


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class _lowerCamelCase(		_a,					unittest.TestCase  ):
       lowercase_					:      Any       					=     KandinskyVaaControlnetImgaImgPipeline
       lowercase_					:      Tuple       					=     ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
       lowercase_					:      Optional[int]       					=     ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
       lowercase_					:      str       					=     [
           """generator""",
           """height""",
           """width""",
           """strength""",
           """guidance_scale""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowercase_					:      List[str]       					=     False
       @property
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           return 32
       @property
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           return 32
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return self.time_input_dim
       @property
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           return self.time_input_dim * 4
       @property
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           return 1_00
       @property
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           torch.manual_seed(0)

           _lowercase			:				Tuple 						=						{
               'in_channels': 8,
               # Out channels is double in channels because predicts mean and variance
               'out_channels': 8,
               'addition_embed_type': 'image_hint',
               'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
               'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
               'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
               'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
               'layers_per_block': 1,
               'encoder_hid_dim': self.text_embedder_hidden_size,
               'encoder_hid_dim_type': 'image_proj',
               'cross_attention_dim': self.cross_attention_dim,
               'attention_head_dim': 4,
               'resnet_time_scale_shift': 'scale_shift',
               'class_embed_type': None,
           }

           _lowercase			:				Tuple 						=						UNetaDConditionModel(**lowerCamelCase)
           return model
       @property
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           return {
               "block_out_channels": [32, 32, 64, 64],
               "down_block_types": [
                   "DownEncoderBlock2D",
                   "DownEncoderBlock2D",
                   "DownEncoderBlock2D",
                   "AttnDownEncoderBlock2D",
               ],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 12,
               "out_channels": 3,
               "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
               "vq_embed_dim": 4,
           }
       @property
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase			:				List[Any] 						=						VQModel(**self.dummy_movq_kwargs)
           return model
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				List[str] 						=						self.dummy_unet
           _lowercase			:				List[Any] 						=						self.dummy_movq

           _lowercase			:				Union[str, Any] 						=						{
               'num_train_timesteps': 10_00,
               'beta_schedule': 'linear',
               'beta_start': 0.0_0_0_8_5,
               'beta_end': 0.0_1_2,
               'clip_sample': False,
               'set_alpha_to_one': False,
               'steps_offset': 0,
               'prediction_type': 'epsilon',
               'thresholding': False,
           }

           _lowercase			:				Any 						=						DDIMScheduler(**lowerCamelCase)

           _lowercase			:				Optional[Any] 						=						{
               'unet': unet,
               'scheduler': scheduler,
               'movq': movq,
           }

           return components
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=0)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Dict 						=						floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase			:				Any 						=						floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1)).to(
               lowerCamelCase)
           # create init_image
           _lowercase			:				Tuple 						=						floats_tensor((1, 3, 64, 64), rng=random.Random(lowerCamelCase)).to(lowerCamelCase)
           _lowercase			:				str 						=						image.cpu().permute(0, 2, 3, 1)[0]
           _lowercase			:				Optional[int] 						=						Image.fromarray(np.uinta(lowerCamelCase)).convert('RGB').resize((2_56, 2_56))
           # create hint
           _lowercase			:				int 						=						floats_tensor((1, 3, 64, 64), rng=random.Random(lowerCamelCase)).to(lowerCamelCase)

           if str(lowerCamelCase).startswith('mps'):
               _lowercase			:				str 						=						torch.manual_seed(lowerCamelCase)
           else:
               _lowercase			:				Any 						=						torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase			:				Any 						=						{
               'image': init_image,
               'image_embeds': image_embeds,
               'negative_image_embeds': negative_image_embeds,
               'hint': hint,
               'generator': generator,
               'height': 64,
               'width': 64,
               'num_inference_steps': 10,
               'guidance_scale': 7.0,
               'strength': 0.2,
               'output_type': 'np',
           }
           return inputs




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




           """simple docstring"""

           _lowercase			:				Dict 						=						'cpu'

           _lowercase			:				Optional[Any] 						=						self.get_dummy_components()

           _lowercase			:				Any 						=						self.pipeline_class(**lowerCamelCase)
           _lowercase			:				str 						=						pipe.to(lowerCamelCase)

           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase			:				Any 						=						pipe(**self.get_dummy_inputs(lowerCamelCase))
           _lowercase			:				List[str] 						=						output.images

           _lowercase			:				List[Any] 						=						pipe(
               **self.get_dummy_inputs(lowerCamelCase), return_dict=lowerCamelCase, )[0]

           _lowercase			:				Tuple 						=						image[0, -3:, -3:, -1]
           _lowercase			:				Optional[Any] 						=						image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           _lowercase			:				int 						=						np.array(
               [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6])
           assert (
               np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
           ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
           ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class _lowerCamelCase(		unittest.TestCase  ):
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()




       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy')

           _lowercase			:				Optional[Any] 						=						load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
           _lowercase			:				Dict 						=						init_image.resize((5_12, 5_12))

           _lowercase			:				Union[str, Any] 						=						load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinskyv22/hint_image_cat.png')
           _lowercase			:				List[str] 						=						torch.from_numpy(np.array(lowerCamelCase)).float() / 2_5_5.0
           _lowercase			:				Union[str, Any] 						=						hint.permute(2, 0, 1).unsqueeze(0)

           _lowercase			:				int 						=						'A robot, 4k photo'

           _lowercase			:				List[Any] 						=						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-2-prior', torch_dtype=torch.floataa)
           pipe_prior.to(lowerCamelCase)

