

"""simple docstring"""





from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)




@dataclass
class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Union[List[PIL.Image.Image], np.ndarray]
 __UpperCAmelCase     : Optional[List[bool]]


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .pipeline_cycle_diffusion import CycleDiffusionPipeline
   from .pipeline_stable_diffusion import StableDiffusionPipeline
   from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
   from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
   from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
   from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
   from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
   from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
   from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
   from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
   from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
   from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
   from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
   from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
   from .pipeline_stable_unclip import StableUnCLIPPipeline
   from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
   from .safety_checker import StableDiffusionSafetyChecker
   from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
   if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
   from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
   if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.26.0')):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import (
       StableDiffusionDepthaImgPipeline,
       StableDiffusionDiffEditPipeline,
       StableDiffusionPixaPixZeroPipeline,
   )
else:
   from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
   from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
   from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
   if not (
       is_torch_available()
       and is_transformers_available()
       and is_k_diffusion_available()
       and is_k_diffusion_version('>=', '0.0.12')
   ):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
   from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
   if not (is_transformers_available() and is_onnx_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_onnx_objects import *  # noqa F403
else:
   from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
   from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
   from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
   from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
   from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
   import flax




   @flax.struct.dataclass
   class  _lowerCAmelCase  (     lowercase       ):







    """simple docstring"""







    __UpperCAmelCase     : np.ndarray
    __UpperCAmelCase     : List[bool]







   from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
   from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
   from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
   from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
   from .safety_checker_flax import FlaxStableDiffusionSafetyChecker







"""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)




