
"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Union

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

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE      =     """
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""



@dataclass
class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Union[PIL.Image.Image, np.ndarray]





class   __a  (      _lowerCAmelCase	):






       def __init__(  self :							Optional[int]       ,      UpperCAmelCase_ :							PriorTransformer       ,      UpperCAmelCase_ :							CLIPVisionModel       ,      UpperCAmelCase_ :							CLIPImageProcessor       ,      UpperCAmelCase_ :							HeunDiscreteScheduler       ,      UpperCAmelCase_ :							ShapERenderer       ,      )->  List[str]:







              """simple docstring"""
              super().__init__()

              self.register_modules(
                  prior=UpperCAmelCase_       ,      image_encoder=UpperCAmelCase_       ,      image_processor=UpperCAmelCase_       ,      scheduler=UpperCAmelCase_       ,      renderer=UpperCAmelCase_       ,      )






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							Optional[Any]       ,      UpperCAmelCase_ :							Optional[Any]       ,      UpperCAmelCase_ :							Optional[Any]       ,      UpperCAmelCase_ :							Any			)->  str:







              """simple docstring"""
              if latents is None:
                     UpperCamelCase					 =		randn_tensor(UpperCAmelCase_       ,      generator=UpperCAmelCase_       ,      device=UpperCAmelCase_       ,      dtype=UpperCAmelCase_			)
              else:
                     if latents.shape != shape:
                            raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"			)
                     UpperCamelCase					 =		latents.to(UpperCAmelCase_			)

              UpperCamelCase					 =		latents * scheduler.init_noise_sigma
              return latents






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							Tuple=0			)->  List[str]:







              """simple docstring"""
              if is_accelerate_available():
                     from accelerate import cpu_offload
              else:
                     raise ImportError("Please install accelerate via `pip install accelerate`"			)

              UpperCamelCase					 =		torch.device(f"cuda:{gpu_id}"			)

              UpperCamelCase					 =		[self.image_encoder, self.prior]
              for cpu_offloaded_model in models:
                     if cpu_offloaded_model is not None:
                            cpu_offload(UpperCAmelCase_       ,      UpperCAmelCase_			)






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Tuple:







              """simple docstring"""
              if self.device != torch.device("meta"			) or not hasattr(self.image_encoder       ,      "_hf_hook"			):
                     return self.device
              for module in self.image_encoder.modules():
                     if (
                         hasattr(UpperCAmelCase_       ,      "_hf_hook"			)
                         and hasattr(module._hf_hook       ,      "execution_device"			)
                         and module._hf_hook.execution_device is not None
                     ):
                            return torch.device(module._hf_hook.execution_device			)
              return self.device






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							Optional[Any]       ,      UpperCAmelCase_ :							Any       ,      UpperCAmelCase_ :							Tuple       ,      UpperCAmelCase_ :							Any       ,      )->  Optional[Any]:







              """simple docstring"""
              if isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			) and isinstance(image[0]       ,      torch.Tensor			):
                     UpperCamelCase					 =		torch.cat(UpperCAmelCase_       ,      axis=0			) if image[0].ndim == 4 else torch.stack(UpperCAmelCase_       ,      axis=0			)

              if not isinstance(UpperCAmelCase_       ,      torch.Tensor			):
                     UpperCamelCase					 =		self.image_processor(UpperCAmelCase_       ,      return_tensors="pt"			).pixel_values[0].unsqueeze(0			)

              UpperCamelCase					 =		image.to(dtype=self.image_encoder.dtype       ,      device=UpperCAmelCase_			)

              UpperCamelCase					 =		self.image_encoder(UpperCAmelCase_			)["last_hidden_state"]
              UpperCamelCase					 =		image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

              UpperCamelCase					 =		image_embeds.repeat_interleave(UpperCAmelCase_       ,      dim=0			)

              if do_classifier_free_guidance:
                     UpperCamelCase					 =		torch.zeros_like(UpperCAmelCase_			)

                     # For classifier free guidance, we need to do two forward passes.
                     # Here we concatenate the unconditional and text embeddings into a single batch
                     # to avoid doing two forward passes
                     UpperCamelCase					 =		torch.cat([negative_image_embeds, image_embeds]			)

              return image_embeds






       @torch.no_grad()
       @replace_example_docstring(UpperCAmelCase_			)
       def __call__(  self :							List[str]       ,      UpperCAmelCase_ :							Union[PIL.Image.Image, List[PIL.Image.Image]]       ,      UpperCAmelCase_ :							int = 1       ,      UpperCAmelCase_ :							int = 25       ,      UpperCAmelCase_ :							Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      UpperCAmelCase_ :							Optional[torch.FloatTensor] = None       ,      UpperCAmelCase_ :							float = 4.0       ,      UpperCAmelCase_ :							int = 64       ,      UpperCAmelCase_ :							Optional[str] = "pil"       ,      UpperCAmelCase_ :							bool = True       ,      )->  Optional[Any]:







              """simple docstring"""

              if isinstance(UpperCAmelCase_       ,      PIL.Image.Image			):
                     UpperCamelCase					 =		1
              elif isinstance(UpperCAmelCase_       ,      torch.Tensor			):
                     UpperCamelCase					 =		image.shape[0]
              elif isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			) and isinstance(image[0]       ,      (torch.Tensor, PIL.Image.Image)			):
                     UpperCamelCase					 =		len(UpperCAmelCase_			)
              else:
                     raise ValueError(
                         f"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(UpperCAmelCase_			)}"			)

