'''simple docstring'''

import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


_A			:      List[str]					      =				logging.get_logger(__name__)

enable_full_determinism()







class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					UNetaDModel
 _SCREAMING_SNAKE_CASE     :       Any      	=					"""sample"""
 @property
 def a					(      self   :	Tuple							)  ->    Optional[int]:
  __lowerCAmelCase											=						4
  __lowerCAmelCase											=						3
  __lowerCAmelCase											=						(32, 32)

  __lowerCAmelCase											=						floats_tensor((batch_size, num_channels) + sizes							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.tensor([10]							).to(SCREAMING_SNAKE_CASE__							)

  return {"sample": noise, "timestep": time_step}
 @property
 def a					(      self   :	int							)  ->    List[Any]:
  return (3, 32, 32)
 @property
 def a					(      self   :	Any							)  ->    Optional[int]:
  return (3, 32, 32)





 def a					(      self   :	Union[str, Any]							)  ->    Any:
  __lowerCAmelCase											=						{
      """block_out_channels""": (32, 64),
      """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
      """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
      """attention_head_dim""": 3,
      """out_channels""": 3,
      """in_channels""": 3,
      """layers_per_block""": 2,
      """sample_size""": 32,
  }
  __lowerCAmelCase											=						self.dummy_input
  return init_dict, inputs_dict







class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Dict      	=					UNetaDModel
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					"""sample"""
 @property
 def a					(      self   :	str							)  ->    Optional[Any]:
  __lowerCAmelCase											=						4
  __lowerCAmelCase											=						4
  __lowerCAmelCase											=						(32, 32)

  __lowerCAmelCase											=						floats_tensor((batch_size, num_channels) + sizes							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.tensor([10]							).to(SCREAMING_SNAKE_CASE__							)

  return {"sample": noise, "timestep": time_step}
 @property
 def a					(      self   :	int							)  ->    Dict:
  return (4, 32, 32)
 @property
 def a					(      self   :	Optional[Any]							)  ->    str:
  return (4, 32, 32)
 def a					(      self   :	Optional[int]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						{
      """sample_size""": 32,
      """in_channels""": 4,
      """out_channels""": 4,
      """layers_per_block""": 2,
      """block_out_channels""": (32, 64),
      """attention_head_dim""": 32,
      """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
      """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
  }
  __lowerCAmelCase											=						self.dummy_input
  return init_dict, inputs_dict
 def a					(      self   :	Dict							)  ->    Any:
  __lowerCAmelCase							,       __lowerCAmelCase											=						UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,       output_loading_info=SCREAMING_SNAKE_CASE__							)

  self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
  self.assertEqual(len(loading_info["""missing_keys"""]							)      ,       0							)

  model.to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						model(**self.dummy_input							).sample

  assert image is not None, "Make sure output is not None"
 @unittest.skipIf(torch_device != """cuda"""      ,       """This test is supposed to run on GPU"""							)
 def a					(      self   :	List[Any]							)  ->    List[str]:
  __lowerCAmelCase							,       __lowerCAmelCase											=						UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,       output_loading_info=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						model(**self.dummy_input							).sample

  assert image is not None, "Make sure output is not None"
 @unittest.skipIf(torch_device != """cuda"""      ,       """This test is supposed to run on GPU"""							)
 def a					(      self   :	Optional[int]							)  ->    str:
  # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
  __lowerCAmelCase							,       __lowerCAmelCase											=						UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,       output_loading_info=SCREAMING_SNAKE_CASE__							)
  model_accelerate.to(SCREAMING_SNAKE_CASE__							)
  model_accelerate.eval()

  __lowerCAmelCase											=						torch.randn(
      1      ,       model_accelerate.config.in_channels      ,       model_accelerate.config.sample_size      ,       model_accelerate.config.sample_size      ,       generator=torch.manual_seed(0							)      ,       )
  __lowerCAmelCase											=						noise.to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.tensor([10] * noise.shape[0]							).to(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						model_accelerate(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)["""sample"""]

  # two models don't need to stay in the device at the same time
  del model_accelerate
  torch.cuda.empty_cache()
  gc.collect()

  __lowerCAmelCase							,       __lowerCAmelCase											=						UNetaDModel.from_pretrained(
      """fusing/unet-ldm-dummy-update"""      ,       output_loading_info=SCREAMING_SNAKE_CASE__      ,       low_cpu_mem_usage=SCREAMING_SNAKE_CASE__							)
  model_normal_load.to(SCREAMING_SNAKE_CASE__							)
  model_normal_load.eval()
  __lowerCAmelCase											=						model_normal_load(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)["""sample"""]

  assert torch_all_close(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       rtol=1e-3							)





 def a					(      self   :	Any							)  ->    str:
  __lowerCAmelCase											=						UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""							)
  model.eval()
  model.to(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.randn(
      1      ,       model.config.in_channels      ,       model.config.sample_size      ,       model.config.sample_size      ,       generator=torch.manual_seed(0							)      ,       )
  __lowerCAmelCase											=						noise.to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.tensor([10] * noise.shape[0]							).to(SCREAMING_SNAKE_CASE__							)

  with torch.no_grad():
   __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							).sample

  __lowerCAmelCase											=						output[0, -1, -3:, -3:].flatten().cpu()
  # fmt: off
  __lowerCAmelCase											=						torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]							)
  # fmt: on

  self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       rtol=1e-3							)							)







class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					UNetaDModel
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					"""sample"""
 @property
 def a					(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=(32, 32)							)  ->    List[Any]:
  __lowerCAmelCase											=						4
  __lowerCAmelCase											=						3

