


from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class 				SCREAMING_SNAKE_CASE__						:
      def __init__( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Collection[float] | None = None  )	->    None:
             if components is None:
                    a_      :	List[Any]							=						[]
             a_      :	Tuple							=						list(SCREAMING_SNAKE_CASE__  )
      def __len__( self       : List[str]  )	->    int:
             return len(self.__components  )
      def __str__( self       : Any  )	->    str:
             return "(" + ",".join(map(SCREAMING_SNAKE_CASE__  ,							self.__components  )  ) + ")"
      def __add__( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Vector  )	->    Vector:
             a_      :	Optional[Any]							=						len(self  )
             if size == len(SCREAMING_SNAKE_CASE__  ):
                    a_      :	List[Any]							=						[self.__components[i] + other.component(SCREAMING_SNAKE_CASE__  ) for i in range(SCREAMING_SNAKE_CASE__  )]
                    return Vector(SCREAMING_SNAKE_CASE__  )
             else:
                    raise Exception('must have the same size'  )
      def __sub__( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Vector  )	->    Vector:
             a_      :	int							=						len(self  )
             if size == len(SCREAMING_SNAKE_CASE__  ):
                    a_      :	Union[str, Any]							=						[self.__components[i] - other.component(SCREAMING_SNAKE_CASE__  ) for i in range(SCREAMING_SNAKE_CASE__  )]
                    return Vector(SCREAMING_SNAKE_CASE__  )
             else:  # error case
                    raise Exception('must have the same size'  )
      @overload
      def __mul__( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : float  )	->    Vector:
             ...
      @overload
      def __mul__( self       : str  ,							SCREAMING_SNAKE_CASE__       : Vector  )	->    float:
             ...
      def __mul__( self       : int  ,							SCREAMING_SNAKE_CASE__       : float | Vector  )	->    float | Vector:
             if isinstance(SCREAMING_SNAKE_CASE__  ,							(float, int)  ):
                    a_      :	List[Any]							=						[c * other for c in self.__components]
                    return Vector(SCREAMING_SNAKE_CASE__  )
             elif isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ) and len(self  ) == len(SCREAMING_SNAKE_CASE__  ):
                    a_      :	int							=						len(self  )
                    a_      :	Tuple							=						[self.__components[i] * other.component(SCREAMING_SNAKE_CASE__  ) for i in range(SCREAMING_SNAKE_CASE__  )]
                    return sum(SCREAMING_SNAKE_CASE__  )
             else:  # error case
                    raise Exception('invalid operand!'  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Vector:
             return Vector(self.__components  )
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							SCREAMING_SNAKE_CASE__       : int  )	->    float:
             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ) and -len(self.__components  ) <= i < len(self.__components  ):
                    return self.__components[i]
             else:
                    raise Exception('index out of range'  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : float  )	->    None:
             assert -len(self.__components  ) <= pos < len(self.__components  )
             a_      :	Dict							=						value
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    float:
             if len(self.__components  ) == 0:
                    raise Exception('Vector is empty'  )
             a_      :	Optional[int]							=						[c**2 for c in self.__components]
             return math.sqrt(sum(SCREAMING_SNAKE_CASE__  )  )

      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Vector  ,							SCREAMING_SNAKE_CASE__       : bool = False  )	->    float:
             a_      :	Union[str, Any]							=						self * other
             a_      :	Optional[int]							=						self.euclidean_length() * other.euclidean_length()
             if deg:
                    return math.degrees(math.acos(num / den  )  )
             else:
                    return math.acos(num / den  )







def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	Vector:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       return Vector([0] * dimension       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    int       )			->	Vector:

       """simple docstring"""

       assert isinstance(__A						,					__A       ) and (isinstance(__A						,					__A       ))
       a_      :	int							=						[0] * dimension
       a_      :	Any							=						1
       return Vector(__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    float						,					__A				:    Vector						,					__A				:    Vector       )			->	Vector:

       """simple docstring"""

       assert (
           isinstance(__A						,					__A       )
           and isinstance(__A						,					__A       )
           and (isinstance(__A						,					(int, float)       ))
       )
       return x * scalar + y







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    int						,					__A				:    int       )			->	Vector:

       """simple docstring"""

