


from __future__ import annotations

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         snake_case					=    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(_a   ) != 0:
              snake_case					=    len(rows[0]   )
              if cols == 0:
                   raise error
              for row in rows:
                   if len(_a   ) != cols:
                        raise error
                   for value in row:
                        if not isinstance(_a      , (int, float)   ):
                             raise error
              snake_case					=    rows
         else:
              snake_case					=    []
    def 		a_      (	self   ):
         return [[row[i] for row in self.rows] for i in range(len(self.rows[0]   )   )]
    @property
    def 		a_      (	self   ):
         return len(self.rows   )
    @property
    def 		a_      (	self   ):
         return len(self.rows[0]   )
    @property
    def 		a_      (	self   ):
         return (self.num_rows, self.num_columns)
    @property
    def 		a_      (	self   ):
         return self.order[0] == self.order[1]
    def 		a_      (	self   ):
         snake_case					=    [
             [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(_a   )
    def 		a_      (	self   ):
         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 		a_      (	self   ):
         return bool(self.determinant()   )
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    [
             [
                 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(_a   ).determinant()
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         if (row + column) % 2 == 0:
              return self.get_minor(_a      , _a   )
         return -1 * self.get_minor(_a      , _a   )
    def 		a_      (	self   ):
         return Matrix(
             [
                 [self.get_minor(_a      , _a   ) for column in range(self.num_columns   )]
                 for row in range(self.num_rows   )
             ]   )
    def 		a_      (	self   ):
         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 		a_      (	self   ):
         snake_case					=    [
             [self.cofactors().rows[column][row] for column in range(self.num_columns   )]
             for row in range(self.num_rows   )
         ]
         return Matrix(_a   )
    def 		a_      (	self   ):
         snake_case					=    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   ):
         return str(self.rows   )
    def __str__(	self   ):
         if self.num_rows == 0:
              return "[]"
         if self.num_rows == 1:
              return "[[" + ". ".join(str(self.rows[0]   )   ) + "]]"
         return (
             "["
             + "\n ".join(
                 [
                     '''[''' + '''. '''.join([str(_a   ) for value in row]   ) + '''.]'''
                     for row in self.rows
                 ]   )
             + "]"
         )
    def 		a_      (	self      , __snake_case      , __snake_case = None   ):
         snake_case					=    TypeError('''Row must be a list containing all ints and/or floats'''   )
         if not isinstance(_a      , _a   ):
              raise type_error
         for value in row:
              if not isinstance(_a      , (int, float)   ):
                   raise type_error
         if len(_a   ) != 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(_a   )
         else:
              snake_case					=    self.rows[0:position] + [row] + self.rows[position:]
    def 		a_      (	self      , __snake_case      , __snake_case = None   ):
         snake_case					=    TypeError(
             '''Column must be a list containing all ints and/or floats'''   )
         if not isinstance(_a      , _a   ):
              raise type_error
         for value in column:
              if not isinstance(_a      , (int, float)   ):
                   raise type_error
         if len(_a   ) != self.num_rows:
              raise ValueError(
                  '''Column must be equal in length to the other columns in the matrix'''   )
         if position is None:
              snake_case					=    [self.rows[i] + [column[i]] for i in range(self.num_rows   )]
         else:
              snake_case					=    [
                  self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                  for i in range(self.num_rows   )
              ]
    def __eq__(	self      , __snake_case   ):
         if not isinstance(_a      , _a   ):
              return NotImplemented
         return self.rows == other.rows
    def __ne__(	self      , __snake_case   ):
         return not self == other
    def __neg__(	self   ):
         return self * -1
    def __add__(	self      , __snake_case   ):
         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      , __snake_case   ):
         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      , __snake_case   ):
         if isinstance(_a      , (int, float)   ):
              return Matrix(
                  [[int(element * other   ) for element in row] for row in self.rows]   )
         elif isinstance(_a      , _a   ):
              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(_a      , _a   ) 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      , __snake_case   ):
         if not isinstance(_a      , _a   ):
              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'''   )
         snake_case					=    self
         for _ in range(other - 1   ):
              result *= self
         return result




    @classmethod
    def 		a_      (	cls      , __snake_case      , __snake_case   ):
         return sum(row[i] * column[i] for i in range(len(_a   )   )   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()








import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		UnCLIPImageVariationPipeline
    __magic_name__        =		IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
    __magic_name__        =		IMAGE_VARIATION_BATCH_PARAMS

