from __future__ import annotations

from typing import Any


def SCREAMING_SNAKE_CASE			(       *lowercase_:		list[list[int]] ):
       if all(_check_not_integer(lowercase_ ) for m in matrix_s ):
              for i in matrix_s[1:]:
                     _verify_matrix_sizes(matrix_s[0]						,  lowercase_ )
              return [[sum(lowercase_ ) for t in zip(*lowercase_ )] for m in zip(*lowercase_ )]
       raise TypeError('Expected a matrix, got int/list instead' )


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[list[int]]						,  lowercase_:		list[list[int]] ):
       if (
           _check_not_integer(lowercase_ )
           and _check_not_integer(lowercase_ )
           and _verify_matrix_sizes(lowercase_						,  lowercase_ )
       ):
              return [[i - j for i, j in zip(*lowercase_ )] for m in zip(lowercase_						,  lowercase_ )]
       raise TypeError('Expected a matrix, got int/list instead' )


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[list[int]]						,  lowercase_:		int | float ):
       return [[x * n for x in row] for row in matrix]


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[list[int]]						,  lowercase_:		list[list[int]] ):
       if _check_not_integer(lowercase_ ) and _check_not_integer(lowercase_ ):
              _UpperCamelCase       :    Dict					  =					_verify_matrix_sizes(lowercase_						,  lowercase_ )

       if cols[0] != rows[1]:
              _UpperCamelCase       :    Dict					  =					(
                  'Cannot multiply matrix of dimensions '
                  F"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
              )
              raise ValueError(lowercase_ )
       return [
           [sum(m * n for m, n in zip(lowercase_						,  lowercase_ ) ) for j in zip(*lowercase_ )] for i in matrix_a
       ]


def SCREAMING_SNAKE_CASE			(       lowercase_:		int ):
       _UpperCamelCase       :    int					  =					int(lowercase_ )
       return [[int(row == column ) for column in range(lowercase_ )] for row in range(lowercase_ )]


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[list[int]]						,  lowercase_:		bool = True ):
       if _check_not_integer(lowercase_ ):
              if return_map:
                     return map(lowercase_						,  zip(*lowercase_ ) )
              else:
                     return list(map(lowercase_						,  zip(*lowercase_ ) ) )
       raise TypeError('Expected a matrix, got int/list instead' )


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[list[int]]						,  lowercase_:		int						,  lowercase_:		int ):
       _UpperCamelCase       :    List[str]					  =					matrix[:row] + matrix[row + 1 :]
       return [row[:column] + row[column + 1 :] for row in minor]


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[list[int]] ):
       if len(lowercase_ ) == 1:
              return matrix[0][0]

       return sum(
           x * determinant(minor(lowercase_						,  0						,  lowercase_ ) ) * (-1) ** i
           for i, x in enumerate(matrix[0] ) )


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[list[int]] ):
       _UpperCamelCase       :    int					  =					determinant(lowercase_ )
       if det == 0:
              return None

       _UpperCamelCase       :    Tuple					  =					[
           [determinant(minor(lowercase_						,  lowercase_						,  lowercase_ ) ) for j in range(len(lowercase_ ) )]
           for i in range(len(lowercase_ ) )
       ]

       _UpperCamelCase       :    Dict					  =					[
           [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row] )]
           for row in range(len(lowercase_ ) )
       ]
       _UpperCamelCase       :    List[Any]					  =					list(transpose(lowercase_ ) )
       return scalar_multiply(lowercase_						,  1 / det )


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[list[int]] ):
       return not isinstance(lowercase_						,  lowercase_ ) and not isinstance(matrix[0]						,  lowercase_ )


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[list[int]] ):
       return len(lowercase_ ), len(matrix[0] )


def SCREAMING_SNAKE_CASE			(       lowercase_:		list[list[int]]						,  lowercase_:		list[list[int]] ):
       _UpperCamelCase       :    str					  =					_shape(lowercase_ ) + _shape(lowercase_ )
       if shape[0] != shape[3] or shape[1] != shape[2]:
              _UpperCamelCase       :    Optional[Any]					  =					(
                  'operands could not be broadcast together with shape '
                  F"({shape[0], shape[1]}), ({shape[2], shape[3]})"
              )
              raise ValueError(lowercase_ )
       return (shape[0], shape[2]), (shape[1], shape[3])


def SCREAMING_SNAKE_CASE			(       ):
       _UpperCamelCase       :    List[Any]					  =					[[12, 10], [3, 9]]
       _UpperCamelCase       :    Any					  =					[[3, 4], [7, 4]]
       _UpperCamelCase       :    List[Any]					  =					[[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
       _UpperCamelCase       :    Optional[int]					  =					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
       print(F"Add Operation, {add(lowercase_						,  lowercase_ ) = } \n" )
       print(
           F"Multiply Operation, {multiply(lowercase_						,  lowercase_ ) = } \n"						,  )
       print(F"Identity: {identity(5 )}\n" )
       print(F"Minor of {matrix_c} = {minor(lowercase_						,  1						,  2 )} \n" )
       print(F"Determinant of {matrix_b} = {determinant(lowercase_ )} \n" )
       print(F"Inverse of {matrix_d} = {inverse(lowercase_ )}\n" )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase     						=			False




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



 '''simple docstring'''

 def    __snake_case   (     self    :						Optional[Any]					)	->							str:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def    __snake_case   (     self    :						Dict					)	->							str:
        _UpperCamelCase       :    Dict					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Dict					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)

        _UpperCamelCase       :    Optional[int]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type='numpy'    ,     ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(lowerCamelCase					)
               _UpperCamelCase       :    int					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCamelCase					)

        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    str					  =					generator.manual_seed(0					)
        _UpperCamelCase       :    Tuple					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type='numpy'    ,     ).images

        assert np.abs(image - new_image					).sum() < 1e-5, "Models don't have the same forward pass"

 def    __snake_case   (     self    :						Tuple					)	->							Optional[int]:
        _UpperCamelCase       :    Optional[Any]					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Union[str, Any]					  =					'cyberpunk 2077'
        _UpperCamelCase       :    str					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
        _UpperCamelCase       :    List[Any]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt=lowerCamelCase    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=5_0    ,     output_type='numpy'    ,     ).images

        _UpperCamelCase       :    Any					  =					image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        _UpperCamelCase       :    Optional[int]					  =					np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]					)

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



