

'''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,
)


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

lowerCamelCase			:				Optional[Any] 			=					"\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						:					Any							,			_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					Optional[Any]=8    )				->      Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     _SCREAMING_SNAKE_CASE							   =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						:					int							,			_UpperCamelCase						:					Any=5_12							,			_UpperCamelCase						:					Union[str, Any]=5_12    )				->      Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =pil_image.resize((w, h)							,			resample=Image.BICUBIC							,			reducing_gap=1    )
     _SCREAMING_SNAKE_CASE							   =np.array(pil_image.convert('RGB'    )    )
     _SCREAMING_SNAKE_CASE							   =arr.astype(np.floataa    ) / 1_27.5 - 1
     _SCREAMING_SNAKE_CASE							   =np.transpose(__a							,			[2, 0, 1]    )
     _SCREAMING_SNAKE_CASE							   =torch.from_numpy(__a    ).unsqueeze(0    )
     return image







class 							A__   (	snake_case_  ):





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





       '''simple docstring'''






       super().__init__()

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





  def 						A  (		self				:     Optional[int]							, _a				:     Any							, _a				:     Tuple							, _a				:     Union[str, Any]			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =min(int(num_inference_steps * strength			)							, _A			)

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

       return timesteps, num_inference_steps - t_start





  def 						A  (		self				:     Optional[Any]							, _a				:     List[Any]							, _a				:     Optional[int]							, _a				:     Optional[int]							, _a				:     int							, _a				:     Optional[Any]							, _a				:     Optional[int]							, _a				:     Dict=None			)     ->   Optional[Any]:





       '''simple docstring'''






       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			)}"			)

       _SCREAMING_SNAKE_CASE							   =image.to(device=_A							, dtype=_A			)

       _SCREAMING_SNAKE_CASE							   =batch_size * num_images_per_prompt

       if image.shape[1] == 4:
            _SCREAMING_SNAKE_CASE							   =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			):
                 _SCREAMING_SNAKE_CASE							   =[
                     self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(_A			)
                 ]
                 _SCREAMING_SNAKE_CASE							   =torch.cat(_A							, dim=0			)
            else:
                 _SCREAMING_SNAKE_CASE							   =self.movq.encode(_A			).latent_dist.sample(_A			)

            _SCREAMING_SNAKE_CASE							   =self.movq.config.scaling_factor * init_latents

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

       _SCREAMING_SNAKE_CASE							   =init_latents.shape
       _SCREAMING_SNAKE_CASE							   =randn_tensor(_A							, generator=_A							, device=_A							, dtype=_A			)

       # get latents
       _SCREAMING_SNAKE_CASE							   =self.scheduler.add_noise(_A							, _A							, _A			)

       _SCREAMING_SNAKE_CASE							   =init_latents

       return latents





  def 						A  (		self				:     List[Any]							, _a				:     Optional[Any]=0			)     ->   Union[str, Any]:





       '''simple docstring'''






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

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

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





  def 						A  (		self				:     Optional[Any]							, _a				:     List[str]=0			)     ->   int:





       '''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.'			)

       _SCREAMING_SNAKE_CASE							   =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)

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

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





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





       '''simple docstring'''






       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				:     Union[str, Any]							, _a				:     Union[torch.FloatTensor, List[torch.FloatTensor]]							, _a				:     Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]							, _a				:     Union[torch.FloatTensor, List[torch.FloatTensor]]							, _a				:     int = 512							, _a				:     int = 512							, _a				:     int = 100							, _a				:     float = 4.0							, _a				:     float = 0.3							, _a				:     int = 1							, _a				:     Optional[Union[torch.Generator, List[torch.Generator]]] = None							, _a				:     Optional[str] = "pil"							, _a				:     bool = True							, )     ->   List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self._execution_device

       _SCREAMING_SNAKE_CASE							   =guidance_scale > 1.0

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

       if do_classifier_free_guidance:
            _SCREAMING_SNAKE_CASE							   =image_embeds.repeat_interleave(_A							, dim=0			)
            _SCREAMING_SNAKE_CASE							   =negative_image_embeds.repeat_interleave(_A							, dim=0			)

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

       if not isinstance(_A							, _A			):
            _SCREAMING_SNAKE_CASE							   =[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"			)

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

       _SCREAMING_SNAKE_CASE							   =self.movq.encode(_A			)['latents']
       _SCREAMING_SNAKE_CASE							   =latents.repeat_interleave(_A							, dim=0			)
       self.scheduler.set_timesteps(_A							, device=_A			)
       _SCREAMING_SNAKE_CASE							   =self.get_timesteps(_A							, _A							, _A			)
       _SCREAMING_SNAKE_CASE							   =timesteps[:1].repeat(batch_size * num_images_per_prompt			)
       _SCREAMING_SNAKE_CASE							   =downscale_height_and_width(_A							, _A							, self.movq_scale_factor			)
       _SCREAMING_SNAKE_CASE							   =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
            _SCREAMING_SNAKE_CASE							   =torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

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

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

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

       # post-processing
       _SCREAMING_SNAKE_CASE							   =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"]:
            _SCREAMING_SNAKE_CASE							   =image * 0.5 + 0.5
            _SCREAMING_SNAKE_CASE							   =image.clamp(0							, 1			)
            _SCREAMING_SNAKE_CASE							   =image.cpu().permute(0							, 2							, 3							, 1			).float().numpy()

       if output_type == "pil":
            _SCREAMING_SNAKE_CASE							   =self.numpy_to_pil(_A			)

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=_A			)



'''simple docstring'''
from math import factorial


def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					float    )				->      float:
     """simple docstring"""




     if successes > trials:
          raise ValueError('successes must be lower or equal to trials'    )
     if trials < 0 or successes < 0:
          raise ValueError('the function is defined for non-negative integers'    )
     if not isinstance(_UpperCamelCase							,			_UpperCamelCase    ) or not isinstance(_UpperCamelCase							,			_UpperCamelCase    ):
          raise ValueError('the function is defined for non-negative integers'    )
     if not 0 < prob < 1:
          raise ValueError('prob has to be in range of 1 - 0'    )
     _SCREAMING_SNAKE_CASE							   =(prob**successes) * ((1 - prob) ** (trials - successes))
     # Calculate the binomial coefficient: n! / k!(n-k)!
     _SCREAMING_SNAKE_CASE							   =float(factorial(_UpperCamelCase    )    )
     coefficient /= factorial(_UpperCamelCase    ) * factorial(trials - successes    )
     return probability * coefficient


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     print("Probability of 2 successes out of 4 trails")
     print("with probability of 0.75 is:", end=" ")
     print(binomial_distribution(2, 4, 0.7_5))
