



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,
)


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

_lowercase						  =		"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       Union[str, Any] ,       UpperCamelCase_				:       int=8	)->  Optional[Any]:
    A__		 =					height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    A__		 =					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_				:       Tuple ,       UpperCamelCase_				:       Dict=5_1_2 ,       UpperCamelCase_				:       List[Any]=5_1_2	)->  int:
    A__		 =					pil_image.resize((w, h) ,       resample=Image.BICUBIC ,       reducing_gap=1	)
    A__		 =					np.array(pil_image.convert('''RGB'''	)	)
    A__		 =					arr.astype(np.floataa	) / 127.5 - 1
    A__		 =					np.transpose(UpperCamelCase_ ,       [2, 0, 1]	)
    A__		 =					torch.from_numpy(UpperCamelCase_	).unsqueeze(0	)
    return image



class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__ ,						a__ ,						):
         super().__init__()

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




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         # get the original timestep using init_timestep
         A__		 =					min(int(num_inference_steps * strength) ,						a__)

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

         return timesteps, num_inference_steps - t_start




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__=None):
         if not isinstance(a__ ,						(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(a__)}")

         A__		 =					image.to(device=a__ ,						dtype=a__)

         A__		 =					batch_size * num_images_per_prompt

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

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

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

             A__		 =					self.movq.config.scaling_factor * init_latents

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

         A__		 =					init_latents.shape
         A__		 =					randn_tensor(a__ ,						generator=a__ ,						device=a__ ,						dtype=a__)

         # get latents
         A__		 =					self.scheduler.add_noise(a__ ,						a__ ,						a__)

         A__		 =					init_latents

         return latents




     def 		snake_case_		(				self ,						a__=0):
         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError('''Please install accelerate via `pip install accelerate`''')

         A__		 =					torch.device(F"cuda:{gpu_id}")

         A__		 =					[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                 cpu_offload(a__ ,						a__)




     def 		snake_case_		(				self ,						a__=0):
         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.''')

         A__		 =					torch.device(F"cuda:{gpu_id}")

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

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

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




     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 		snake_case_		(				self):
         if not hasattr(self.unet ,						'''_hf_hook'''):
             return self.device
         for module in self.unet.modules():
             if (
                 hasattr(a__ ,						'''_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(a__)
     def __call__(				self ,						a__ ,						a__ ,						a__ ,						a__ = 5_1_2 ,						a__ = 5_1_2 ,						a__ = 1_0_0 ,						a__ = 4.0 ,						a__ = 0.3 ,						a__ = 1 ,						a__ = None ,						a__ = "pil" ,						a__ = True ,						):
         A__		 =					self._execution_device

         A__		 =					guidance_scale > 1.0

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

         if do_classifier_free_guidance:
             A__		 =					image_embeds.repeat_interleave(a__ ,						dim=0)
             A__		 =					negative_image_embeds.repeat_interleave(a__ ,						dim=0)

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

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

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

         A__		 =					self.movq.encode(a__)['''latents''']
         A__		 =					latents.repeat_interleave(a__ ,						dim=0)
         self.scheduler.set_timesteps(a__ ,						device=a__)
         A__ ,	A__		 =					self.get_timesteps(a__ ,						a__ ,						a__)
         A__		 =					timesteps[:1].repeat(batch_size * num_images_per_prompt)
         A__ ,	A__		 =					downscale_height_and_width(a__ ,						a__ ,						self.movq_scale_factor)
         A__		 =					self.prepare_latents(
             a__ ,						a__ ,						a__ ,						a__ ,						image_embeds.dtype ,						a__ ,						a__)
         for i, t in enumerate(self.progress_bar(a__)):
             # expand the latents if we are doing classifier free guidance
             A__		 =					torch.cat([latents] * 2) if do_classifier_free_guidance else latents

             A__		 =					{'''image_embeds''': image_embeds}
             A__		 =					self.unet(
                 sample=a__ ,						timestep=a__ ,						encoder_hidden_states=a__ ,						added_cond_kwargs=a__ ,						return_dict=a__ ,						)[0]

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

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

         # post-processing
         A__		 =					self.movq.decode(a__ ,						force_not_quantize=a__)['''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"]:
             A__		 =					image * 0.5 + 0.5
             A__		 =					image.clamp(0 ,						1)
             A__		 =					image.cpu().permute(0 ,						2 ,						3 ,						1).float().numpy()

         if output_type == "pil":
             A__		 =					self.numpy_to_pil(a__)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=a__)





import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier



def 		lowerCAmelCase__    (					UpperCamelCase_				:       dict	)->  tuple:
    return (data["data"], data["target"])



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.ndarray ,       UpperCamelCase_				:       np.ndarray	)->  XGBClassifier:
    A__		 =					XGBClassifier()
    classifier.fit(UpperCamelCase_ ,       UpperCamelCase_	)
    return classifier



def 		lowerCAmelCase__    (					)->  None:
    A__		 =					load_iris()
    A__ ,	A__		 =					data_handling(UpperCamelCase_	)
    A__ ,	A__ ,	A__ ,	A__		 =					train_test_split(
        UpperCamelCase_ ,       UpperCamelCase_ ,       test_size=0.25	)

    A__		 =					iris['''target_names''']

    # Create an XGBoost Classifier from the training data
    A__		 =					xgboost(UpperCamelCase_ ,       UpperCamelCase_	)

    # Display the confusion matrix of the classifier with both training and test sets
    ConfusionMatrixDisplay.from_estimator(
        UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       display_labels=UpperCamelCase_ ,       cmap='''Blues''' ,       normalize='''true''' ,       )
    plt.title('''Normalized Confusion Matrix - IRIS Dataset'''	)
    plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()
