


'''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,
)


A				:					Optional[int]    						=			logging.get_logger(__name__)  # pylint: disable=invalid-name

A				:					List[Any]    						=			'''
    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__	(    lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      int  ,lowerCamelCase					:      Any=8       ):
       _A					:     int     = height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       _A					:     Dict     = width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Tuple=512  ,lowerCamelCase					:      Union[str, Any]=512       ):
       _A					:     Optional[Any]     = pil_image.resize((w, h)  ,resample=Image.BICUBIC  ,reducing_gap=1       )
       _A					:     Dict     = np.array(pil_image.convert('RGB'       )       )
       _A					:     int     = arr.astype(np.floataa       ) / 127.5 - 1
       _A					:     Any     = np.transpose(__lowerCAmelCase  ,[2, 0, 1]       )
       _A					:     int     = torch.from_numpy(__lowerCAmelCase       ).unsqueeze(0       )
       return image


class 			__lowerCamelCase			(	lowerCamelCase_	):
    """simple docstring"""


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							UNetaDConditionModel				,							SCREAMING_SNAKE_CASE      :							DDPMScheduler				,							SCREAMING_SNAKE_CASE      :							VQModel				,							):
           super().__init__()

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


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]):
           # get the original timestep using init_timestep
           _A					:     Optional[int]     = min(int(num_inference_steps * strength)				,							__snake_case)

           _A					:     Any     = max(num_inference_steps - init_timestep				,							0)
           _A					:     List[str]     = self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Tuple=None):
           if not isinstance(__snake_case				,							(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(__snake_case)}')

           _A					:     Any     = image.to(device=__snake_case				,							dtype=__snake_case)

           _A					:     List[str]     = batch_size * num_images_per_prompt

           if image.shape[1] == 4:
                  _A					:     Union[str, Any]     = image

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

                  elif isinstance(__snake_case				,							__snake_case):
                         _A					:     Union[str, Any]     = [
                             self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(__snake_case)
                         ]
                         _A					:     Tuple     = torch.cat(__snake_case				,							dim=0)
                  else:
                         _A					:     List[Any]     = self.movq.encode(__snake_case).latent_dist.sample(__snake_case)

                  _A					:     List[str]     = self.movq.config.scaling_factor * init_latents

           _A					:     Optional[int]     = torch.cat([init_latents]				,							dim=0)

           _A					:     Optional[Any]     = init_latents.shape
           _A					:     Optional[Any]     = randn_tensor(__snake_case				,							generator=__snake_case				,							device=__snake_case				,							dtype=__snake_case)

           # get latents
           _A					:     Tuple     = self.scheduler.add_noise(__snake_case				,							__snake_case				,							__snake_case)

           _A					:     List[Any]     = init_latents

           return latents


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]=0):
           if is_accelerate_available():
                  from accelerate import cpu_offload
           else:
                  raise ImportError('Please install accelerate via `pip install accelerate`')

           _A					:     str     = torch.device(F'cuda:{gpu_id}')

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


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[int]=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					:     Dict     = torch.device(F'cuda:{gpu_id}')

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

           _A					:     int     = None
           for cpu_offloaded_model in [self.unet, self.movq]:
                  _A  ,			_A					:     List[str]     = cpu_offload_with_hook(__snake_case				,							__snake_case				,							prev_module_hook=__snake_case)

           # We'll offload the last model manually.
           _A					:     List[Any]     = hook


    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def        A   (  self      :							Optional[Any]):
           if not hasattr(self.unet				,							'_hf_hook'):
                  return self.device
           for module in self.unet.modules():
                  if (
                      hasattr(__snake_case				,							'_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(__snake_case)
    def __call__(  self      :							str				,							SCREAMING_SNAKE_CASE      :							Union[torch.FloatTensor, List[torch.FloatTensor]]				,							SCREAMING_SNAKE_CASE      :							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]				,							SCREAMING_SNAKE_CASE      :							Union[torch.FloatTensor, List[torch.FloatTensor]]				,							SCREAMING_SNAKE_CASE      :							int = 512				,							SCREAMING_SNAKE_CASE      :							int = 512				,							SCREAMING_SNAKE_CASE      :							int = 100				,							SCREAMING_SNAKE_CASE      :							float = 4.0				,							SCREAMING_SNAKE_CASE      :							float = 0.3				,							SCREAMING_SNAKE_CASE      :							int = 1				,							SCREAMING_SNAKE_CASE      :							Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							SCREAMING_SNAKE_CASE      :							Optional[str] = "pil"				,							SCREAMING_SNAKE_CASE      :							bool = True				,							):
           _A					:     Any     = self._execution_device

