






from __future__ import annotations







class 	_UpperCAmelCase  :




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				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:
              lowerCAmelCase__   :Union[str, Any]      							=				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
              lowerCAmelCase__   :Optional[Any]      							=				rows
          else:
              lowerCAmelCase__   :Union[str, Any]      							=				[]
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return [[row[i] for row in self.rows] for i in range(len(self.rows[0]		)		)]
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return len(self.rows		)
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return len(self.rows[0]		)
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return (self.num_rows, self.num_columns)
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.order[0] == self.order[1]
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				[
              [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 			snake_case_      (   self		):



          '''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 			snake_case_      (   self		):



          '''simple docstring'''
          return bool(self.determinant()		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          if (row + column) % 2 == 0:
              return self.get_minor(_lowerCAmelCase    ,	_lowerCAmelCase		)
          return -1 * self.get_minor(_lowerCAmelCase    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return Matrix(
              [
                  [self.get_minor(_lowerCAmelCase    ,	_lowerCAmelCase		) for column in range(self.num_columns		)]
                  for row in range(self.num_rows		)
              ]		)
      def 			snake_case_      (   self		):



          '''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 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				[
              [self.cofactors().rows[column][row] for column in range(self.num_columns		)]
              for row in range(self.num_rows		)
          ]
          return Matrix(_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				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		):



          '''simple docstring'''
          return str(self.rows		)
      def __str__(   self		):



          '''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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None		):



          '''simple docstring'''
          lowerCAmelCase__   :List[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:
              lowerCAmelCase__   :Any      							=				self.rows[0:position] + [row] + self.rows[position:]
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None		):



          '''simple docstring'''
          lowerCAmelCase__   :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:
              lowerCAmelCase__   :str      							=				[self.rows[i] + [column[i]] for i in range(self.num_rows		)]
          else:
              lowerCAmelCase__   :List[Any]      							=				[
                  self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                  for i in range(self.num_rows		)
              ]
      def __eq__(   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          if not isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		):
              return NotImplemented
          return self.rows == other.rows
      def __ne__(   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          return not self == other
      def __neg__(   self		):



          '''simple docstring'''
          return self * -1
      def __add__(   self    ,	_lowerCAmelCase		):



          '''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		):



          '''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		):



          '''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		):



          '''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"		)
          lowerCAmelCase__   :int      							=				self
          for _ in range(other - 1		):
              result *= self
          return result



      @classmethod
      def 			snake_case_      (   cls    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          return sum(row[i] * column[i] for i in range(len(_lowerCAmelCase		)		)		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()











import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class 	_UpperCAmelCase  :




      """simple docstring"""



      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)
          lowerCAmelCase__   :List[str]      							=				TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

          torch.manual_seed(0		)
          lowerCAmelCase__   :List[Any]      							=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"		)

          torch.manual_seed(0		)
          lowerCAmelCase__   :Tuple      							=				UNetaDConditionModel(
              sample_size=32    ,	layers_per_block=1    ,	block_out_channels=[32, 64]    ,	down_block_types=[
                  "ResnetDownsampleBlock2D",
                  "SimpleCrossAttnDownBlock2D",
              ]    ,	mid_block_type="UNetMidBlock2DSimpleCrossAttn"    ,	up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]    ,	in_channels=3    ,	out_channels=6    ,	cross_attention_dim=32    ,	encoder_hid_dim=32    ,	attention_head_dim=8    ,	addition_embed_type="text"    ,	addition_embed_type_num_heads=2    ,	cross_attention_norm="group_norm"    ,	resnet_time_scale_shift="scale_shift"    ,	act_fn="gelu"    ,	)
          unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

          torch.manual_seed(0		)
          lowerCAmelCase__   :Dict      							=				DDPMScheduler(
              num_train_timesteps=1_000    ,	beta_schedule="squaredcos_cap_v2"    ,	beta_start=0.0001    ,	beta_end=0.02    ,	thresholding=_lowerCAmelCase    ,	dynamic_thresholding_ratio=0.95    ,	sample_max_value=1.0    ,	prediction_type="epsilon"    ,	variance_type="learned_range"    ,	)

          torch.manual_seed(0		)
          lowerCAmelCase__   :List[str]      							=				IFWatermarker()

          return {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "watermarker": watermarker,
              "safety_checker": None,
              "feature_extractor": None,
          }
      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)
          lowerCAmelCase__   :int      							=				TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"		)

          torch.manual_seed(0		)
          lowerCAmelCase__   :Dict      							=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"		)

          torch.manual_seed(0		)
          lowerCAmelCase__   :Union[str, Any]      							=				UNetaDConditionModel(
              sample_size=32    ,	layers_per_block=[1, 2]    ,	block_out_channels=[32, 64]    ,	down_block_types=[
                  "ResnetDownsampleBlock2D",
                  "SimpleCrossAttnDownBlock2D",
              ]    ,	mid_block_type="UNetMidBlock2DSimpleCrossAttn"    ,	up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]    ,	in_channels=6    ,	out_channels=6    ,	cross_attention_dim=32    ,	encoder_hid_dim=32    ,	attention_head_dim=8    ,	addition_embed_type="text"    ,	addition_embed_type_num_heads=2    ,	cross_attention_norm="group_norm"    ,	resnet_time_scale_shift="scale_shift"    ,	act_fn="gelu"    ,	class_embed_type="timestep"    ,	mid_block_scale_factor=1.414    ,	time_embedding_act_fn="gelu"    ,	time_embedding_dim=32    ,	)
          unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