              UpperCamelCase					 =		self._execution_device

              UpperCamelCase					 =		batch_size * num_images_per_prompt

              UpperCamelCase					 =		guidance_scale > 1.0
              UpperCamelCase					 =		self._encode_image(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)

              # prior

              self.scheduler.set_timesteps(UpperCAmelCase_       ,      device=UpperCAmelCase_			)
              UpperCamelCase					 =		self.scheduler.timesteps

              UpperCamelCase					 =		self.prior.config.num_embeddings
              UpperCamelCase					 =		self.prior.config.embedding_dim

              UpperCamelCase					 =		self.prepare_latents(
                  (batch_size, num_embeddings * embedding_dim)       ,      image_embeds.dtype       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      self.scheduler       ,      )

              # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
              UpperCamelCase					 =		latents.reshape(latents.shape[0]       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)

              for i, t in enumerate(self.progress_bar(UpperCAmelCase_			)			):
                     # expand the latents if we are doing classifier free guidance
                     UpperCamelCase					 =		torch.cat([latents] * 2			) if do_classifier_free_guidance else latents
                     UpperCamelCase					 =		self.scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		self.prior(
                         UpperCAmelCase_       ,      timestep=UpperCAmelCase_       ,      proj_embedding=UpperCAmelCase_       ,      ).predicted_image_embedding

                     # remove the variance
                     UpperCamelCase ,       UpperCamelCase					 =		noise_pred.split(
                         scaled_model_input.shape[2]       ,      dim=2			)  # batch_size, num_embeddings, embedding_dim

                     if do_classifier_free_guidance is not None:
                            UpperCamelCase ,       UpperCamelCase					 =		noise_pred.chunk(2			)
                            UpperCamelCase					 =		noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                     UpperCamelCase					 =		self.scheduler.step(
                         UpperCAmelCase_       ,      timestep=UpperCAmelCase_       ,      sample=UpperCAmelCase_       ,      ).prev_sample

              if output_type == "latent":
                     return ShapEPipelineOutput(images=UpperCAmelCase_			)

              UpperCamelCase					 =		[]
              for i, latent in enumerate(UpperCAmelCase_			):
                     print()
                     UpperCamelCase					 =		self.renderer.decode(
                         latent[None, :]       ,      UpperCAmelCase_       ,      size=UpperCAmelCase_       ,      ray_batch_size=4_096       ,      n_coarse_samples=64       ,      n_fine_samples=128       ,      )

                     images.append(UpperCAmelCase_			)

              UpperCamelCase					 =		torch.stack(UpperCAmelCase_			)

              if output_type not in ["np", "pil"]:
                     raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}"			)

              UpperCamelCase					 =		images.cpu().numpy()

              if output_type == "pil":
                     UpperCamelCase					 =		[self.numpy_to_pil(UpperCAmelCase_			) for image in images]

              # Offload last model to CPU
              if hasattr(self       ,      "final_offload_hook"			) and self.final_offload_hook is not None:
                     self.final_offload_hook.offload()

              if not return_dict:
                     return (images,)

              return ShapEPipelineOutput(images=UpperCAmelCase_			)







"""simple docstring"""


import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
      # Initialising the CNN
      # (Sequential- Building the model layer by layer)
      SCREAMING_SNAKE_CASE      =     models.Sequential()

      # Step 1 - Convolution
      # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
      # (3,3) is the kernel size (filter matrix)
      classifier.add(
          layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="""relu""")
      )

      # Step 2 - Pooling
      classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

      # Adding a second convolutional layer
      classifier.add(layers.ConvaD(32, (3, 3), activation="""relu"""))
      classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

      # Step 3 - Flattening
      classifier.add(layers.Flatten())

      # Step 4 - Full connection
      classifier.add(layers.Dense(units=128, activation="""relu"""))
      classifier.add(layers.Dense(units=1, activation="""sigmoid"""))

      # Compiling the CNN
      classifier.compile(
          optimizer="""adam""", loss="""binary_crossentropy""", metrics=["""accuracy"""]
      )

      # Part 2 - Fitting the CNN to the images

      # Load Trained model weights

      # from keras.models import load_model
      # regressor=load_model('cnn.h5')

      SCREAMING_SNAKE_CASE      =     tf.keras.preprocessing.image.ImageDataGenerator(
          rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
      )

      SCREAMING_SNAKE_CASE      =     tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

      SCREAMING_SNAKE_CASE      =     train_datagen.flow_from_directory(
          """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
      )

      SCREAMING_SNAKE_CASE      =     test_datagen.flow_from_directory(
          """dataset/test_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
      )

      classifier.fit_generator(
          training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
      )

      classifier.save("""cnn.h5""")

      # Part 3 - Making new predictions

      SCREAMING_SNAKE_CASE      =     tf.keras.preprocessing.image.load_img(
          """dataset/single_prediction/image.png""", target_size=(64, 64)
      )
      SCREAMING_SNAKE_CASE      =     tf.keras.preprocessing.image.img_to_array(test_image)
      SCREAMING_SNAKE_CASE      =     np.expand_dims(test_image, axis=0)
      SCREAMING_SNAKE_CASE      =     classifier.predict(test_image)
      # training_set.class_indices
      if result[0][0] == 0:
            SCREAMING_SNAKE_CASE      =     """Normal"""
      if result[0][0] == 1:
            SCREAMING_SNAKE_CASE      =     """Abnormality detected"""





