




import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


_UpperCAmelCase	:						Dict			=     (
    "https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
)


_UpperCAmelCase	:						Any			=     logging.get_logger(__name__)  # pylint: disable=invalid-name



def       UpperCAmelCase__  (     ):
  lowercase    :Union[str, Any]      	=						"https://pypi.org/pypi/diffusers/json"
  lowercase    :Optional[int]      	=						json.loads(request.urlopen(_lowercase   ).read()   )["releases"].keys()
  return sorted(_lowercase,			key=lambda lowerCamelCase   : version.Version(_lowercase   )   )



def       UpperCAmelCase__  (     ):







  if HF_MODULES_CACHE in sys.path:
    return

  sys.path.append(_lowercase   )
  os.makedirs(_lowercase,			exist_ok=_lowercase   )
  lowercase    :List[str]      	=						Path(_lowercase   ) / "__init__.py"
  if not init_path.exists():
    init_path.touch()



def       UpperCAmelCase__  (     lowerCamelCase   ):
  init_hf_modules()
  lowercase    :Optional[Any]      	=						Path(_lowercase   ) / name
  # If the parent module does not exist yet, recursively create it.
  if not dynamic_module_path.parent.exists():
    create_dynamic_module(dynamic_module_path.parent   )
  os.makedirs(_lowercase,			exist_ok=_lowercase   )
  lowercase    :str      	=						dynamic_module_path / "__init__.py"
  if not init_path.exists():
    init_path.touch()



def       UpperCAmelCase__  (     lowerCamelCase   ):







  with open(_lowercase,			"r",			encoding="utf-8"   ) as f:
    lowercase    :List[Any]      	=						f.read()

  # Imports of the form `import .xxx`
  lowercase    :Union[str, Any]      	=						re.findall("^\s*import\s+\.(\S+)\s*$",			_lowercase,			flags=re.MULTILINE   )
  # Imports of the form `from .xxx import yyy`
  relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import",			_lowercase,			flags=re.MULTILINE   )
  # Unique-ify
  return list(set(_lowercase   )   )



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :List[str]      	=						False
  lowercase    :List[str]      	=						[module_file]
  lowercase    :List[str]      	=						[]

  # Let's recurse through all relative imports
  while not no_change:
    lowercase    :Union[str, Any]      	=						[]
    for f in files_to_check:
      new_imports.extend(get_relative_imports(_lowercase   )   )

    lowercase    :List[str]      	=						Path(_lowercase   ).parent
    lowercase    :int      	=						[str(module_path / m   ) for m in new_imports]
    lowercase    :Dict      	=						[f for f in new_import_files if f not in all_relative_imports]
    lowercase    :Tuple      	=						[F"{f}.py" for f in new_import_files]

    lowercase    :str      	=						len(_lowercase   ) == 0
    all_relative_imports.extend(_lowercase   )

  return all_relative_imports



def       UpperCAmelCase__  (     lowerCamelCase   ):







  with open(_lowercase,			"r",			encoding="utf-8"   ) as f:
    lowercase    :int      	=						f.read()

  # Imports of the form `import xxx`
  lowercase    :Dict      	=						re.findall("^\s*import\s+(\S+)\s*$",			_lowercase,			flags=re.MULTILINE   )
  # Imports of the form `from xxx import yyy`
  imports += re.findall("^\s*from\s+(\S+)\s+import",			_lowercase,			flags=re.MULTILINE   )
  # Only keep the top-level module
  lowercase    :int      	=						[imp.split("."   )[0] for imp in imports if not imp.startswith("."   )]

  # Unique-ify and test we got them all
  lowercase    :Optional[int]      	=						list(set(_lowercase   )   )
  lowercase    :List[Any]      	=						[]
  for imp in imports:
    try:
      importlib.import_module(_lowercase   )
    except ImportError:
      missing_packages.append(_lowercase   )

  if len(_lowercase   ) > 0:
    raise ImportError(
        "This modeling file requires the following packages that were not found in your environment: "
        F"{', '.join(_lowercase   )}. Run `pip install {' '.join(_lowercase   )}`"   )

  return get_relative_imports(_lowercase   )



