code: 
  
  
  import argparse
  import os
  
  
  A_														=      {
      "pytorch": {
          "1.13": {
              "torch": "1.13.1",
              "torchvision": "0.14.1",
              "torchaudio": "0.13.1",
              "python": 3.9,
              "cuda": "cu116",
              "install": (
                  "python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1"
                  " --extra-index-url https://download.pytorch.org/whl/cu116"
              ),
              "base_image": "nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04",
          },
          "1.12": {
              "torch": "1.12.1",
              "torchvision": "0.13.1",
              "torchaudio": "0.12.1",
              "python": 3.9,
              "cuda": "cu113",
              "install": (
                  "python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1"
                  " --extra-index-url https://download.pytorch.org/whl/cu113"
              ),
              "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
          },
          "1.11": {
              "torch": "1.11.0",
              "torchvision": "0.12.0",
              "torchaudio": "0.11.0",
              "python": 3.9,
              "cuda": "cu113",
              "install": (
                  "python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0"
                  " --extra-index-url https://download.pytorch.org/whl/cu113"
              ),
              "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
          },
          "1.10": {
              "torch": "1.10.2",
              "torchvision": "0.11.3",
              "torchaudio": "0.10.2",
              "python": 3.9,
              "cuda": "cu113",
              "install": (
                  "python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2"
                  " --extra-index-url https://download.pytorch.org/whl/cu113"
              ),
              "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
          },
          # torchaudio < 0.10 has no CUDA-enabled binary distributions
          "1.9": {
              "torch": "1.9.1",
              "torchvision": "0.10.1",
              "torchaudio": "0.9.1",
              "python": 3.9,
              "cuda": "cu111",
              "install": (
                  "python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1"
                  " --extra-index-url https://download.pytorch.org/whl/cu111"
              ),
              "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
          },
      },
      "tensorflow": {
          "2.11": {
              "tensorflow": "2.11.1",
              "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.11.1",
              "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
          },
          "2.10": {
              "tensorflow": "2.10.1",
              "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.10.1",
              "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
          },
          "2.9": {
              "tensorflow": "2.9.3",
              "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.9.3",
              "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
          },
          "2.8": {
              "tensorflow": "2.8.2",
              "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.8.2",
              "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
          },
          "2.7": {
              "tensorflow": "2.7.3",
              "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.7.3",
              "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
          },
          "2.6": {
              "tensorflow": "2.6.5",
              "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.6.5",
              "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
          },
          "2.5": {
              "tensorflow": "2.5.3",
              "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.5.3",
              "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
          },
      },
  }
  
  
  if __name__ == "__main__":
  		A_														=      argparse.ArgumentParser("Choose the framework and version to install")
  		parser.add_argument(
  		    "--framework", help="The framework to install. Should be `torch` or `tensorflow`", type=str, required=True
  		)
  		parser.add_argument("--version", help="The version of the framework to install.", type=str, required=True)
  		A_														=      parser.parse_args()
  
  		A_														=      past_versions_testing[args.framework][args.version]
  
  		os.system(F'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile')
  		print(F'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile')
  
  		A_														=      ""
  		if args.framework == "pytorch":
  				A_														=      info["cuda"]
  		os.system(F'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')
  		print(F'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')
  
  
  
style_context: 
  
  
  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 						snake_case			(       unittest.TestCase	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       Union[str, Any]       = """hf-internal-testing/tiny-stable-diffusion-pipe"""
  			UpperCAmelCase       :       int       = 1
  			UpperCAmelCase       :       Optional[Any]       = 77
  			UpperCAmelCase       :       Any       = 32
  			UpperCAmelCase       :       str       = 4
  			UpperCAmelCase       :       Optional[int]       = 64
  			UpperCAmelCase       :       Tuple       = torch.manual_seed(0	)
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     str=0.9_999					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       UNetaDConditionModel.from_pretrained(self.model_id							,							subfolder='''unet'''					)
  								SCREAMING_SNAKE_CASE_	      =       unet.to(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       EMAModel(unet.parameters()							,							decay=lowerCAmelCase_							,							model_cls=lowerCAmelCase_							,							model_config=unet.config					)
  								return unet, ema_unet
  
  			def _lowercase (	self     :     str					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       torch.randn(
  								    self.batch_size							,							self.num_in_channels							,							self.latent_height							,							self.latent_width							,							generator=self.generator					).to(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       torch.randint(0							,							1_000							,							size=(self.batch_size,)							,							generator=self.generator					).to(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       torch.randn(
  								    self.batch_size							,							self.prompt_length							,							self.text_encoder_hidden_dim							,							generator=self.generator					).to(lowerCAmelCase_					)
  								return noisy_latents, timesteps, encoder_hidden_states
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Tuple					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       {}
  								for k, param in unet.state_dict().items():
  													SCREAMING_SNAKE_CASE_	      =       torch.randn_like(lowerCAmelCase_					) + (param * torch.randn_like(lowerCAmelCase_					))
  													updated_state_dict.update({k: updated_param}					)
  								unet.load_state_dict(lowerCAmelCase_					)
  								return unet
  
  			def _lowercase (	self     :     Dict					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_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 _lowercase (	self     :     Union[str, Any]					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_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()					)
  								SCREAMING_SNAKE_CASE_	      =       list(unet.parameters()					)
  								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
  													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  								# 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							,							lowerCAmelCase_					):
  													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Any					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_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.
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  
  								# 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.
  								SCREAMING_SNAKE_CASE_	      =       list(unet_pseudo_updated_step_one.parameters()					)
  								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
  													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  								# 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							,							lowerCAmelCase_					):
  													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Any					)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
  
  								# First backprop + EMA
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_one.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								# Second backprop + EMA
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_two.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								for step_one, step_two in zip(lowerCAmelCase_							,							lowerCAmelCase_					):
  													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  			def _lowercase (	self     :     str					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models(decay=0.0					)
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_one.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_two.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								for step_one, step_two in zip(lowerCAmelCase_							,							lowerCAmelCase_					):
  													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  
  
  
  			@skip_mps
  			def _lowercase (	self     :     str					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_dummy_inputs()
  
  								with tempfile.TemporaryDirectory() as tmpdir:
  													ema_unet.save_pretrained(lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       UNetaDConditionModel.from_pretrained(lowerCAmelCase_							,							model_cls=lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       loaded_unet.to(unet.device					)
  
  								# Since no EMA step has been performed the outputs should match.
  								SCREAMING_SNAKE_CASE_	      =       unet(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					).sample
  								SCREAMING_SNAKE_CASE_	      =       loaded_unet(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					).sample
  
  								assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_							,							atol=1e-4					)
  
  
  