





import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class     __lowerCAmelCase	(   UpperCamelCase__						,    unittest.TestCase):
      _lowercase    : str            =      DDIMPipeline
      _lowercase    : List[Any]            =      UNCONDITIONAL_IMAGE_GENERATION_PARAMS
      _lowercase    : List[str]            =      PipelineTesterMixin.required_optional_params - {
          """num_images_per_prompt""",
          """latents""",
          """callback""",
          """callback_steps""",
      }
      _lowercase    : Union[str, Any]            =      UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
      _lowercase    : Union[str, Any]            =      False







      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__     : List[str]  =UNetaDModel(
            block_out_channels=(3_2, 6_4)		,				layers_per_block=2		,				sample_size=3_2		,				in_channels=3		,				out_channels=3		,				down_block_types=("DownBlock2D", "AttnDownBlock2D")		,				up_block_types=("AttnUpBlock2D", "UpBlock2D")		,				)
        a__     : Any  =DDIMScheduler()
        a__     : List[Any]  ={"unet": unet, "scheduler": scheduler}
        return components







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__=0							)       ->						List[Any]:



        '''simple docstring'''




        if str(lowerCAmelCase__							).startswith("mps"							):
          a__     : List[str]  =torch.manual_seed(lowerCAmelCase__							)
        else:
          a__     : Optional[int]  =torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
        a__     : Union[str, Any]  ={
            "batch_size": 1,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "numpy",
        }
        return inputs







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : int  ="cpu"

        a__     : Union[str, Any]  =self.get_dummy_components()
        a__     : int  =self.pipeline_class(**lowerCAmelCase__							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__     : Any  =self.get_dummy_inputs(lowerCAmelCase__							)
        a__     : Tuple  =pipe(**lowerCAmelCase__							).images
        a__     : Tuple  =image[0, -3:, -3:, -1]

        self.assertEqual(image.shape		,				(1, 3_2, 3_2, 3)							)
        a__     : str  =np.array(
            [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]							)
        a__     : List[Any]  =np.abs(image_slice.flatten() - expected_slice							).max()
        self.assertLessEqual(lowerCAmelCase__		,				1E-3							)







      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        super().test_save_load_local(expected_max_difference=3E-3							)







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        super().test_save_load_optional_components(expected_max_difference=3E-3							)







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        super().test_inference_batch_single_identical(expected_max_diff=3E-3							)







@slow
@require_torch_gpu
class     __lowerCAmelCase	(   unittest.TestCase):







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : List[str]  ="google/ddpm-cifar10-32"

        a__     : str  =UNetaDModel.from_pretrained(lowerCAmelCase__							)
        a__     : List[str]  =DDIMScheduler()

        a__     : Any  =DDIMPipeline(unet=lowerCAmelCase__		,				scheduler=lowerCAmelCase__							)
        ddim.to(lowerCAmelCase__							)
        ddim.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__     : Any  =torch.manual_seed(0							)
        a__     : Optional[int]  =ddim(generator=lowerCAmelCase__		,				eta=0.0		,				output_type="numpy"							).images

        a__     : int  =image[0, -3:, -3:, -1]

        assert image.shape == (1, 3_2, 3_2, 3)
        a__     : List[Any]  =np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Union[str, Any]  ="google/ddpm-ema-bedroom-256"

        a__     : Dict  =UNetaDModel.from_pretrained(lowerCAmelCase__							)
        a__     : Any  =DDIMScheduler.from_pretrained(lowerCAmelCase__							)

        a__     : Union[str, Any]  =DDIMPipeline(unet=lowerCAmelCase__		,				scheduler=lowerCAmelCase__							)
        ddpm.to(lowerCAmelCase__							)
        ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__     : Any  =torch.manual_seed(0							)
        a__     : List[str]  =ddpm(generator=lowerCAmelCase__		,				output_type="numpy"							).images

        a__     : Union[str, Any]  =image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_5_6, 2_5_6, 3)
        a__     : Optional[Any]  =np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2













from __future__ import annotations







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : int  =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:
          a__     : List[str]  =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
          a__     : List[Any]  =rows
        else:
          a__     : str  =[]







      def        _lowercase  (   self							)       ->						list[list[int]]:



        '''simple docstring'''




        return [[row[i] for row in self.rows] for i in range(len(self.rows[0]							)							)]







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.rows							)







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.rows[0]							)







      @property
      def        _lowercase  (   self							)       ->						tuple[int, int]:



        '''simple docstring'''




        return (self.num_rows, self.num_columns)







      @property
      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        return self.order[0] == self.order[1]







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : str  =[
            [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        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        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        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        return bool(self.determinant()							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : List[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(lowerCAmelCase__							).determinant()







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        if (row + column) % 2 == 0:
          return self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							)
        return -1 * self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        return Matrix(
            [
                [self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							) for column in range(self.num_columns							)]
                for row in range(self.num_rows							)
            ]							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        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        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : Dict  =[
            [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
            for row in range(self.num_rows							)
        ]
        return Matrix(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : Union[str, Any]  =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:



        '''simple docstring'''




        return str(self.rows							)







      def __str__(   self							)       ->						str:



        '''simple docstring'''




        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        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




        a__     : List[str]  =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:
          a__     : Tuple  =self.rows[0:position] + [row] + self.rows[position:]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




        a__     : str  =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:
          a__     : Optional[Any]  =[self.rows[i] + [column[i]] for i in range(self.num_rows							)]
        else:
          a__     : Any  =[
              self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
              for i in range(self.num_rows							)
          ]







      def __eq__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          return NotImplemented
        return self.rows == other.rows







      def __ne__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




        return not self == other







      def __neg__(   self							)       ->						Matrix:



        '''simple docstring'''




        return self * -1







      def __add__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        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		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        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		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        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		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        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"							)
        a__     : Tuple  =self
        for _ in range(other - 1							):
          result *= self
        return result







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        return sum(row[i] * column[i] for i in range(len(lowerCAmelCase__							)							)							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