    __magic_name__        =		[
        'generator',
        'return_dict',
        'decoder_num_inference_steps',
        'super_res_num_inference_steps',
    ]
    __magic_name__        =		False
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return self.time_input_dim
    @property
    def 		a_      (	self   ):
         return self.time_input_dim * 4
    @property
    def 		a_      (	self   ):
         return 1_0_0
    @property
    def 		a_      (	self   ):
         snake_case					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
         return tokenizer
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    CLIPTextConfig(
             bos_token_id=0      , eos_token_id=2      , hidden_size=self.text_embedder_hidden_size      , projection_dim=self.text_embedder_hidden_size      , intermediate_size=3_7      , layer_norm_eps=1E-05      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1_0_0_0      , )
         return CLIPTextModelWithProjection(__snake_case   )
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size      , projection_dim=self.text_embedder_hidden_size      , num_hidden_layers=5      , num_attention_heads=4      , image_size=3_2      , intermediate_size=3_7      , patch_size=1      , )
         return CLIPVisionModelWithProjection(__snake_case   )
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    {
             '''clip_embeddings_dim''': self.text_embedder_hidden_size,
             '''time_embed_dim''': self.time_embed_dim,
             '''cross_attention_dim''': self.cross_attention_dim,
         }

         snake_case					=    UnCLIPTextProjModel(**__snake_case   )
         return model
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    {
             '''sample_size''': 3_2,
             # RGB in channels
             '''in_channels''': 3,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 6,
             '''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,
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': '''identity''',
         }

         snake_case					=    UNetaDConditionModel(**__snake_case   )
         return model
    @property
    def 		a_      (	self   ):
         return {
             "sample_size": 6_4,
             "layers_per_block": 1,
             "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
             "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "in_channels": 6,
             "out_channels": 3,
         }
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    UNetaDModel(**self.dummy_super_res_kwargs   )
         return model
    @property
    def 		a_      (	self   ):
         # seeded differently to get different unet than `self.dummy_super_res_first`
         torch.manual_seed(1   )

         snake_case					=    UNetaDModel(**self.dummy_super_res_kwargs   )
         return model
    def 		a_      (	self   ):
         snake_case					=    self.dummy_decoder
         snake_case					=    self.dummy_text_proj
         snake_case					=    self.dummy_text_encoder
         snake_case					=    self.dummy_tokenizer
         snake_case					=    self.dummy_super_res_first
         snake_case					=    self.dummy_super_res_last

         snake_case					=    UnCLIPScheduler(
             variance_type='''learned_range'''      , prediction_type='''epsilon'''      , num_train_timesteps=1_0_0_0      , )

         snake_case					=    UnCLIPScheduler(
             variance_type='''fixed_small_log'''      , prediction_type='''epsilon'''      , num_train_timesteps=1_0_0_0      , )

         snake_case					=    CLIPImageProcessor(crop_size=3_2      , size=3_2   )

         snake_case					=    self.dummy_image_encoder

         return {
             "decoder": decoder,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "text_proj": text_proj,
             "feature_extractor": feature_extractor,
             "image_encoder": image_encoder,
             "super_res_first": super_res_first,
             "super_res_last": super_res_last,
             "decoder_scheduler": decoder_scheduler,
             "super_res_scheduler": super_res_scheduler,
         }
    def 		a_      (	self      , __snake_case      , __snake_case=0      , __snake_case=True   ):
         snake_case					=    floats_tensor((1, 3, 3_2, 3_2)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         if str(__snake_case   ).startswith('''mps'''   ):
              snake_case					=    torch.manual_seed(__snake_case   )
         else:
              snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )

         if pil_image:
              snake_case					=    input_image * 0.5 + 0.5
              snake_case					=    input_image.clamp(0      , 1   )
              snake_case					=    input_image.cpu().permute(0      , 2      , 3      , 1   ).float().numpy()
              snake_case					=    DiffusionPipeline.numpy_to_pil(__snake_case   )[0]

         return {
             "image": input_image,
             "generator": generator,
             "decoder_num_inference_steps": 2,
             "super_res_num_inference_steps": 2,
             "output_type": "np",
         }
    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

         snake_case					=    image[0, -3:, -3:, -1]
         snake_case					=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case					=    np.array(
             [
                 0.9997,
                 0.0002,
                 0.9997,
                 0.9997,
                 0.9969,
                 0.0023,
                 0.9997,
                 0.9969,
                 0.9970,
             ]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

         snake_case					=    image[0, -3:, -3:, -1]
         snake_case					=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case					=    np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )
         snake_case					=    [
             pipeline_inputs['''image'''],
             pipeline_inputs['''image'''],
         ]