          torch.manual_seed(0		)
          lowerCAmelCase__   :Tuple      							=				DDPMScheduler(
              num_train_timesteps=1_000    ,	beta_schedule="squaredcos_cap_v2"    ,	beta_start=0.0001    ,	beta_end=0.02    ,	thresholding=_lowerCAmelCase    ,	dynamic_thresholding_ratio=0.95    ,	sample_max_value=1.0    ,	prediction_type="epsilon"    ,	variance_type="learned_range"    ,	)

          torch.manual_seed(0		)
          lowerCAmelCase__   :str      							=				DDPMScheduler(
              num_train_timesteps=1_000    ,	beta_schedule="squaredcos_cap_v2"    ,	beta_start=0.0001    ,	beta_end=0.02    ,	)

          torch.manual_seed(0		)
          lowerCAmelCase__   :List[str]      							=				IFWatermarker()

          return {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "image_noising_scheduler": image_noising_scheduler,
              "watermarker": watermarker,
              "safety_checker": None,
              "feature_extractor": None,
          }
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				self.get_dummy_components()
          lowerCAmelCase__   :Any      							=				self.pipeline_class(**_lowerCAmelCase		)
          pipe.to(_lowerCAmelCase		)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :int      							=				self.get_dummy_inputs(_lowerCAmelCase		)

          lowerCAmelCase__   :Optional[int]      							=				inputs["prompt"]
          lowerCAmelCase__   :Tuple      							=				inputs["generator"]
          lowerCAmelCase__   :List[Any]      							=				inputs["num_inference_steps"]
          lowerCAmelCase__   :Tuple      							=				inputs["output_type"]

          if "image" in inputs:
              lowerCAmelCase__   :Dict      							=				inputs["image"]
          else:
              lowerCAmelCase__   :Union[str, Any]      							=				None

          if "mask_image" in inputs:
              lowerCAmelCase__   :int      							=				inputs["mask_image"]
          else:
              lowerCAmelCase__   :str      							=				None

          if "original_image" in inputs:
              lowerCAmelCase__   :List[Any]      							=				inputs["original_image"]
          else:
              lowerCAmelCase__   :Dict      							=				None

          lowerCAmelCase__							,lowerCAmelCase__   :int      							=				pipe.encode_prompt(_lowerCAmelCase		)

          # inputs with prompt converted to embeddings
          lowerCAmelCase__   :Optional[Any]      							=				{
              "prompt_embeds": prompt_embeds,
              "negative_prompt_embeds": negative_prompt_embeds,
              "generator": generator,
              "num_inference_steps": num_inference_steps,
              "output_type": output_type,
          }

          if image is not None:
              lowerCAmelCase__   :List[Any]      							=				image

          if mask_image is not None:
              lowerCAmelCase__   :Any      							=				mask_image

          if original_image is not None:
              lowerCAmelCase__   :Tuple      							=				original_image

          # set all optional components to None
          for optional_component in pipe._optional_components:
              setattr(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :Optional[int]      							=				pipe(**_lowerCAmelCase		)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_lowerCAmelCase		)
              lowerCAmelCase__   :str      							=				self.pipeline_class.from_pretrained(_lowerCAmelCase		)
              pipe_loaded.to(_lowerCAmelCase		)
              pipe_loaded.set_progress_bar_config(disable=_lowerCAmelCase		)

          pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

          for optional_component in pipe._optional_components:
              self.assertTrue(
                  getattr(_lowerCAmelCase    ,	_lowerCAmelCase		) is None    ,	f'''`{optional_component}` did not stay set to None after loading.'''    ,	)

          lowerCAmelCase__   :Any      							=				self.get_dummy_inputs(_lowerCAmelCase		)

          lowerCAmelCase__   :Tuple      							=				inputs["generator"]
          lowerCAmelCase__   :Union[str, Any]      							=				inputs["num_inference_steps"]
          lowerCAmelCase__   :str      							=				inputs["output_type"]

          # inputs with prompt converted to embeddings
          lowerCAmelCase__   :Union[str, Any]      							=				{
              "prompt_embeds": prompt_embeds,
              "negative_prompt_embeds": negative_prompt_embeds,
              "generator": generator,
              "num_inference_steps": num_inference_steps,
              "output_type": output_type,
          }

          if image is not None:
              lowerCAmelCase__   :Tuple      							=				image

          if mask_image is not None:
              lowerCAmelCase__   :str      							=				mask_image

          if original_image is not None:
              lowerCAmelCase__   :Union[str, Any]      							=				original_image

          lowerCAmelCase__   :List[Any]      							=				pipe_loaded(**_lowerCAmelCase		)[0]

          lowerCAmelCase__   :Dict      							=				np.abs(to_np(_lowerCAmelCase		) - to_np(_lowerCAmelCase		)		).max()
          self.assertLess(_lowerCAmelCase    ,	1e-4		)



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.get_dummy_components()
          lowerCAmelCase__   :Optional[int]      							=				self.pipeline_class(**_lowerCAmelCase		)
          pipe.to(_lowerCAmelCase		)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :Optional[int]      							=				self.get_dummy_inputs(_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				pipe(**_lowerCAmelCase		)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_lowerCAmelCase		)
              lowerCAmelCase__   :Dict      							=				self.pipeline_class.from_pretrained(_lowerCAmelCase		)
              pipe_loaded.to(_lowerCAmelCase		)
              pipe_loaded.set_progress_bar_config(disable=_lowerCAmelCase		)

          pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

          lowerCAmelCase__   :Optional[int]      							=				self.get_dummy_inputs(_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				pipe_loaded(**_lowerCAmelCase		)[0]

          lowerCAmelCase__   :str      							=				np.abs(to_np(_lowerCAmelCase		) - to_np(_lowerCAmelCase		)		).max()
          self.assertLess(_lowerCAmelCase    ,	1e-4		)