           _A					:     str     = guidance_scale > 1.0

           if isinstance(__snake_case				,							__snake_case):
                  _A					:     List[str]     = torch.cat(__snake_case				,							dim=0)
           _A					:     str     = image_embeds.shape[0]
           if isinstance(__snake_case				,							__snake_case):
                  _A					:     List[Any]     = torch.cat(__snake_case				,							dim=0)

           if do_classifier_free_guidance:
                  _A					:     List[str]     = image_embeds.repeat_interleave(__snake_case				,							dim=0)
                  _A					:     Tuple     = negative_image_embeds.repeat_interleave(__snake_case				,							dim=0)

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

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

           _A					:     Tuple     = torch.cat([prepare_image(__snake_case				,							__snake_case				,							__snake_case) for i in image]				,							dim=0)
           _A					:     List[str]     = image.to(dtype=image_embeds.dtype				,							device=__snake_case)

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

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

                  if do_classifier_free_guidance:
                         _A  ,			_A					:     str     = noise_pred.split(latents.shape[1]				,							dim=1)
                         _A  ,			_A					:     List[Any]     = noise_pred.chunk(2)
                         _A  ,			_A					:     List[Any]     = variance_pred.chunk(2)
                         _A					:     Tuple     = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                         _A					:     Any     = 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					:     Union[str, Any]     = noise_pred.split(latents.shape[1]				,							dim=1)

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

           # post-processing
           _A					:     str     = self.movq.decode(__snake_case				,							force_not_quantize=__snake_case)['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					:     Any     = image * 0.5 + 0.5
                  _A					:     Optional[Any]     = image.clamp(0				,							1)
                  _A					:     List[str]     = image.cpu().permute(0				,							2				,							3				,							1).float().numpy()

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

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=__snake_case)










'''simple docstring'''







from __future__ import annotations


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[list[int]]):
           _A					:     Dict     = TypeError(
               'Matrices must be formed from a list of zero or more lists containing at '
               'least one and the same number of values, each of which must be of type '
               'int or float.')
           if len(SCREAMING_SNAKE_CASE) != 0:
                  _A					:     str     = len(rows[0])
                  if cols == 0:
                         raise error
                  for row in rows:
                         if len(SCREAMING_SNAKE_CASE) != cols:
                                raise error
                         for value in row:
                                if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                                       raise error
                  _A					:     str     = rows
           else:
                  _A					:     Tuple     = []


    def        A   (  self      :							Optional[Any]):
           return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]


    @property
    def        A   (  self      :							List[str]):
           return len(self.rows)


    @property
    def        A   (  self      :							Optional[int]):
           return len(self.rows[0])


    @property
    def        A   (  self      :							Optional[int]):
           return (self.num_rows, self.num_columns)


    @property
    def        A   (  self      :							Any):
           return self.order[0] == self.order[1]


    def        A   (  self      :							int):
           _A					:     Union[str, Any]     = [
               [0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
               for row_num in range(self.num_rows)
           ]
           return Matrix(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[Any]):
           if not self.is_square:
                  return 0
           if self.order == (0, 0):
                  return 1
           if self.order == (1, 1):
                  return int(self.rows[0][0])
           if self.order == (2, 2):
                  return int(
                      (self.rows[0][0] * self.rows[1][1])
                      - (self.rows[0][1] * self.rows[1][0]))
           else:
                  return sum(
                      self.rows[0][column] * self.cofactors().rows[0][column]
                      for column in range(self.num_columns))


    def        A   (  self      :							Tuple):
           return bool(self.determinant())


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           _A					:     str     = [
               [
                   self.rows[other_row][other_column]
                   for other_column in range(self.num_columns)
                   if other_column != column
               ]
               for other_row in range(self.num_rows)
               if other_row != row
           ]
           return Matrix(SCREAMING_SNAKE_CASE).determinant()