           _lowercase			:				Dict 						=						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-2-controlnet-depth', torch_dtype=torch.floataa)
           _lowercase			:				Union[str, Any] 						=						pipeline.to(lowerCamelCase)

           pipeline.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase			:				Tuple 						=						torch.Generator(device='cpu').manual_seed(0)

           _lowercase						,							_lowercase			:				str 						=						pipe_prior(
               lowerCamelCase, image=lowerCamelCase, strength=0.8_5, generator=lowerCamelCase, negative_prompt='', ).to_tuple()

           _lowercase			:				Union[str, Any] 						=						pipeline(
               image=lowerCamelCase, image_embeds=lowerCamelCase, negative_image_embeds=lowerCamelCase, hint=lowerCamelCase, generator=lowerCamelCase, num_inference_steps=1_00, height=5_12, width=5_12, strength=0.5, output_type='np', )

           _lowercase			:				Optional[Any] 						=						output.images[0]

           assert image.shape == (5_12, 5_12, 3)

           assert_mean_pixel_difference(lowerCamelCase, lowerCamelCase)






from __future__ import annotations

from typing import Any






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = 0)			->  None:




           """simple docstring"""

           _lowercase						,							_lowercase			:				str 						=						row, column
           _lowercase			:				Any 						=						[[default_value for c in range(lowerCamelCase)] for r in range(lowerCamelCase)]
       def __str__(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Tuple 						=						F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

           # Make string identifier
           _lowercase			:				str 						=						0
           for row_vector in self.array:
               for obj in row_vector:
                   _lowercase			:				Optional[int] 						=						max(lowerCamelCase, len(str(lowerCamelCase)))
           _lowercase			:				List[str] 						=						F'''%{max_element_length}s'''

           # Make string and return
           def single_line(lowerCamelCase) -> str:
               nonlocal string_format_identifier
               _lowercase			:				Union[str, Any] 						=						'['
               line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
               line += "]"
               return line

           s += "\n".join(single_line(lowerCamelCase) for row_vector in self.array)
           return s
       def __repr__(   self)			->  str:




           """simple docstring"""

           return str(self)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  bool:




           """simple docstring"""

           if not (isinstance(lowerCamelCase, (list, tuple)) and len(lowerCamelCase) == 2):
               return False
           elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
               return False
           else:
               return True
       def __getitem__(   self, lowerCamelCase)			->  Any:




           """simple docstring"""

           assert self.validate_indicies(lowerCamelCase)
           return self.array[loc[0]][loc[1]]
       def __setitem__(   self, lowerCamelCase, lowerCamelCase)			->  None:




           """simple docstring"""

           assert self.validate_indicies(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						value
       def __add__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           assert isinstance(lowerCamelCase, lowerCamelCase)
           assert self.row == another.row and self.column == another.column

           # Add
           _lowercase			:				Any 						=						Matrix(self.row, self.column)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				int 						=						self[r, c] + another[r, c]
           return result
       def __neg__(   self)			->  Matrix:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						Matrix(self.row, self.column)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				List[str] 						=						-self[r, c]
           return result
       def __sub__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           return self + (-another)
       def __mul__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           if isinstance(lowerCamelCase, (int, float)):  # Scalar multiplication
               _lowercase			:				Dict 						=						Matrix(self.row, self.column)
               for r in range(self.row):
                   for c in range(self.column):
                       _lowercase			:				Union[str, Any] 						=						self[r, c] * another
               return result
           elif isinstance(lowerCamelCase, lowerCamelCase):  # Matrix multiplication
               assert self.column == another.row
               _lowercase			:				str 						=						Matrix(self.row, another.column)
               for r in range(self.row):
                   for c in range(another.column):
                       for i in range(self.column):
                           result[r, c] += self[r, i] * another[i, c]
               return result
           else:
               _lowercase			:				Tuple 						=						F'''Unsupported type given for another ({type(lowerCamelCase)})'''
               raise TypeError(lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Matrix:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						Matrix(self.column, self.row)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				Union[str, Any] 						=						self[r, c]
           return result




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Any:




           """simple docstring"""

           assert isinstance(lowerCamelCase, lowerCamelCase) and isinstance(lowerCamelCase, lowerCamelCase)
           assert self.row == self.column == u.row == v.row  # u, v should be column vector
           assert u.column == v.column == 1  # u, v should be column vector

           # Calculate
           _lowercase			:				Dict 						=						v.transpose()
           _lowercase			:				Any 						=						(v_t * self * u)[0, 0] + 1
           if numerator_factor == 0:
               return None  # It's not invertable
           return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



 def 							UpperCamelCase_( )       ->		None:
     # a^(-1)
     _lowercase			:				Optional[int] 						=						Matrix(3				,					3				,					0					)
     for i in range(3					):
         _lowercase			:				int 						=						1
     print(F'''a^(-1) is {ainv}'''					)
     # u, v
     _lowercase			:				Dict 						=						Matrix(3				,					1				,					0					)
     _lowercase						,							_lowercase						,							_lowercase			:				Dict 						=						1, 2, -3
     _lowercase			:				List[Any] 						=						Matrix(3				,					1				,					0					)
     _lowercase						,							_lowercase						,							_lowercase			:				int 						=						4, -2, 5
     print(F'''u is {u}'''					)
     print(F'''v is {v}'''					)
     print(F'''uv^T is {u * v.transpose()}'''					)
     # Sherman Morrison
     print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(lowerCamelCase_				,					lowerCamelCase_					)}'''					)



 def 							UpperCamelCase_( )       ->		None:
     import doctest

     doctest.testmod()

 testa()


