






'''simple docstring'''

from __future__ import annotations




class      A     :






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Optional[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:
             UpperCamelCase_					: 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
             UpperCamelCase_					: int            =				rows
       else:
             UpperCamelCase_					: Any            =				[]
 def 	_UpperCAmelCase							(				self			):
       return [[row[i] for row in self.rows] for i in range(len(self.rows[0]			)			)]
 @property
 def 	_UpperCAmelCase							(				self			):
       return len(self.rows			)
 @property
 def 	_UpperCAmelCase							(				self			):
       return len(self.rows[0]			)
 @property
 def 	_UpperCAmelCase							(				self			):
       return (self.num_rows, self.num_columns)
 @property
 def 	_UpperCAmelCase							(				self			):
       return self.order[0] == self.order[1]
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Tuple            =				[
           [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 	_UpperCAmelCase							(				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 	_UpperCAmelCase							(				self			):
       return bool(self.determinant()			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Tuple            =				[
           [
               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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       if (row + column) % 2 == 0:
             return self.get_minor(__lowerCAmelCase      ,  __lowerCAmelCase			)
       return -1 * self.get_minor(__lowerCAmelCase      ,  __lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       return Matrix(
           [
               [self.get_minor(__lowerCAmelCase      ,  __lowerCAmelCase			) for column in range(self.num_columns			)]
               for row in range(self.num_rows			)
           ]			)
 def 	_UpperCAmelCase							(				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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				[
           [self.cofactors().rows[column][row] for column in range(self.num_columns			)]
           for row in range(self.num_rows			)
       ]
       return Matrix(__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				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(__lowerCAmelCase			) for value in row]			) + """.]"""
                   for row in self.rows
               ]			)
           + "]"
       )
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None			):
       UpperCamelCase_					: Any            =				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_					: Any            =				self.rows[0:position] + [row] + self.rows[position:]
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None			):
       UpperCamelCase_					: Union[str, Any]            =				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_					: str            =				[self.rows[i] + [column[i]] for i in range(self.num_rows			)]
       else:
             UpperCamelCase_					: Tuple            =				[
                 self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                 for i in range(self.num_rows			)
             ]
 def __eq__(				self      ,  __lowerCAmelCase			):
       if not isinstance(__lowerCAmelCase      ,  __lowerCAmelCase			):
             return NotImplemented
       return self.rows == other.rows
 def __ne__(				self      ,  __lowerCAmelCase			):
       return not self == other
 def __neg__(				self			):
       return self * -1
 def __add__(				self      ,  __lowerCAmelCase			):
       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			):
       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			):
       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			):
       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_					: int            =				self
       for _ in range(other - 1			):
             result *= self
       return result





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


if __name__ == "__main__":
      import doctest

      doctest.testmod()














'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


UpperCamelCase      					=False




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 @property
 def 	_UpperCAmelCase							(				self			):
       return 12
 @property
 def 	_UpperCAmelCase							(				self			):
       return 12
 @property
 def 	_UpperCAmelCase							(				self			):
       return 32
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: Union[str, Any]            =				VQModel(
           block_out_channels=[32, 64]      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,  latent_channels=3      ,  num_vq_embeddings=self.num_embed      ,  vq_embed_dim=3      ,  )
       return model
 @property
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
       return tokenizer
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: Any            =				CLIPTextConfig(
           bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=self.text_embedder_hidden_size      ,  intermediate_size=37      ,  layer_norm_eps=1E-05      ,  num_attention_heads=4      ,  num_hidden_layers=5      ,  pad_token_id=1      ,  vocab_size=10_00      ,  )
       return CLIPTextModel(__lowerCAmelCase			)
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)

       UpperCamelCase_					: Optional[Any]            =				12
       UpperCamelCase_					: Optional[int]            =				12