       random.seed(__A       )
       a_      :	Dict							=						[random.randint(__A						,					__A       ) for _ in range(__A       )]
       return Vector(__A       )
class 				SCREAMING_SNAKE_CASE__						:
      def __init__( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : list[list[float]]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : int  )	->    None:
             a_      :	Optional[Any]							=						matrix
             a_      :	Dict							=						w
             a_      :	str							=						h
      def __str__( self       : Dict  )	->    str:
             a_      :	Optional[Any]							=						''
             for i in range(self.__height  ):
                    ans += "|"
                    for j in range(self.__width  ):
                           if j < self.__width - 1:
                                  ans += str(self.__matrix[i][j]  ) + ","
                           else:
                                  ans += str(self.__matrix[i][j]  ) + "|\n"
             return ans
      def __add__( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Matrix  )	->    Matrix:
             if self.__width == other.width() and self.__height == other.height():
                    a_      :	List[Any]							=						[]
                    for i in range(self.__height  ):
                           a_      :	Tuple							=						[
                               self.__matrix[i][j] + other.component(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                               for j in range(self.__width  )
                           ]
                           matrix.append(SCREAMING_SNAKE_CASE__  )
                    return Matrix(SCREAMING_SNAKE_CASE__  ,							self.__width  ,							self.__height  )
             else:
                    raise Exception('matrix must have the same dimension!'  )
      def __sub__( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Matrix  )	->    Matrix:
             if self.__width == other.width() and self.__height == other.height():
                    a_      :	Union[str, Any]							=						[]
                    for i in range(self.__height  ):
                           a_      :	Tuple							=						[
                               self.__matrix[i][j] - other.component(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                               for j in range(self.__width  )
                           ]
                           matrix.append(SCREAMING_SNAKE_CASE__  )
                    return Matrix(SCREAMING_SNAKE_CASE__  ,							self.__width  ,							self.__height  )
             else:
                    raise Exception('matrices must have the same dimension!'  )
      @overload
      def __mul__( self       : int  ,							SCREAMING_SNAKE_CASE__       : float  )	->    Matrix:
             ...
      @overload
      def __mul__( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Vector  )	->    Vector:
             ...
      def __mul__( self       : Any  ,							SCREAMING_SNAKE_CASE__       : float | Vector  )	->    Vector | Matrix:
             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):  # matrix-vector
                    if len(SCREAMING_SNAKE_CASE__  ) == self.__width:
                           a_      :	Dict							=						zero_vector(self.__height  )
                           for i in range(self.__height  ):
                                  a_      :	Union[str, Any]							=						[
                                      self.__matrix[i][j] * other.component(SCREAMING_SNAKE_CASE__  )
                                      for j in range(self.__width  )
                                  ]
                                  ans.change_component(SCREAMING_SNAKE_CASE__  ,							sum(SCREAMING_SNAKE_CASE__  )  )
                           return ans
                    else:
                           raise Exception(
                               'vector must have the same size as the '
                               'number of columns of the matrix!'  )
             elif isinstance(SCREAMING_SNAKE_CASE__  ,							(int, float)  ):  # matrix-scalar
                    a_      :	Tuple							=						[
                        [self.__matrix[i][j] * other for j in range(self.__width  )]
                        for i in range(self.__height  )
                    ]
                    return Matrix(SCREAMING_SNAKE_CASE__  ,							self.__width  ,							self.__height  )
             return None
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    int:
             return self.__height
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             return self.__width
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : int  )	->    float:
             if 0 <= x < self.__height and 0 <= y < self.__width:
                    return self.__matrix[x][y]
             else:
                    raise Exception('change_component: indices out of bounds'  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : float  )	->    None:
             if 0 <= x < self.__height and 0 <= y < self.__width:
                    a_      :	List[Any]							=						value
             else:
                    raise Exception('change_component: indices out of bounds'  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : int  )	->    float:
             if self.__height != self.__width:
                    raise Exception('Matrix is not square'  )
             a_      :	List[Any]							=						self.__matrix[:x] + self.__matrix[x + 1 :]
             for i in range(len(SCREAMING_SNAKE_CASE__  )  ):
                    a_      :	List[Any]							=						minor[i][:y] + minor[i][y + 1 :]
             return Matrix(SCREAMING_SNAKE_CASE__  ,							self.__width - 1  ,							self.__height - 1  ).determinant()
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : int  )	->    float:
             if self.__height != self.__width:
                    raise Exception('Matrix is not square'  )
             if 0 <= x < self.__height and 0 <= y < self.__width:
                    return (-1) ** (x + y) * self.minor(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             else:
                    raise Exception('Indices out of bounds'  )

      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    float:
             if self.__height != self.__width:
                    raise Exception('Matrix is not square'  )
             if self.__height < 1:
                    raise Exception('Matrix has no element'  )
             elif self.__height == 1:
                    return self.__matrix[0][0]
             elif self.__height == 2:
                    return (
                        self.__matrix[0][0] * self.__matrix[1][1]
                        - self.__matrix[0][1] * self.__matrix[1][0]
                    )
             else:
                    a_      :	str							=						[
                        self.__matrix[0][y] * self.cofactor(0  ,							SCREAMING_SNAKE_CASE__  ) for y in range(self.__width  )
                    ]
                    return sum(SCREAMING_SNAKE_CASE__  )







def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	Matrix:

       """simple docstring"""

       a_      :	list[list[float]]							=						[[0] * n for _ in range(__A       )]
       return Matrix(__A						,					__A						,					__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    int						,					__A				:    int						,					__A				:    int       )			->	Matrix:

       """simple docstring"""

       random.seed(__A       )
       a_      :	list[list[float]]							=						[
           [random.randint(__A						,					__A       ) for _ in range(__A       )] for _ in range(__A       )
       ]
       return Matrix(__A						,					__A						,					__A       )






from typing import List, Optional, Union

import torch

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,
)


