



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class     lowerCAmelCase_		(  _UpperCAmelCase	,   _UpperCAmelCase	,   unittest.TestCase							):
       UpperCAmelCase__    : Any       					=  StableDiffusionSAGPipeline
       UpperCAmelCase__    : Union[str, Any]       					=  TEXT_TO_IMAGE_PARAMS
       UpperCAmelCase__    : List[Any]       					=  TEXT_TO_IMAGE_BATCH_PARAMS
       UpperCAmelCase__    : Any       					=  TEXT_TO_IMAGE_IMAGE_PARAMS
       UpperCAmelCase__    : List[str]       					=  TEXT_TO_IMAGE_IMAGE_PARAMS
       UpperCAmelCase__    : Union[str, Any]       					=  False
       def  snake_case_	(      self	)					->		str:
          torch.manual_seed(0	)
          UpperCamelCase   :  str						     =     UNetaDConditionModel(
              block_out_channels=(32, 64),      layers_per_block=2,      sample_size=32,      in_channels=4,      out_channels=4,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),      cross_attention_dim=32,      )
          UpperCamelCase   :  Any						     =     DDIMScheduler(
              beta_start=0.0_00_85,      beta_end=0.0_12,      beta_schedule='scaled_linear',      clip_sample=lowercase_,      set_alpha_to_one=lowercase_,      )
          torch.manual_seed(0	)
          UpperCamelCase   :  Dict						     =     AutoencoderKL(
              block_out_channels=[32, 64],      in_channels=3,      out_channels=3,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],      latent_channels=4,      )
          torch.manual_seed(0	)
          UpperCamelCase   :  Any						     =     CLIPTextConfig(
              bos_token_id=0,      eos_token_id=2,      hidden_size=32,      intermediate_size=37,      layer_norm_eps=1e-05,      num_attention_heads=4,      num_hidden_layers=5,      pad_token_id=1,      vocab_size=1000,      )
          UpperCamelCase   :  Dict						     =     CLIPTextModel(lowercase_	)
          UpperCamelCase   :  int						     =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

          UpperCamelCase   :  Dict						     =     {
              """unet""": unet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=0	)					->		List[str]:
          if str(lowercase_	).startswith('mps'	):
             UpperCamelCase   :  Tuple						     =     torch.manual_seed(lowercase_	)
          else:
             UpperCamelCase   :  str						     =     torch.Generator(device=lowercase_	).manual_seed(lowercase_	)
          UpperCamelCase   :  Any						     =     {
              """prompt""": """.""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 1.0,
              """sag_scale""": 1.0,
              """output_type""": """numpy""",
          }
          return inputs






       def  snake_case_	(      self	)					->		Union[str, Any]:
          super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






@slow
@require_torch_gpu
class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self	)					->		Dict:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  str						     =     StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'	)
          UpperCamelCase   :  Optional[int]						     =     sag_pipe.to(lowercase_	)
          sag_pipe.set_progress_bar_config(disable=lowercase_	)

          UpperCamelCase   :  str						     =     """."""
          UpperCamelCase   :  List[Any]						     =     torch.manual_seed(0	)
          UpperCamelCase   :  int						     =     sag_pipe(
              [prompt],      generator=lowercase_,      guidance_scale=7.5,      sag_scale=1.0,      num_inference_steps=20,      output_type='np'	)

          UpperCamelCase   :  Dict						     =     output.images

          UpperCamelCase   :  Union[str, Any]						     =     image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  List[str]						     =     np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49]	)

          assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-2
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Tuple						     =     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'	)
          UpperCamelCase   :  Tuple						     =     sag_pipe.to(lowercase_	)
          sag_pipe.set_progress_bar_config(disable=lowercase_	)

          UpperCamelCase   :  str						     =     """."""
          UpperCamelCase   :  Any						     =     torch.manual_seed(0	)
          UpperCamelCase   :  Optional[int]						     =     sag_pipe(
              [prompt],      generator=lowercase_,      guidance_scale=7.5,      sag_scale=1.0,      num_inference_steps=20,      output_type='np'	)

          UpperCamelCase   :  str						     =     output.images

          UpperCamelCase   :  Optional[Any]						     =     image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCamelCase   :  Any						     =     np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71]	)

          assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-2






       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'	)
          UpperCamelCase   :  Union[str, Any]						     =     sag_pipe.to(lowercase_	)
          sag_pipe.set_progress_bar_config(disable=lowercase_	)

          UpperCamelCase   :  Optional[Any]						     =     """."""
          UpperCamelCase   :  List[str]						     =     torch.manual_seed(0	)
          UpperCamelCase   :  int						     =     sag_pipe(
              [prompt],      width=768,      height=512,      generator=lowercase_,      guidance_scale=7.5,      sag_scale=1.0,      num_inference_steps=20,      output_type='np',      )

          UpperCamelCase   :  int						     =     output.images

          assert image.shape == (1, 512, 768, 3)








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