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )
         snake_case					=    [
             tuple_pipeline_inputs['''image'''],
             tuple_pipeline_inputs['''image'''],
         ]

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

         snake_case					=    image[0, -3:, -3:, -1]
         snake_case					=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (2, 6_4, 6_4, 3)

         snake_case					=    np.array(
             [
                 0.9997,
                 0.9989,
                 0.0008,
                 0.0021,
                 0.9960,
                 0.0018,
                 0.0014,
                 0.0002,
                 0.9933,
             ]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
    def 		a_      (	self   ):
         snake_case					=    torch.device('''cpu'''   )

         class   A__ :







             """simple docstring"""

             __magic_name__        =		1






         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(0   )
         snake_case					=    pipe.decoder.dtype
         snake_case					=    1

         snake_case					=    (
             batch_size,
             pipe.decoder.config.in_channels,
             pipe.decoder.config.sample_size,
             pipe.decoder.config.sample_size,
         )
         snake_case					=    pipe.prepare_latents(
             __snake_case      , dtype=__snake_case      , device=__snake_case      , generator=__snake_case      , latents=__snake_case      , scheduler=DummyScheduler()   )

         snake_case					=    (
             batch_size,
             pipe.super_res_first.config.in_channels // 2,
             pipe.super_res_first.config.sample_size,
             pipe.super_res_first.config.sample_size,
         )
         snake_case					=    pipe.prepare_latents(
             __snake_case      , dtype=__snake_case      , device=__snake_case      , generator=__snake_case      , latents=__snake_case      , scheduler=DummyScheduler()   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , decoder_latents=__snake_case      , super_res_latents=__snake_case   ).images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )
         # Don't pass image, instead pass embedding
         snake_case					=    pipeline_inputs.pop('''image'''   )
         snake_case					=    pipe.image_encoder(__snake_case   ).image_embeds

         snake_case					=    pipe(
             **__snake_case      , decoder_latents=__snake_case      , super_res_latents=__snake_case      , image_embeddings=__snake_case      , ).images

         # make sure passing text embeddings manually is identical
         assert np.abs(img_out_a - img_out_a   ).max() < 1E-4
    @skip_mps
    def 		a_      (	self   ):
         snake_case					=    torch_device == '''cpu'''

         # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
         snake_case					=    1E-2

         self._test_attention_slicing_forward_pass(
             test_max_difference=__snake_case      , expected_max_diff=__snake_case   )
    @skip_mps
    def 		a_      (	self   ):
         snake_case					=    torch_device == '''cpu'''
         snake_case					=    True
         snake_case					=    [
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         self._test_inference_batch_single_identical(
             test_max_difference=__snake_case      , relax_max_difference=__snake_case      , additional_params_copy_to_batched_inputs=__snake_case      , )
    def 		a_      (	self   ):
         snake_case					=    [
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         if torch_device == "mps":
              # TODO: MPS errors with larger batch sizes
              snake_case					=    [2, 3]
              self._test_inference_batch_consistent(
                  batch_sizes=__snake_case      , additional_params_copy_to_batched_inputs=__snake_case      , )
         else:
              self._test_inference_batch_consistent(
                  additional_params_copy_to_batched_inputs=__snake_case   )
    @skip_mps
    def 		a_      (	self   ):
         return super().test_dict_tuple_outputs_equivalent()
    @skip_mps
    def 		a_      (	self   ):
         return super().test_save_load_local()




    @skip_mps
    def 		a_      (	self   ):
         return super().test_save_load_optional_components()






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







    """simple docstring"""

    def 		a_      (	self   ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




    def 		a_      (	self   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''   )
         snake_case					=    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''   )

         snake_case					=    UnCLIPImageVariationPipeline.from_pretrained(
             '''kakaobrain/karlo-v1-alpha-image-variations'''      , torch_dtype=torch.floataa   )
         snake_case					=    pipeline.to(__snake_case   )
         pipeline.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    torch.Generator(device='''cpu'''   ).manual_seed(0   )
         snake_case					=    pipeline(
             __snake_case      , generator=__snake_case      , output_type='''np'''      , )

         snake_case					=    output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         assert_mean_pixel_difference(__snake_case      , __snake_case      , 1_5   )




