




'''simple docstring'''

import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()





class __magic_name__(						unittest.TestCase      ):
						UpperCAmelCase_		:					Any    =  """hf-internal-testing/tiny-stable-diffusion-pipe"""
						UpperCAmelCase_		:					Optional[int]    =  1
						UpperCAmelCase_		:					Dict    =  77
						UpperCAmelCase_		:					Dict    =  32
						UpperCAmelCase_		:					Optional[int]    =  4
						UpperCAmelCase_		:					str    =  64
						UpperCAmelCase_		:					Union[str, Any]    =  torch.manual_seed(0      )






						def       __lowerCAmelCase(   self						:						List[Any]					,   __UpperCamelCase						:						Dict=0.99_99       ):



									'''simple docstring'''






									snake_case__          =						UNetaDConditionModel.from_pretrained(self.model_id					,   subfolder="""unet"""       )
									snake_case__          =						unet.to(lowercase__       )
									snake_case__          =						EMAModel(unet.parameters()					,   decay=lowercase__					,   model_cls=lowercase__					,   model_config=unet.config       )
									return unet, ema_unet






						def       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






									snake_case__          =						torch.randn(
									    self.batch_size					,   self.num_in_channels					,   self.latent_height					,   self.latent_width					,   generator=self.generator       ).to(lowercase__       )
									snake_case__          =						torch.randint(0					,   1_0_0_0					,   size=(self.batch_size,)					,   generator=self.generator       ).to(lowercase__       )
									snake_case__          =						torch.randn(
									    self.batch_size					,   self.prompt_length					,   self.text_encoder_hidden_dim					,   generator=self.generator       ).to(lowercase__       )
									return noisy_latents, timesteps, encoder_hidden_states






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						{}
									for k, param in unet.state_dict().items():
												snake_case__          =						torch.randn_like(lowercase__       ) + (param * torch.randn_like(lowercase__       ))
												updated_state_dict.update({k: updated_param}       )
									unet.load_state_dict(lowercase__       )
									return unet






						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__          =						self.get_models()
									# Take the first (hypothetical) EMA step.
									ema_unet.step(unet.parameters()       )
									assert ema_unet.optimization_step == 1

									# Take two more.
									for _ in range(2       ):
												ema_unet.step(unet.parameters()       )
									assert ema_unet.optimization_step == 3






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__          =						self.get_models()
									# Since the `unet` is not being updated (i.e., backprop'd)
									# there won't be any difference between the `params` of `unet`
									# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
									ema_unet.step(unet.parameters()       )
									snake_case__          =						list(unet.parameters()       )
									for s_param, param in zip(ema_unet.shadow_params					,   lowercase__       ):
												assert torch.allclose(lowercase__					,   lowercase__       )

									# The above holds true even if we call `ema.step()` multiple times since
									# `unet` params are still not being updated.
									for _ in range(4       ):
												ema_unet.step(unet.parameters()       )
									for s_param, param in zip(ema_unet.shadow_params					,   lowercase__       ):
												assert torch.allclose(lowercase__					,   lowercase__       )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__          =						self.get_models()
									# Here we simulate the parameter updates for `unet`. Since there might
									# be some parameters which are initialized to zero we take extra care to
									# initialize their values to something non-zero before the multiplication.
									snake_case__          =						self.simulate_backprop(lowercase__       )

									# Take the EMA step.
									ema_unet.step(unet_pseudo_updated_step_one.parameters()       )

									# Now the EMA'd parameters won't be equal to the original model parameters.
									snake_case__          =						list(unet_pseudo_updated_step_one.parameters()       )
									for s_param, param in zip(ema_unet.shadow_params					,   lowercase__       ):
												assert ~torch.allclose(lowercase__					,   lowercase__       )

									# Ensure this is the case when we take multiple EMA steps.
									for _ in range(4       ):
												ema_unet.step(unet.parameters()       )
									for s_param, param in zip(ema_unet.shadow_params					,   lowercase__       ):
												assert ~torch.allclose(lowercase__					,   lowercase__       )






