

"""simple docstring"""





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu




class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 def  _lowercase			(      self							:      Optional[Any]     ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 @property
 def  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						1
    __lowercase								=						3
    __lowercase								=						(3_2, 3_2)

    __lowercase								=						floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0     )     ).to(UpperCAmelCase__     )
    return image


 @property
 def  _lowercase			(      self							:      Optional[int]     ):
    torch.manual_seed(0     )
    __lowercase								=						UNetaDConditionModel(
        block_out_channels=(3_2, 6_4), layers_per_block=2, sample_size=3_2, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=3_2, )
    return model


 @property
 def  _lowercase			(      self							:      Optional[Any]     ):
    torch.manual_seed(0     )
    __lowercase								=						AutoencoderKL(
        block_out_channels=[3_2, 6_4], in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], latent_channels=4, )
    return model


 @property
 def  _lowercase			(      self							:      Dict     ):
    torch.manual_seed(0     )
    __lowercase								=						CLIPTextConfig(
        bos_token_id=0, eos_token_id=2, hidden_size=3_2, intermediate_size=3_7, layer_norm_eps=1E-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_0_0_0, )
    return CLIPTextModel(UpperCAmelCase__     )


 @property
 def  _lowercase			(      self							:      Any     ):
    def extract(*UpperCAmelCase__							:      str, **UpperCAmelCase__							:      List[Any]     ):




       class  _lowerCAmelCase  :







        """simple docstring"""







        def __init__(      self							:      Any     ):
           __lowercase								=						torch.ones([0]     )



        def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      Optional[int]     ):
           self.pixel_values.to(UpperCAmelCase__     )
           return self







       return Out()

    return extract


 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						"cpu"  # ensure determinism for the device-dependent torch.Generator
    __lowercase								=						self.dummy_cond_unet
    __lowercase								=						DDIMScheduler(
        beta_start=0.00_085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=UpperCAmelCase__, set_alpha_to_one=UpperCAmelCase__, )

    __lowercase								=						self.dummy_vae
    __lowercase								=						self.dummy_text_encoder
    __lowercase								=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

    # make sure here that pndm scheduler skips prk
    __lowercase								=						StableDiffusionPipeline(
        unet=UpperCAmelCase__, scheduler=UpperCAmelCase__, vae=UpperCAmelCase__, text_encoder=UpperCAmelCase__, tokenizer=UpperCAmelCase__, safety_checker=UpperCAmelCase__, feature_extractor=self.dummy_extractor, )
    __lowercase								=						sd_pipe.to(UpperCAmelCase__     )
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

    __lowercase								=						"A painting of a squirrel eating a burger"

    __lowercase								=						torch.Generator(device=UpperCAmelCase__     ).manual_seed(0     )
    __lowercase								=						sd_pipe([prompt], generator=UpperCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type="np"     )
    __lowercase								=						output.images

    __lowercase								=						torch.Generator(device=UpperCAmelCase__     ).manual_seed(0     )
    __lowercase								=						sd_pipe(
        [prompt], generator=UpperCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type="np", return_dict=UpperCAmelCase__, )[0]

    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)
    __lowercase								=						np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]     )

    assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2


 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						"cpu"  # ensure determinism for the device-dependent torch.Generator
    __lowercase								=						self.dummy_cond_unet
    __lowercase								=						PNDMScheduler(skip_prk_steps=UpperCAmelCase__     )
    __lowercase								=						self.dummy_vae
    __lowercase								=						self.dummy_text_encoder
    __lowercase								=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

    # make sure here that pndm scheduler skips prk
    __lowercase								=						StableDiffusionPipeline(
        unet=UpperCAmelCase__, scheduler=UpperCAmelCase__, vae=UpperCAmelCase__, text_encoder=UpperCAmelCase__, tokenizer=UpperCAmelCase__, safety_checker=UpperCAmelCase__, feature_extractor=self.dummy_extractor, )
    __lowercase								=						sd_pipe.to(UpperCAmelCase__     )
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

    __lowercase								=						"A painting of a squirrel eating a burger"
    __lowercase								=						torch.Generator(device=UpperCAmelCase__     ).manual_seed(0     )
    __lowercase								=						sd_pipe([prompt], generator=UpperCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type="np"     )

    __lowercase								=						output.images

    __lowercase								=						torch.Generator(device=UpperCAmelCase__     ).manual_seed(0     )
    __lowercase								=						sd_pipe(
        [prompt], generator=UpperCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type="np", return_dict=UpperCAmelCase__, )[0]

    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)
    __lowercase								=						np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]     )

    assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2


 def  _lowercase			(      self							:      Dict     ):
    __lowercase								=						StableDiffusionPipeline.from_pretrained(
        "hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=UpperCAmelCase__     )
    assert isinstance(UpperCAmelCase__, UpperCAmelCase__     )
    assert isinstance(pipe.scheduler, UpperCAmelCase__     )
    assert pipe.safety_checker is None

    __lowercase								=						pipe("example prompt", num_inference_steps=2     ).images[0]
    assert image is not None

    # check that there's no error when saving a pipeline with one of the models being None
    with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(UpperCAmelCase__     )
       __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase__     )

    # sanity check that the pipeline still works
    assert pipe.safety_checker is None
    __lowercase								=						pipe("example prompt", num_inference_steps=2     ).images[0]
    assert image is not None



