_seed(0   )

        SCREAMING_SNAKE_CASE_          =  {
            "num_attention_heads": 2,
            "attention_head_dim": 16,
            "embedding_dim": self.time_input_dim,
            "num_embeddings": 32,
            "embedding_proj_dim": self.text_embedder_hidden_size,
            "time_embed_dim": self.time_embed_dim,
            "num_layers": 1,
            "clip_embed_dim": self.time_input_dim * 2,
            "additional_embeddings": 0,
            "time_embed_act_fn": "gelu",
            "norm_in_type": "layer",
            "embedding_proj_norm_type": "layer",
            "encoder_hid_proj_type": None,
            "added_emb_type": None,
        }

        SCREAMING_SNAKE_CASE_          =  PriorTransformer(**__magic_name__   )
        return model







  @property
  def    __A     (							self :       Any   )  ->	List[str]:
        torch.manual_seed(0   )

        SCREAMING_SNAKE_CASE_          =  {
            "param_shapes": (
                (self.renderer_dim, 93),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
            ),
            "d_latent": self.time_input_dim,
            "d_hidden": self.renderer_dim,
            "n_output": 12,
            "background": (
                0.1,
                0.1,
                0.1,
            ),
        }
        SCREAMING_SNAKE_CASE_          =  ShapERenderer(**__magic_name__   )
        return model







  def    __A     (							self :       str   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.dummy_prior
        SCREAMING_SNAKE_CASE_          =  self.dummy_image_encoder
        SCREAMING_SNAKE_CASE_          =  self.dummy_image_processor
        SCREAMING_SNAKE_CASE_          =  self.dummy_renderer

        SCREAMING_SNAKE_CASE_          =  HeunDiscreteScheduler(
            beta_schedule="exp"  ,     num_train_timesteps=1_024  ,     prediction_type="sample"  ,     use_karras_sigmas=__magic_name__  ,     clip_sample=__magic_name__  ,     clip_sample_range=1.0  ,     )
        SCREAMING_SNAKE_CASE_          =  {
            "prior": prior,
            "image_encoder": image_encoder,
            "image_processor": image_processor,
            "renderer": renderer,
            "scheduler": scheduler,
        }

        return components







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       int  ,     __magic_name__ :       Tuple=0   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  floats_tensor((1, 3, 64, 64)  ,     rng=random.Random(__magic_name__   )   ).to(__magic_name__   )

        if str(__magic_name__   ).startswith("mps"   ):
              SCREAMING_SNAKE_CASE_          =  torch.manual_seed(__magic_name__   )
        else:
              SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {
            "image": input_image,
            "generator": generator,
            "num_inference_steps": 1,
            "frame_size": 32,
            "output_type": "np",
        }
        return inputs







  def    __A     (							self :       Tuple   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  "cpu"

        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()

        SCREAMING_SNAKE_CASE_          =  self.pipeline_class(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  pipe.to(__magic_name__   )

        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  pipe(**self.get_dummy_inputs(__magic_name__   )   )
        SCREAMING_SNAKE_CASE_          =  output.images[0]
        SCREAMING_SNAKE_CASE_          =  image[0, -3:, -3:, -1]

        assert image.shape == (20, 32, 32, 3)

        SCREAMING_SNAKE_CASE_          =  np.array(
            [
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
                0.0003_9216,
            ]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
        self._test_inference_batch_consistent(batch_sizes=[1, 2]   )







  def    __A     (							self :       Union[str, Any]   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  torch_device == "cpu"
        SCREAMING_SNAKE_CASE_          =  True
        self._test_inference_batch_single_identical(
            batch_size=2  ,     test_max_difference=__magic_name__  ,     relax_max_difference=__magic_name__  ,     )







  def    __A     (							self :       Union[str, Any]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()
        SCREAMING_SNAKE_CASE_          =  self.pipeline_class(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_          =  2

        SCREAMING_SNAKE_CASE_          =  self.get_dummy_inputs(__magic_name__   )

        for key in inputs.keys():
              if key in self.batch_params:
                    SCREAMING_SNAKE_CASE_          =  batch_size * [inputs[key]]

        SCREAMING_SNAKE_CASE_          =  pipe(**__magic_name__  ,     num_images_per_prompt=__magic_name__   )[0]

        assert images.shape[0] == batch_size * num_images_per_prompt



@slow
@require_torch_gpu
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       List[str]   )  ->	Any:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def    __A     (							self :       Optional[int]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"   )
        SCREAMING_SNAKE_CASE_          =  load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/shap_e/test_shap_e_img2img_out.npy"   )
        SCREAMING_SNAKE_CASE_          =  ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"   )
        SCREAMING_SNAKE_CASE_          =  pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(0   )

        SCREAMING_SNAKE_CASE_          =  pipe(
            __magic_name__  ,     generator=__magic_name__  ,     guidance_scale=3.0  ,     num_inference_steps=64  ,     frame_size=64  ,     output_type="np"  ,     ).images[0]

        assert images.shape == (20, 64, 64, 3)

        assert_mean_pixel_difference(__magic_name__  ,     __magic_name__   )



from collections.abc import Callable


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  a
      SCREAMING_SNAKE_CASE_          =  b
      if function(__UpperCamelCase				) == 0:  # one of the a or b is a root for the function
            return a
      elif function(__UpperCamelCase				) == 0:
            return b
      elif (
          function(__UpperCamelCase				) * function(__UpperCamelCase				) > 0
      ):  # if none of these are root and they are both positive or negative,
            # then this algorithm can't find the root
            raise ValueError("could not find root in given interval."				)
      else:
            SCREAMING_SNAKE_CASE_          =  start + (end - start) / 2.0
            while abs(start - mid				) > 1_0**-7:  # until precisely equals to 10^-7
                  if function(__UpperCamelCase				) == 0:
                        return mid
                  elif function(__UpperCamelCase				) * function(__UpperCamelCase				) < 0:
                        SCREAMING_SNAKE_CASE_          =  mid
                  else:
                        SCREAMING_SNAKE_CASE_          =  mid
                  SCREAMING_SNAKE_CASE_          =  start + (end - start) / 2.0
            return mid


def      a__ (		__UpperCamelCase				):
      return x**3 - 2 * x - 5


if __name__ == "__main__":
      print(bisection(f, 1, 10_00))

      import doctest

      doctest.testmod()