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__          =						self.get_models()

									# First backprop + EMA
									snake_case__          =						self.simulate_backprop(lowercase__       )
									ema_unet.step(unet_step_one.parameters()       )
									snake_case__          =						ema_unet.shadow_params

									# Second backprop + EMA
									snake_case__          =						self.simulate_backprop(lowercase__       )
									ema_unet.step(unet_step_two.parameters()       )
									snake_case__          =						ema_unet.shadow_params

									for step_one, step_two in zip(lowercase__					,   lowercase__       ):
												assert ~torch.allclose(lowercase__					,   lowercase__       )






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__          =						self.get_models(decay=0.0       )
									snake_case__          =						self.simulate_backprop(lowercase__       )
									ema_unet.step(unet_step_one.parameters()       )
									snake_case__          =						ema_unet.shadow_params

									snake_case__          =						self.simulate_backprop(lowercase__       )
									ema_unet.step(unet_step_two.parameters()       )
									snake_case__          =						ema_unet.shadow_params

									for step_one, step_two in zip(lowercase__					,   lowercase__       ):
												assert torch.allclose(lowercase__					,   lowercase__       )







						@skip_mps
						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__    ,			snake_case__          =						self.get_models()
									snake_case__    ,			snake_case__    ,			snake_case__          =						self.get_dummy_inputs()

									with tempfile.TemporaryDirectory() as tmpdir:
												ema_unet.save_pretrained(lowercase__       )
												snake_case__          =						UNetaDConditionModel.from_pretrained(lowercase__					,   model_cls=lowercase__       )
												snake_case__          =						loaded_unet.to(unet.device       )

									# Since no EMA step has been performed the outputs should match.
									snake_case__          =						unet(lowercase__					,   lowercase__					,   lowercase__       ).sample
									snake_case__          =						loaded_unet(lowercase__					,   lowercase__					,   lowercase__       ).sample

									assert torch.allclose(lowercase__					,   lowercase__					,   atol=1E-4       )





from __future__ import annotations

from typing import Any






def 				UpperCAmelCase__  (							*_A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  return [[x * n for x in row] for row in matrix]






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if _check_not_integer(_A			) and _check_not_integer(_A			):
    a_    , a_		    =  _verify_matrix_sizes(_A      ,      _A			)

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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  int(_A			)
  return [[int(row == column			) for column in range(_A			)] for row in range(_A			)]






def 				UpperCAmelCase__  (							_A      ,      _A = True			):






  """simple docstring"""


  if _check_not_integer(_A			):
    if return_map:
      return map(_A      ,      zip(*_A			)			)
    else:
      return list(map(_A      ,      zip(*_A			)			)			)
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  matrix[:row] + matrix[row + 1 :]
  return [row[:column] + row[column + 1 :] for row in minor]






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  if len(_A			) == 1:
    return matrix[0][0]

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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  determinant(_A			)
  if det == 0:
    return None

  a_		    =  [
      [determinant(minor(_A      ,      _A      ,      _A			)			) for j in range(len(_A			)			)]
      for i in range(len(_A			)			)
  ]

  a_		    =  [
      [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]			)]
      for row in range(len(_A			)			)
  ]
  a_		    =  list(transpose(_A			)			)
  return scalar_multiply(_A      ,      1 / det			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  return not isinstance(_A      ,      _A			) and not isinstance(matrix[0]      ,      _A			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  return len(_A			), len(matrix[0]			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  _shape(_A			) + _shape(_A			)
  if shape[0] != shape[3] or shape[1] != shape[2]:
    a_		    =  (
        '''operands could not be broadcast together with shape '''
        f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
    )
    raise ValueError(_A			)
  return (shape[0], shape[2]), (shape[1], shape[3])






def 				UpperCAmelCase__  (							):






  """simple docstring"""


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


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()