       UpperCamelCase_					: Optional[Any]            =				{
           """attention_bias""": True,
           """cross_attention_dim""": 32,
           """attention_head_dim""": height * width,
           """num_attention_heads""": 1,
           """num_vector_embeds""": self.num_embed,
           """num_embeds_ada_norm""": self.num_embeds_ada_norm,
           """norm_num_groups""": 32,
           """sample_size""": width,
           """activation_fn""": """geglu-approximate""",
       }

       UpperCamelCase_					: Optional[Any]            =				TransformeraDModel(**__lowerCAmelCase			)
       return model
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				"""cpu"""

       UpperCamelCase_					: List[str]            =				self.dummy_vqvae
       UpperCamelCase_					: str            =				self.dummy_text_encoder
       UpperCamelCase_					: Optional[Any]            =				self.dummy_tokenizer
       UpperCamelCase_					: Optional[int]            =				self.dummy_transformer
       UpperCamelCase_					: Optional[Any]            =				VQDiffusionScheduler(self.num_embed			)
       UpperCamelCase_					: Union[str, Any]            =				LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				VQDiffusionPipeline(
           vqvae=__lowerCAmelCase      ,  text_encoder=__lowerCAmelCase      ,  tokenizer=__lowerCAmelCase      ,  transformer=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  learned_classifier_free_sampling_embeddings=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Union[str, Any]            =				pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Optional[int]            =				"""teddy bear playing in the pool"""

       UpperCamelCase_					: Union[str, Any]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: List[Any]            =				pipe([prompt]      ,  generator=__lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="""np"""			)
       UpperCamelCase_					: Dict            =				output.images

       UpperCamelCase_					: List[str]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: Dict            =				pipe(
           [prompt]      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  return_dict=__lowerCAmelCase      ,  num_inference_steps=2			)[0]

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

       assert image.shape == (1, 24, 24, 3)

       UpperCamelCase_					: List[str]            =				np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]			)

       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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: int            =				"""cpu"""

       UpperCamelCase_					: List[Any]            =				self.dummy_vqvae
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_text_encoder
       UpperCamelCase_					: int            =				self.dummy_tokenizer
       UpperCamelCase_					: Tuple            =				self.dummy_transformer
       UpperCamelCase_					: Tuple            =				VQDiffusionScheduler(self.num_embed			)
       UpperCamelCase_					: List[Any]            =				LearnedClassifierFreeSamplingEmbeddings(
           learnable=__lowerCAmelCase      ,  hidden_size=self.text_embedder_hidden_size      ,  length=tokenizer.model_max_length			)

       UpperCamelCase_					: Optional[Any]            =				VQDiffusionPipeline(
           vqvae=__lowerCAmelCase      ,  text_encoder=__lowerCAmelCase      ,  tokenizer=__lowerCAmelCase      ,  transformer=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  learned_classifier_free_sampling_embeddings=__lowerCAmelCase      ,  )
       UpperCamelCase_					: List[Any]            =				pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				"""teddy bear playing in the pool"""

       UpperCamelCase_					: List[str]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pipe([prompt]      ,  generator=__lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="""np"""			)
       UpperCamelCase_					: List[str]            =				output.images

       UpperCamelCase_					: Optional[int]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: List[str]            =				pipe(
           [prompt]      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  return_dict=__lowerCAmelCase      ,  num_inference_steps=2			)[0]

       UpperCamelCase_					: Dict            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: Optional[int]            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 24, 24, 3)

       UpperCamelCase_					: List[Any]            =				np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 2.0
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2






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






 """simple docstring"""





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





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""			)

       UpperCamelCase_					: Optional[int]            =				VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""			)
       UpperCamelCase_					: str            =				pipeline.to(__lowerCAmelCase			)
       pipeline.set_progress_bar_config(disable=__lowerCAmelCase			)

       # requires GPU generator for gumbel softmax
       # don't use GPU generator in tests though
       UpperCamelCase_					: List[Any]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: str            =				pipeline(
           """teddy bear playing in the pool"""      ,  num_images_per_prompt=1      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  )

       UpperCamelCase_					: Union[str, Any]            =				output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image			).max() < 2.0