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           if (row + column) % 2 == 0:
                  return self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           return -1 * self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           return Matrix(
               [
                   [self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for column in range(self.num_columns)]
                   for row in range(self.num_rows)
               ])


    def        A   (  self      :							Optional[int]):
           return Matrix(
               [
                   [
                       self.minors().rows[row][column]
                       if (row + column) % 2 == 0
                       else self.minors().rows[row][column] * -1
                       for column in range(self.minors().num_columns)
                   ]
                   for row in range(self.minors().num_rows)
               ])


    def        A   (  self      :							Tuple):
           _A					:     str     = [
               [self.cofactors().rows[column][row] for column in range(self.num_columns)]
               for row in range(self.num_rows)
           ]
           return Matrix(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Tuple):
           _A					:     str     = self.determinant()
           if not determinant:
                  raise TypeError('Only matrices with a non-zero determinant have an inverse')
           return self.adjugate() * (1 / determinant)


    def __repr__(  self      :							str):
           return str(self.rows)


    def __str__(  self      :							Optional[int]):
           if self.num_rows == 0:
                  return "[]"
           if self.num_rows == 1:
                  return "[[" + ". ".join(str(self.rows[0])) + "]]"
           return (
               "["
               + "\n ".join(
                   [
                       '[' + '. '.join([str(SCREAMING_SNAKE_CASE) for value in row]) + '.]'
                       for row in self.rows
                   ])
               + "]"
           )


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							int | None = None):
           _A					:     Tuple     = TypeError('Row must be a list containing all ints and/or floats')
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise type_error
           for value in row:
                  if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                         raise type_error
           if len(SCREAMING_SNAKE_CASE) != self.num_columns:
                  raise ValueError(
                      'Row must be equal in length to the other rows in the matrix')
           if position is None:
                  self.rows.append(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     List[str]     = self.rows[0:position] + [row] + self.rows[position:]


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							int | None = None):
           _A					:     int     = TypeError(
               'Column must be a list containing all ints and/or floats')
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise type_error
           for value in column:
                  if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                         raise type_error
           if len(SCREAMING_SNAKE_CASE) != self.num_rows:
                  raise ValueError(
                      'Column must be equal in length to the other columns in the matrix')
           if position is None:
                  _A					:     str     = [self.rows[i] + [column[i]] for i in range(self.num_rows)]
           else:
                  _A					:     Dict     = [
                      self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                      for i in range(self.num_rows)
                  ]


    def __eq__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							object):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  return NotImplemented
           return self.rows == other.rows


    def __ne__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							object):
           return not self == other


    def __neg__(  self      :							Optional[Any]):
           return self * -1


    def __add__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Matrix):
           if self.order != other.order:
                  raise ValueError('Addition requires matrices of the same order')
           return Matrix(
               [
                   [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
                   for i in range(self.num_rows)
               ])


    def __sub__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Matrix):
           if self.order != other.order:
                  raise ValueError('Subtraction requires matrices of the same order')
           return Matrix(
               [
                   [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
                   for i in range(self.num_rows)
               ])


    def __mul__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Matrix | int | float):
           if isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                  return Matrix(
                      [[int(element * other) for element in row] for row in self.rows])
           elif isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  if self.num_columns != other.num_rows:
                         raise ValueError(
                             'The number of columns in the first matrix must '
                             'be equal to the number of rows in the second')
                  return Matrix(
                      [
                          [Matrix.dot_product(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for column in other.columns()]
                          for row in self.rows
                      ])
           else:
                  raise TypeError(
                      'A Matrix can only be multiplied by an int, float, or another matrix')


    def __pow__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							int):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise TypeError('A Matrix can only be raised to the power of an int')
           if not self.is_square:
                  raise ValueError('Only square matrices can be raised to a power')
           if other == 0:
                  return self.identity()
           if other < 0:
                  if self.is_invertable():
                         return self.inverse() ** (-other)
                  raise ValueError(
                      'Only invertable matrices can be raised to a negative power')
           _A					:     Union[str, Any]     = self
           for _ in range(other - 1):
                  result *= self
           return result


    @classmethod
    def        A   (  cls      :							List[str]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							list[int]):
           return sum(row[i] * column[i] for i in range(len(SCREAMING_SNAKE_CASE)))


if __name__ == "__main__":
   import doctest

   doctest.testmod()






