"""simple docstring"""


from __future__ import annotations


class 							a					:


    def __init__(       self						:	Optional[int] ,					__lowerCAmelCase						:	list[list[int]]       ):
      _UpperCAmelCase    			=   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(__lowerCAmelCase       ) != 0:
        _UpperCAmelCase    			=   len(rows[0]       )
        if cols == 0:
          raise error
        for row in rows:
          if len(__lowerCAmelCase       ) != cols:
            raise error
          for value in row:
            if not isinstance(__lowerCAmelCase ,					(int, float)       ):
              raise error
        _UpperCAmelCase    			=   rows
      else:
        _UpperCAmelCase    			=   []


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      return [[row[i] for row in self.rows] for i in range(len(self.rows[0]       )       )]


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


    @property
    def   lowerCAmelCase_	(       self						:	str       ):
      return len(self.rows[0]       )


    @property
    def   lowerCAmelCase_	(       self						:	Any       ):
      return (self.num_rows, self.num_columns)


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


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   [
          [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(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      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   lowerCAmelCase_	(       self						:	str       ):
      return bool(self.determinant()       )


    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   [
          [
              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(__lowerCAmelCase       ).determinant()


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	int       ):
      if (row + column) % 2 == 0:
        return self.get_minor(__lowerCAmelCase ,					__lowerCAmelCase       )
      return -1 * self.get_minor(__lowerCAmelCase ,					__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Dict       ):
      return Matrix(
          [
              [self.get_minor(__lowerCAmelCase ,					__lowerCAmelCase       ) for column in range(self.num_columns       )]
              for row in range(self.num_rows       )
          ]       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      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   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   [
          [self.cofactors().rows[column][row] for column in range(self.num_columns       )]
          for row in range(self.num_rows       )
      ]
      return Matrix(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   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						:	Any       ):
      return str(self.rows       )


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


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	list[int] ,					__lowerCAmelCase						:	int | None = None       ):
      _UpperCAmelCase    			=   TypeError("""Row must be a list containing all ints and/or floats"""       )
      if not isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
        raise type_error
      for value in row:
        if not isinstance(__lowerCAmelCase ,					(int, float)       ):
          raise type_error
      if len(__lowerCAmelCase       ) != 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(__lowerCAmelCase       )
      else:
        _UpperCAmelCase    			=   self.rows[0:position] + [row] + self.rows[position:]


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


    def __eq__(       self						:	Any ,					__lowerCAmelCase						:	object       ):
      if not isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
        return NotImplemented
      return self.rows == other.rows


    def __ne__(       self						:	Tuple ,					__lowerCAmelCase						:	object       ):
      return not self == other


    def __neg__(       self						:	List[str]       ):
      return self * -1


    def __add__(       self						:	str ,					__lowerCAmelCase						:	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						:	Optional[int] ,					__lowerCAmelCase						:	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						:	List[str] ,					__lowerCAmelCase						:	Matrix | int | float       ):
      if isinstance(__lowerCAmelCase ,					(int, float)       ):
        return Matrix(
            [[int(element * other       ) for element in row] for row in self.rows]       )
      elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
        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(__lowerCAmelCase ,					__lowerCAmelCase       ) 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						:	Optional[int] ,					__lowerCAmelCase						:	int       ):
      if not isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
        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"""       )
      _UpperCAmelCase    			=   self
      for _ in range(other - 1       ):
        result *= self
      return result


    @classmethod
    def   lowerCAmelCase_	(       cls						:	Optional[Any] ,					__lowerCAmelCase						:	list[int] ,					__lowerCAmelCase						:	list[int]       ):
      return sum(row[i] * column[i] for i in range(len(__lowerCAmelCase       )       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

UpperCAmelCase__			=    """
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""


@dataclass
class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Union[PIL.Image.Image, np.ndarray]



class 							a					(		lowerCAmelCase_ ):


    def __init__(       self						:	Dict ,					__lowerCAmelCase						:	PriorTransformer ,					__lowerCAmelCase						:	CLIPVisionModel ,					__lowerCAmelCase						:	CLIPImageProcessor ,					__lowerCAmelCase						:	HeunDiscreteScheduler ,					__lowerCAmelCase						:	ShapERenderer ,					):
      super().__init__()

      self.register_modules(
          prior=__lowerCAmelCase ,					image_encoder=__lowerCAmelCase ,					image_processor=__lowerCAmelCase ,					scheduler=__lowerCAmelCase ,					renderer=__lowerCAmelCase ,					)


    def   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	List[Any]       ):
      if latents is None:
        _UpperCAmelCase    			=   randn_tensor(__lowerCAmelCase ,					generator=__lowerCAmelCase ,					device=__lowerCAmelCase ,					dtype=__lowerCAmelCase       )
      else:
        if latents.shape != shape:
          raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''       )
        _UpperCAmelCase    			=   latents.to(__lowerCAmelCase       )

      _UpperCAmelCase    			=   latents * scheduler.init_noise_sigma
      return latents


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	List[str]=0       ):
      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.image_encoder, self.prior]
      for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
          cpu_offload(__lowerCAmelCase ,					__lowerCAmelCase       )


    @property
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      if self.device != torch.device("""meta"""       ) or not hasattr(self.image_encoder ,					"""_hf_hook"""       ):
        return self.device
      for module in self.image_encoder.modules():
        if (
            hasattr(__lowerCAmelCase ,					"""_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


