
'''simple docstring'''
import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
				import jax
				import jax.numpy as jnp


@slow
@require_flax
class      _lowerCAmelCase       (       unittest.TestCase ):







							'''simple docstring'''

							def 			lowercase				(self    ,    UpperCAmelCase    ,    UpperCAmelCase    )	->  Optional[int]:
														return f"""gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE__    ) for s in shape]    )}.npy"""

							def 			lowercase				(self    )	->  str:
														super().tearDown()
														gc.collect()

							def 			lowercase				(self    ,    UpperCAmelCase=0    ,    UpperCAmelCase=(4, 4, 64, 64)    ,    UpperCAmelCase=False    )	->  Optional[Any]:
														_snake_case  =    jnp.bfloataa if fpaa else jnp.floataa
														_snake_case  =    jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE__    ,    SCREAMING_SNAKE_CASE__    )    )    ,    dtype=SCREAMING_SNAKE_CASE__    )
														return image

							def 			lowercase				(self    ,    UpperCAmelCase=False    ,    UpperCAmelCase="CompVis/stable-diffusion-v1-4"    )	->  Optional[int]:
														_snake_case  =    jnp.bfloataa if fpaa else jnp.floataa
														_snake_case  =    """bf16""" if fpaa else None

														_snake_case,				_snake_case  =    FlaxUNetaDConditionModel.from_pretrained(
														    SCREAMING_SNAKE_CASE__    ,    subfolder="""unet"""    ,    dtype=SCREAMING_SNAKE_CASE__    ,    revision=SCREAMING_SNAKE_CASE__    )
														return model, params

							def 			lowercase				(self    ,    UpperCAmelCase=0    ,    UpperCAmelCase=(4, 77, 768)    ,    UpperCAmelCase=False    )	->  List[str]:
														_snake_case  =    jnp.bfloataa if fpaa else jnp.floataa
														_snake_case  =    jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE__    ,    SCREAMING_SNAKE_CASE__    )    )    ,    dtype=SCREAMING_SNAKE_CASE__    )
														return hidden_states

							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
							        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
							        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
							        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
							        # fmt: on
							    ]    )
							def 			lowercase				(self    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    )	->  List[str]:
														_snake_case,				_snake_case  =    self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""    ,    fpaa=SCREAMING_SNAKE_CASE__    )
														_snake_case  =    self.get_latents(SCREAMING_SNAKE_CASE__    ,    fpaa=SCREAMING_SNAKE_CASE__    )
														_snake_case  =    self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE__    ,    fpaa=SCREAMING_SNAKE_CASE__    )

														_snake_case  =    model.apply(
														    {"""params""": params}    ,    SCREAMING_SNAKE_CASE__    ,    jnp.array(SCREAMING_SNAKE_CASE__    ,    dtype=jnp.intaa    )    ,    encoder_hidden_states=SCREAMING_SNAKE_CASE__    ,    ).sample

														assert sample.shape == latents.shape

														_snake_case  =    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())    )    ,    dtype=jnp.floataa    )
														_snake_case  =    jnp.array(SCREAMING_SNAKE_CASE__    ,    dtype=jnp.floataa    )

														# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
														assert jnp.allclose(SCREAMING_SNAKE_CASE__    ,    SCREAMING_SNAKE_CASE__    ,    atol=1e-2    )





							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
							        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
							        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
							        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
							        # fmt: on
							    ]    )
							def 			lowercase				(self    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    )	->  Dict:
														_snake_case,				_snake_case  =    self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""    ,    fpaa=SCREAMING_SNAKE_CASE__    )
														_snake_case  =    self.get_latents(SCREAMING_SNAKE_CASE__    ,    shape=(4, 4, 96, 96)    ,    fpaa=SCREAMING_SNAKE_CASE__    )
														_snake_case  =    self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE__    ,    shape=(4, 77, 1024)    ,    fpaa=SCREAMING_SNAKE_CASE__    )

														_snake_case  =    model.apply(
														    {"""params""": params}    ,    SCREAMING_SNAKE_CASE__    ,    jnp.array(SCREAMING_SNAKE_CASE__    ,    dtype=jnp.intaa    )    ,    encoder_hidden_states=SCREAMING_SNAKE_CASE__    ,    ).sample

														assert sample.shape == latents.shape

														_snake_case  =    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())    )    ,    dtype=jnp.floataa    )
														_snake_case  =    jnp.array(SCREAMING_SNAKE_CASE__    ,    dtype=jnp.floataa    )

														# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
														assert jnp.allclose(SCREAMING_SNAKE_CASE__    ,    SCREAMING_SNAKE_CASE__    ,    atol=1e-2    )

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
 # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
 # setup instructions, if using on-demand hardware

 # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
 # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
 # Throw an error if user passes both BYO and on-demand cluster args
 # Otherwise, use default values
 _snake_case      = argparse.ArgumentParser()
 parser.add_argument('''--user''', type=str, default='''ubuntu''')
 parser.add_argument('''--host''', type=str, default='''localhost''')
 parser.add_argument('''--key_path''', type=str, default=None)
 parser.add_argument('''--instance''', type=str, default='''V100:1''')
 parser.add_argument('''--provider''', type=str, default='''cheapest''')
 parser.add_argument('''--use_spot''', type=bool, default=False)
 parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
 _snake_case  ,      _snake_case      = parser.parse_known_args()
 if args.host != "localhost":
  if args.instance != "V100:1" or args.provider != "cheapest":
   raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
  _snake_case      = rh.cluster(
      name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
  )
 else:
  _snake_case      = rh.cluster(
      name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 _snake_case      = args.example.rsplit('''/''', 1)[0]

 # Set up remote environment
 cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
 # Note transformers is copied into the home directory on the remote machine, so we can install from there
 cluster.run([F"pip install -r transformers/examples/{example_dir}/requirements.txt"])
 cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

 # Run example. You can bypass the CLI wrapper and paste your own code here.
 cluster.run([F"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])

 # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
 # from my_script... import train
 # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
 # launch_train_gpu = rh.function(fn=train,
 #                                system=gpu,
 #                                reqs=reqs,
 #                                name='train_bert_glue')
 #
 # We can pass in arguments just like we would to a function:
 # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
 #                  stream_logs=True)


