
"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

SCREAMING_SNAKE_CASE      =     """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_=8				)->							str:







       """simple docstring"""
       UpperCamelCase					 =		height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       UpperCamelCase					 =		width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_=5_12		,      UpperCAmelCase_=5_12				)->							str:







       """simple docstring"""
       UpperCamelCase					 =		pil_image.resize((w, h)		,      resample=Image.BICUBIC		,      reducing_gap=1				)
       UpperCamelCase					 =		np.array(pil_image.convert("RGB"				)				)
       UpperCamelCase					 =		arr.astype(np.floataa				) / 127.5 - 1
       UpperCamelCase					 =		np.transpose(UpperCAmelCase_		,      [2, 0, 1]				)
       UpperCamelCase					 =		torch.from_numpy(UpperCAmelCase_				).unsqueeze(0				)
       return image



class   __a  (      _lowerCAmelCase	):






       def __init__(  self :							Tuple       ,      UpperCAmelCase_ :							UNetaDConditionModel       ,      UpperCAmelCase_ :							DDPMScheduler       ,      UpperCAmelCase_ :							VQModel       ,      )->  List[str]:







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

              self.register_modules(
                  unet=UpperCAmelCase_       ,      scheduler=UpperCAmelCase_       ,      movq=UpperCAmelCase_       ,      )
              UpperCamelCase					 =		2 ** (len(self.movq.config.block_out_channels			) - 1)






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







              """simple docstring"""
              # get the original timestep using init_timestep
              UpperCamelCase					 =		min(int(num_inference_steps * strength			)       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		max(num_inference_steps - init_timestep       ,      0			)
              UpperCamelCase					 =		self.scheduler.timesteps[t_start:]

              return timesteps, num_inference_steps - t_start






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							Union[str, Any]       ,      UpperCAmelCase_ :							int       ,      UpperCAmelCase_ :							Tuple       ,      UpperCAmelCase_ :							List[Any]       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							List[Any]=None			)->  int:







              """simple docstring"""
              if not isinstance(UpperCAmelCase_       ,      (torch.Tensor, PIL.Image.Image, list)			):
                     raise ValueError(
                         f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase_			)}"			)

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

              UpperCamelCase					 =		batch_size * num_images_per_prompt

              if image.shape[1] == 4:
                     UpperCamelCase					 =		image

              else:
                     if isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			) and len(UpperCAmelCase_			) != batch_size:
                            raise ValueError(
                                f"You have passed a list of generators of length {len(UpperCAmelCase_			)}, but requested an effective batch"
                                f" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

                     elif isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			):
                            UpperCamelCase					 =		[
                                self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(UpperCAmelCase_			)
                            ]
                            UpperCamelCase					 =		torch.cat(UpperCAmelCase_       ,      dim=0			)
                     else:
                            UpperCamelCase					 =		self.movq.encode(UpperCAmelCase_			).latent_dist.sample(UpperCAmelCase_			)

                     UpperCamelCase					 =		self.movq.config.scaling_factor * init_latents

              UpperCamelCase					 =		torch.cat([init_latents]       ,      dim=0			)

              UpperCamelCase					 =		init_latents.shape
              UpperCamelCase					 =		randn_tensor(UpperCAmelCase_       ,      generator=UpperCAmelCase_       ,      device=UpperCAmelCase_       ,      dtype=UpperCAmelCase_			)

              # get latents
              UpperCamelCase					 =		self.scheduler.add_noise(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		init_latents

              return latents






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







              """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.unet,
                  self.movq,
              ]
              for cpu_offloaded_model in models:
                     if cpu_offloaded_model is not None:
                            cpu_offload(UpperCAmelCase_       ,      UpperCAmelCase_			)






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







              """simple docstring"""
              if is_accelerate_available() and is_accelerate_version(">="       ,      "0.17.0.dev0"			):
                     from accelerate import cpu_offload_with_hook
              else:
                     raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."			)

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

              if self.device.type != "cpu":
                     self.to("cpu"       ,      silence_dtype_warnings=UpperCAmelCase_			)
                     torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

              UpperCamelCase					 =		None
              for cpu_offloaded_model in [self.unet, self.movq]:
                     UpperCamelCase ,       UpperCamelCase					 =		cpu_offload_with_hook(UpperCAmelCase_       ,      UpperCAmelCase_       ,      prev_module_hook=UpperCAmelCase_			)

              # We'll offload the last model manually.
              UpperCamelCase					 =		hook






       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Any:







              """simple docstring"""
              if not hasattr(self.unet       ,      "_hf_hook"			):
                     return self.device
              for module in self.unet.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