 @unittest.skipIf(torch_device != "cuda", "This test requires a GPU"     )
 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						self.dummy_cond_unet
    __lowercase								=						PNDMScheduler(skip_prk_steps=UpperCAmelCase__     )
    __lowercase								=						self.dummy_vae
    __lowercase								=						self.dummy_text_encoder
    __lowercase								=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

    # put models in fp16
    __lowercase								=						unet.half()
    __lowercase								=						vae.half()
    __lowercase								=						bert.half()

    # make sure here that pndm scheduler skips prk
    __lowercase								=						StableDiffusionPipeline(
        unet=UpperCAmelCase__, scheduler=UpperCAmelCase__, vae=UpperCAmelCase__, text_encoder=UpperCAmelCase__, tokenizer=UpperCAmelCase__, safety_checker=UpperCAmelCase__, feature_extractor=self.dummy_extractor, )
    __lowercase								=						sd_pipe.to(UpperCAmelCase__     )
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

    __lowercase								=						"A painting of a squirrel eating a burger"
    __lowercase								=						sd_pipe([prompt], num_inference_steps=2, output_type="np"     ).images

    assert image.shape == (1, 6_4, 6_4, 3)







@nightly
@require_torch_gpu
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 def  _lowercase			(      self							:      Optional[Any]     ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def  _lowercase			(      self							:      List[Any]     ):
    __lowercase								=						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=UpperCAmelCase__     )
    __lowercase								=						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config     )
    __lowercase								=						sd_pipe.to(UpperCAmelCase__     )
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

    __lowercase								=						(
        "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
        " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
        " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
        " children from bahnhof zoo, detailed "
    )
    __lowercase								=						4_0_0_3_6_6_0_3_4_6
    __lowercase								=						7

    # without safety guidance (sld_guidance_scale = 0)
    __lowercase								=						torch.manual_seed(UpperCAmelCase__     )
    __lowercase								=						sd_pipe(
        [prompt], generator=UpperCAmelCase__, guidance_scale=UpperCAmelCase__, num_inference_steps=5_0, output_type="np", width=5_1_2, height=5_1_2, sld_guidance_scale=0, )

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						[0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

    # without safety guidance (strong configuration)
    __lowercase								=						torch.manual_seed(UpperCAmelCase__     )
    __lowercase								=						sd_pipe(
        [prompt], generator=UpperCAmelCase__, guidance_scale=UpperCAmelCase__, num_inference_steps=5_0, output_type="np", width=5_1_2, height=5_1_2, sld_guidance_scale=2_0_0_0, sld_warmup_steps=7, sld_threshold=0.025, sld_momentum_scale=0.5, sld_mom_beta=0.7, )

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						[0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2


 def  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=UpperCAmelCase__     )
    __lowercase								=						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config     )
    __lowercase								=						sd_pipe.to(UpperCAmelCase__     )
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

    __lowercase								=						"padme amidala taking a bath artwork, safe for work, no nudity"
    __lowercase								=						2_7_3_4_9_7_1_7_5_5
    __lowercase								=						7

    __lowercase								=						torch.manual_seed(UpperCAmelCase__     )
    __lowercase								=						sd_pipe(
        [prompt], generator=UpperCAmelCase__, guidance_scale=UpperCAmelCase__, num_inference_steps=5_0, output_type="np", width=5_1_2, height=5_1_2, sld_guidance_scale=0, )

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						[0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

    __lowercase								=						torch.manual_seed(UpperCAmelCase__     )
    __lowercase								=						sd_pipe(
        [prompt], generator=UpperCAmelCase__, guidance_scale=UpperCAmelCase__, num_inference_steps=5_0, output_type="np", width=5_1_2, height=5_1_2, sld_guidance_scale=2_0_0_0, sld_warmup_steps=7, sld_threshold=0.025, sld_momentum_scale=0.5, sld_mom_beta=0.7, )

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						[0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2



 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"     )
    __lowercase								=						sd_pipe.to(UpperCAmelCase__     )
    sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

    __lowercase								=						(
        "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
        " leyendecker"
    )
    __lowercase								=						1_0_4_4_3_5_5_2_3_4
    __lowercase								=						1_2

    __lowercase								=						torch.manual_seed(UpperCAmelCase__     )
    __lowercase								=						sd_pipe(
        [prompt], generator=UpperCAmelCase__, guidance_scale=UpperCAmelCase__, num_inference_steps=5_0, output_type="np", width=5_1_2, height=5_1_2, sld_guidance_scale=0, )

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]     )

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-7

    __lowercase								=						torch.manual_seed(UpperCAmelCase__     )
    __lowercase								=						sd_pipe(
        [prompt], generator=UpperCAmelCase__, guidance_scale=UpperCAmelCase__, num_inference_steps=5_0, output_type="np", width=5_1_2, height=5_1_2, sld_guidance_scale=2_0_0_0, sld_warmup_steps=7, sld_threshold=0.025, sld_momentum_scale=0.5, sld_mom_beta=0.7, )

    __lowercase								=						output.images
    __lowercase								=						image[0, -3:, -3:, -1]
    __lowercase								=						np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]     )
    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2







"""simple docstring"""





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
   _a							=							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')
   _a				,						_a							=							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')
      _a							=							rh.cluster(
          name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
      )
   else:
      _a							=							rh.cluster(
          name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
      )
   _a							=							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)