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

UpperCAmelCase_   :				str						      = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Tuple						,					__A				:    Tuple=8       )			->	Dict:

       """simple docstring"""

       a_      :	Optional[int]							=						height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       a_      :	int							=						width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : UNetaDConditionModel  ,							SCREAMING_SNAKE_CASE__       : DDPMScheduler  ,							SCREAMING_SNAKE_CASE__       : VQModel  ,							)	->    Any:
             super().__init__()

             self.register_modules(
                 unet=SCREAMING_SNAKE_CASE__  ,							scheduler=SCREAMING_SNAKE_CASE__  ,							movq=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	List[str]							=						2 ** (len(self.movq.config.block_out_channels  ) - 1)
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    str:
             if latents is None:
                    a_      :	List[str]							=						randn_tensor(SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							device=SCREAMING_SNAKE_CASE__  ,							dtype=SCREAMING_SNAKE_CASE__  )
             else:
                    if latents.shape != shape:
                           raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
                    a_      :	Any							=						latents.to(SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						latents * scheduler.init_noise_sigma
             return latents
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0  )	->    Dict:
             if is_accelerate_available():
                    from accelerate import cpu_offload
             else:
                    raise ImportError('Please install accelerate via `pip install accelerate`'  )

             a_      :	Union[str, Any]							=						torch.device(F"""cuda:{gpu_id}"""  )

             a_      :	Optional[Any]							=						[
                 self.unet,
                 self.movq,
             ]
             for cpu_offloaded_model in models:
                    if cpu_offloaded_model is not None:
                           cpu_offload(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0  )	->    Dict:
             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_      :	Tuple							=						torch.device(F"""cuda:{gpu_id}"""  )

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

             a_      :	Dict							=						None
             for cpu_offloaded_model in [self.unet, self.movq]:
                    a_							,	a_      :	Optional[Any]							=						cpu_offload_with_hook(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							prev_module_hook=SCREAMING_SNAKE_CASE__  )

             # We'll offload the last model manually.
             a_      :	str							=						hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[Any]:
             if not hasattr(self.unet  ,							'_hf_hook'  ):
                    return self.device
             for module in self.unet.modules():
                    if (
                        hasattr(SCREAMING_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(SCREAMING_SNAKE_CASE__  )
      def __call__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							SCREAMING_SNAKE_CASE__       : Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							SCREAMING_SNAKE_CASE__       : torch.FloatTensor  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 1_0_0  ,							SCREAMING_SNAKE_CASE__       : float = 4.0  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[torch.FloatTensor] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[str] = "pil"  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							)	->    int:
             a_      :	Dict							=						self._execution_device

             a_      :	int							=						guidance_scale > 1.0

             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Union[str, Any]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )
             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Optional[Any]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )
             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Optional[int]							=						torch.cat(SCREAMING_SNAKE_CASE__  ,							dim=0  )

             a_      :	Dict							=						image_embeds.shape[0] * num_images_per_prompt

             if do_classifier_free_guidance:
                    a_      :	Optional[Any]							=						image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )
                    a_      :	Optional[Any]							=						negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )
                    a_      :	int							=						hint.repeat_interleave(SCREAMING_SNAKE_CASE__  ,							dim=0  )

             a_      :	int							=						torch.cat([negative_image_embeds, image_embeds]  ,							dim=0  ).to(dtype=self.unet.dtype  ,							device=SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						torch.cat([hint, hint]  ,							dim=0  ).to(dtype=self.unet.dtype  ,							device=SCREAMING_SNAKE_CASE__  )
             self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  ,							device=SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						self.scheduler.timesteps

             a_      :	Optional[int]							=						self.movq.config.latent_channels

             a_							,	a_      :	Dict							=						downscale_height_and_width(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							self.movq_scale_factor  )

             # create initial latent
             a_      :	int							=						self.prepare_latents(
                 (batch_size, num_channels_latents, height, width)  ,							image_embeds.dtype  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							self.scheduler  ,							)

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

                    a_      :	int							=						{'image_embeds': image_embeds, 'hint': hint}
                    a_      :	str							=						self.unet(
                        sample=SCREAMING_SNAKE_CASE__  ,							timestep=SCREAMING_SNAKE_CASE__  ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__  ,							added_cond_kwargs=SCREAMING_SNAKE_CASE__  ,							return_dict=SCREAMING_SNAKE_CASE__  ,							)[0]

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

                    # compute the previous noisy sample x_t -> x_t-1
                    a_      :	str							=						self.scheduler.step(
                        SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							)[0]
             # post-processing
             a_      :	str							=						self.movq.decode(SCREAMING_SNAKE_CASE__  ,							force_not_quantize=SCREAMING_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_      :	str							=						image * 0.5 + 0.5
                    a_      :	Union[str, Any]							=						image.clamp(0  ,							1  )
                    a_      :	Optional[int]							=						image.cpu().permute(0  ,							2  ,							3  ,							1  ).float().numpy()

             if output_type == "pil":
                    a_      :	List[str]							=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__  )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__  )