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Any ,					):
      if isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ) and isinstance(image[0] ,					torch.Tensor       ):
        _UpperCAmelCase    			=   torch.cat(__lowerCAmelCase ,					axis=0       ) if image[0].ndim == 4 else torch.stack(__lowerCAmelCase ,					axis=0       )

      if not isinstance(__lowerCAmelCase ,					torch.Tensor       ):
        _UpperCAmelCase    			=   self.image_processor(__lowerCAmelCase ,					return_tensors="""pt"""       ).pixel_values[0].unsqueeze(0       )

      _UpperCAmelCase    			=   image.to(dtype=self.image_encoder.dtype ,					device=__lowerCAmelCase       )

      _UpperCAmelCase    			=   self.image_encoder(__lowerCAmelCase       )["""last_hidden_state"""]
      _UpperCAmelCase    			=   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

      _UpperCAmelCase    			=   image_embeds.repeat_interleave(__lowerCAmelCase ,					dim=0       )

      if do_classifier_free_guidance:
        _UpperCAmelCase    			=   torch.zeros_like(__lowerCAmelCase       )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        _UpperCAmelCase    			=   torch.cat([negative_image_embeds, image_embeds]       )

      return image_embeds


    @torch.no_grad()
    @replace_example_docstring(__lowerCAmelCase       )
    def __call__(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Union[PIL.Image.Image, List[PIL.Image.Image]] ,					__lowerCAmelCase						:	int = 1 ,					__lowerCAmelCase						:	int = 25 ,					__lowerCAmelCase						:	Optional[Union[torch.Generator, List[torch.Generator]]] = None ,					__lowerCAmelCase						:	Optional[torch.FloatTensor] = None ,					__lowerCAmelCase						:	float = 4.0 ,					__lowerCAmelCase						:	int = 64 ,					__lowerCAmelCase						:	Optional[str] = "pil" ,					__lowerCAmelCase						:	bool = True ,					):

      if isinstance(__lowerCAmelCase ,					PIL.Image.Image       ):
        _UpperCAmelCase    			=   1
      elif isinstance(__lowerCAmelCase ,					torch.Tensor       ):
        _UpperCAmelCase    			=   image.shape[0]
      elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ) and isinstance(image[0] ,					(torch.Tensor, PIL.Image.Image)       ):
        _UpperCAmelCase    			=   len(__lowerCAmelCase       )
      else:
        raise ValueError(
            f'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__lowerCAmelCase       )}'''       )

      _UpperCAmelCase    			=   self._execution_device

      _UpperCAmelCase    			=   batch_size * num_images_per_prompt

      _UpperCAmelCase    			=   guidance_scale > 1.0
      _UpperCAmelCase    			=   self._encode_image(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )

      # prior

      self.scheduler.set_timesteps(__lowerCAmelCase ,					device=__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.scheduler.timesteps

      _UpperCAmelCase    			=   self.prior.config.num_embeddings
      _UpperCAmelCase    			=   self.prior.config.embedding_dim

      _UpperCAmelCase    			=   self.prepare_latents(
          (batch_size, num_embeddings * embedding_dim) ,					image_embeds.dtype ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					self.scheduler ,					)

      # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
      _UpperCAmelCase    			=   latents.reshape(latents.shape[0] ,					__lowerCAmelCase ,					__lowerCAmelCase       )

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

        _UpperCAmelCase    			=   self.prior(
            __lowerCAmelCase ,					timestep=__lowerCAmelCase ,					proj_embedding=__lowerCAmelCase ,					).predicted_image_embedding

        # remove the variance
        _UpperCAmelCase			,					_UpperCAmelCase    			=   noise_pred.split(
            scaled_model_input.shape[2] ,					dim=2       )  # batch_size, num_embeddings, embedding_dim

        if do_classifier_free_guidance is not None:
          _UpperCAmelCase			,					_UpperCAmelCase    			=   noise_pred.chunk(2       )
          _UpperCAmelCase    			=   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

        _UpperCAmelCase    			=   self.scheduler.step(
            __lowerCAmelCase ,					timestep=__lowerCAmelCase ,					sample=__lowerCAmelCase ,					).prev_sample

      if output_type == "latent":
        return ShapEPipelineOutput(images=__lowerCAmelCase       )

      _UpperCAmelCase    			=   []
      for i, latent in enumerate(__lowerCAmelCase       ):
        print()
        _UpperCAmelCase    			=   self.renderer.decode(
            latent[None, :] ,					__lowerCAmelCase ,					size=__lowerCAmelCase ,					ray_batch_size=4096 ,					n_coarse_samples=64 ,					n_fine_samples=128 ,					)

        images.append(__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.stack(__lowerCAmelCase       )

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

      _UpperCAmelCase    			=   images.cpu().numpy()

      if output_type == "pil":
        _UpperCAmelCase    			=   [self.numpy_to_pil(__lowerCAmelCase       ) for image in images]

      # Offload last model to CPU
      if hasattr(self ,					"""final_offload_hook"""       ) and self.final_offload_hook is not None:
        self.final_offload_hook.offload()

      if not return_dict:
        return (images,)

      return ShapEPipelineOutput(images=__lowerCAmelCase       )