       @torch.no_grad()
       @replace_example_docstring(UpperCAmelCase_			)
       def __call__(  self :							Tuple       ,      UpperCAmelCase_ :							Union[torch.FloatTensor, List[torch.FloatTensor]]       ,      UpperCAmelCase_ :							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]       ,      UpperCAmelCase_ :							Union[torch.FloatTensor, List[torch.FloatTensor]]       ,      UpperCAmelCase_ :							int = 512       ,      UpperCAmelCase_ :							int = 512       ,      UpperCAmelCase_ :							int = 100       ,      UpperCAmelCase_ :							float = 4.0       ,      UpperCAmelCase_ :							float = 0.3       ,      UpperCAmelCase_ :							int = 1       ,      UpperCAmelCase_ :							Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      UpperCAmelCase_ :							Optional[str] = "pil"       ,      UpperCAmelCase_ :							bool = True       ,      )->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		self._execution_device

              UpperCamelCase					 =		guidance_scale > 1.0

              if isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			):
                     UpperCamelCase					 =		torch.cat(UpperCAmelCase_       ,      dim=0			)
              UpperCamelCase					 =		image_embeds.shape[0]
              if isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			):
                     UpperCamelCase					 =		torch.cat(UpperCAmelCase_       ,      dim=0			)

              if do_classifier_free_guidance:
                     UpperCamelCase					 =		image_embeds.repeat_interleave(UpperCAmelCase_       ,      dim=0			)
                     UpperCamelCase					 =		negative_image_embeds.repeat_interleave(UpperCAmelCase_       ,      dim=0			)

              UpperCamelCase					 =		torch.cat([negative_image_embeds, image_embeds]       ,      dim=0			).to(dtype=self.unet.dtype       ,      device=UpperCAmelCase_			)

              if not isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			):
                     UpperCamelCase					 =		[image]
              if not all(isinstance(UpperCAmelCase_       ,      (PIL.Image.Image, torch.Tensor)			) for i in image			):
                     raise ValueError(
                         f"Input is in incorrect format: {[type(UpperCAmelCase_			) for i in image]}. Currently, we only support  PIL image and pytorch tensor"			)

              UpperCamelCase					 =		torch.cat([prepare_image(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			) for i in image]       ,      dim=0			)
              UpperCamelCase					 =		image.to(dtype=image_embeds.dtype       ,      device=UpperCAmelCase_			)

              UpperCamelCase					 =		self.movq.encode(UpperCAmelCase_			)["latents"]
              UpperCamelCase					 =		latents.repeat_interleave(UpperCAmelCase_       ,      dim=0			)
              self.scheduler.set_timesteps(UpperCAmelCase_       ,      device=UpperCAmelCase_			)
              UpperCamelCase ,       UpperCamelCase					 =		self.get_timesteps(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
              UpperCamelCase					 =		timesteps[:1].repeat(batch_size * num_images_per_prompt			)
              UpperCamelCase ,       UpperCamelCase					 =		downscale_height_and_width(UpperCAmelCase_       ,      UpperCAmelCase_       ,      self.movq_scale_factor			)
              UpperCamelCase					 =		self.prepare_latents(
                  UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      image_embeds.dtype       ,      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					 =		{"image_embeds": image_embeds}
                     UpperCamelCase					 =		self.unet(
                         sample=UpperCAmelCase_       ,      timestep=UpperCAmelCase_       ,      encoder_hidden_states=UpperCAmelCase_       ,      added_cond_kwargs=UpperCAmelCase_       ,      return_dict=UpperCAmelCase_       ,      )[0]

                     if do_classifier_free_guidance:
                            UpperCamelCase ,       UpperCamelCase					 =		noise_pred.split(latents.shape[1]       ,      dim=1			)
                            UpperCamelCase ,       UpperCamelCase					 =		noise_pred.chunk(2			)
                            UpperCamelCase ,       UpperCamelCase					 =		variance_pred.chunk(2			)
                            UpperCamelCase					 =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                            UpperCamelCase					 =		torch.cat([noise_pred, variance_pred_text]       ,      dim=1			)

                     if not (
                         hasattr(self.scheduler.config       ,      "variance_type"			)
                         and self.scheduler.config.variance_type in ["learned", "learned_range"]
                     ):
                            UpperCamelCase ,       UpperCamelCase					 =		noise_pred.split(latents.shape[1]       ,      dim=1			)

                     # compute the previous noisy sample x_t -> x_t-1
                     UpperCamelCase					 =		self.scheduler.step(
                         UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_       ,      )[0]

              # post-processing
              UpperCamelCase					 =		self.movq.decode(UpperCAmelCase_       ,      force_not_quantize=UpperCAmelCase_			)["sample"]

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

              if output_type in ["np", "pil"]:
                     UpperCamelCase					 =		image * 0.5 + 0.5
                     UpperCamelCase					 =		image.clamp(0       ,      1			)
                     UpperCamelCase					 =		image.cpu().permute(0       ,      2       ,      3       ,      1			).float().numpy()

              if output_type == "pil":
                     UpperCamelCase					 =		self.numpy_to_pil(UpperCAmelCase_			)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(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"""