  __lowerCAmelCase											=						floats_tensor((batch_size, num_channels) + sizes							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.tensor(batch_size * [10]							).to(dtype=torch.intaa      ,       device=SCREAMING_SNAKE_CASE__							)

  return {"sample": noise, "timestep": time_step}
 @property
 def a					(      self   :	str							)  ->    Union[str, Any]:
  return (3, 32, 32)
 @property
 def a					(      self   :	Any							)  ->    Any:
  return (3, 32, 32)
 def a					(      self   :	Optional[Any]							)  ->    Optional[int]:
  __lowerCAmelCase											=						{
      """block_out_channels""": [32, 64, 64, 64],
      """in_channels""": 3,
      """layers_per_block""": 1,
      """out_channels""": 3,
      """time_embedding_type""": """fourier""",
      """norm_eps""": 1e-6,
      """mid_block_scale_factor""": math.sqrt(2.0							),
      """norm_num_groups""": None,
      """down_block_types""": [
          """SkipDownBlock2D""",
          """AttnSkipDownBlock2D""",
          """SkipDownBlock2D""",
          """SkipDownBlock2D""",
      ],
      """up_block_types""": [
          """SkipUpBlock2D""",
          """SkipUpBlock2D""",
          """AttnSkipUpBlock2D""",
          """SkipUpBlock2D""",
      ],
  }
  __lowerCAmelCase											=						self.dummy_input
  return init_dict, inputs_dict
 @slow
 def a					(      self   :	str							)  ->    Optional[Any]:
  __lowerCAmelCase							,       __lowerCAmelCase											=						UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""      ,       output_loading_info=SCREAMING_SNAKE_CASE__							)
  self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)
  self.assertEqual(len(loading_info["""missing_keys"""]							)      ,       0							)

  model.to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.dummy_input
  __lowerCAmelCase											=						floats_tensor((4, 3) + (2_56, 2_56)							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						noise
  __lowerCAmelCase											=						model(**SCREAMING_SNAKE_CASE__							)

  assert image is not None, "Make sure output is not None"
 @slow
 def a					(      self   :	List[str]							)  ->    int:
  __lowerCAmelCase											=						UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""							)
  model.to(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						4
  __lowerCAmelCase											=						3
  __lowerCAmelCase											=						(2_56, 2_56)

  __lowerCAmelCase											=						torch.ones((batch_size, num_channels) + sizes							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.tensor(batch_size * [1e-4]							).to(SCREAMING_SNAKE_CASE__							)

  with torch.no_grad():
   __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							).sample

  __lowerCAmelCase											=						output[0, -3:, -3:, -1].flatten().cpu()
  # fmt: off
  __lowerCAmelCase											=						torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]							)
  # fmt: on

  self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       rtol=1e-2							)							)
 def a					(      self   :	Tuple							)  ->    Any:
  __lowerCAmelCase											=						UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""							)
  model.to(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						4
  __lowerCAmelCase											=						3
  __lowerCAmelCase											=						(32, 32)

  __lowerCAmelCase											=						torch.ones((batch_size, num_channels) + sizes							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.tensor(batch_size * [1e-4]							).to(SCREAMING_SNAKE_CASE__							)

  with torch.no_grad():
   __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							).sample

  __lowerCAmelCase											=						output[0, -3:, -3:, -1].flatten().cpu()
  # fmt: off
  __lowerCAmelCase											=						torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]							)
  # fmt: on

  self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       rtol=1e-2							)							)





 def a					(      self   :	str							)  ->    int:
  # not required for this model
  pass







'''simple docstring'''

from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_A			:      List[Any]					      =				299792458

# Symbols
_A      ,     _A      ,     _A      ,     _A			:      Union[str, Any]					      =				symbols('''ct x y z''')







def      UpperCamelCase_						(					snake_case_       :       float      )					->     float:






 '''simple docstring'''

 if velocity > c:
  raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""      )
 elif velocity < 1:
  # Usually the speed should be much higher than 1 (c order of magnitude)
  raise ValueError("""Speed must be greater than or equal to 1!"""      )

 return velocity / c







def      UpperCamelCase_						(					snake_case_       :       float      )					->     float:






 '''simple docstring'''

 return 1 / sqrt(1 - beta(snake_case_      ) ** 2      )







def      UpperCamelCase_						(					snake_case_       :       float      )					->     np.ndarray:






 '''simple docstring'''

 return np.array(
     [
         [gamma(snake_case_      ), -gamma(snake_case_      ) * beta(snake_case_      ), 0, 0],
         [-gamma(snake_case_      ) * beta(snake_case_      ), gamma(snake_case_      ), 0, 0],
         [0, 0, 1, 0],
         [0, 0, 0, 1],
     ]      )







def      UpperCamelCase_						(					snake_case_       :       float						,		snake_case_       :       np.ndarray | None = None      )					->     np.ndarray:






 '''simple docstring'''

 if event is None:
  __lowerCAmelCase											=						np.array([ct, x, y, z]      )  # Symbolic four vector
 else:
  event[0] *= c  # x0 is ct (speed of light * time)

 return transformation_matrix(snake_case_      ) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 _A			:      str					      =				transform(29979245)
 print('''Example of four vector: ''')
 print(f'ct\' = {four_vector[0]}')
 print(f'x\' = {four_vector[1]}')
 print(f'y\' = {four_vector[2]}')
 print(f'z\' = {four_vector[3]}')

 # Substitute symbols with numerical values
 _A			:      int					      =				{ct: c, x: 1, y: 1, z: 1}
 _A			:      Any					      =				[four_vector[i].subs(sub_dict) for i in range(4)]

 print(f'\n{numerical_vector}')