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  lowercase    :List[Any]      	=						module_path.replace(os.path.sep,			"."   )
  lowercase    :Optional[int]      	=						importlib.import_module(_lowercase   )

  if class_name is None:
    return find_pipeline_class(_lowercase   )
  return getattr(_lowercase,			_lowercase   )



def       UpperCAmelCase__  (     lowerCamelCase   ):
  from ..pipelines import DiffusionPipeline

  lowercase    :List[Any]      	=						dict(inspect.getmembers(_lowercase,			inspect.isclass   )   )

  lowercase    :Any      	=						None
  for cls_name, cls in cls_members.items():
    if (
        cls_name != DiffusionPipeline.__name__
        and issubclass(cls,			_lowercase   )
        and cls.__module__.split("."   )[0] != "diffusers"
    ):
      if pipeline_class is not None:
        raise ValueError(
            F"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"
            F" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"
            F" {loaded_module}."   )
      lowercase    :Dict      	=						cls

  return pipeline_class



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase = None,			lowerCamelCase = False,			lowerCamelCase = False,			lowerCamelCase = None,			lowerCamelCase = None,			lowerCamelCase = None,			lowerCamelCase = False,			):
  lowercase    :int      	=						str(_lowercase   )

  lowercase    :Optional[int]      	=						os.path.join(_lowercase,			_lowercase   )

  if os.path.isfile(_lowercase   ):
    lowercase    :Any      	=						module_file_or_url
    lowercase    :int      	=						"local"
  elif pretrained_model_name_or_path.count("/"   ) == 0:
    lowercase    :List[Any]      	=						get_diffusers_versions()
    # cut ".dev0"
    lowercase    :Optional[int]      	=						"v" + ".".join(__version__.split("."   )[:3]   )

    # retrieve github version that matches
    if revision is None:
      lowercase    :Dict      	=						latest_version if latest_version[1:] in available_versions else "main"
      logger.info(F"Defaulting to latest_version: {revision}."   )
    elif revision in available_versions:
      lowercase    :Union[str, Any]      	=						F"v{revision}"
    elif revision == "main":
      lowercase    :Any      	=						revision
    else:
      raise ValueError(
          F"`custom_revision`: {revision} does not exist. Please make sure to choose one of"
          F" {', '.join(available_versions + ['main']   )}."   )

    # community pipeline on GitHub
    lowercase    :str      	=						COMMUNITY_PIPELINES_URL.format(revision=_lowercase,			pipeline=_lowercase   )
    try:
      lowercase    :List[str]      	=						cached_download(
          _lowercase,			cache_dir=_lowercase,			force_download=_lowercase,			proxies=_lowercase,			resume_download=_lowercase,			local_files_only=_lowercase,			use_auth_token=_lowercase,			)
      lowercase    :Dict      	=						"git"
      lowercase    :List[Any]      	=						pretrained_model_name_or_path + ".py"
    except EnvironmentError:
      logger.error(F"Could not locate the {module_file} inside {pretrained_model_name_or_path}."   )
      raise
  else:
    try:
      # Load from URL or cache if already cached
      lowercase    :Any      	=						hf_hub_download(
          _lowercase,			_lowercase,			cache_dir=_lowercase,			force_download=_lowercase,			proxies=_lowercase,			resume_download=_lowercase,			local_files_only=_lowercase,			use_auth_token=_lowercase,			)
      lowercase    :str      	=						os.path.join("local",			"--".join(pretrained_model_name_or_path.split("/"   )   )   )
    except EnvironmentError:
      logger.error(F"Could not locate the {module_file} inside {pretrained_model_name_or_path}."   )
      raise

    # Check we have all the requirements in our environment
  lowercase    :str      	=						check_imports(_lowercase   )

  # Now we move the module inside our cached dynamic modules.
  lowercase    :Dict      	=						DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
  create_dynamic_module(_lowercase   )
  lowercase    :List[Any]      	=						Path(_lowercase   ) / full_submodule
  if submodule == "local" or submodule == "git":
    # We always copy local files (we could hash the file to see if there was a change, and give them the name of
    # that hash, to only copy when there is a modification but it seems overkill for now).
    # The only reason we do the copy is to avoid putting too many folders in sys.path.
    shutil.copy(_lowercase,			submodule_path / module_file   )
    for module_needed in modules_needed:
      lowercase    :Optional[Any]      	=						F"{module_needed}.py"
      shutil.copy(os.path.join(_lowercase,			_lowercase   ),			submodule_path / module_needed   )
  else:
    # Get the commit hash
    # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
    if isinstance(_lowercase,			_lowercase   ):
      lowercase    :str      	=						use_auth_token
    elif use_auth_token is True:
      lowercase    :Any      	=						HfFolder.get_token()
    else:
      lowercase    :int      	=						None

    lowercase    :int      	=						model_info(_lowercase,			revision=_lowercase,			token=_lowercase   ).sha

    # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
    # benefit of versioning.
    lowercase    :int      	=						submodule_path / commit_hash
    lowercase    :Tuple      	=						full_submodule + os.path.sep + commit_hash
    create_dynamic_module(_lowercase   )

    if not (submodule_path / module_file).exists():
      shutil.copy(_lowercase,			submodule_path / module_file   )
    # Make sure we also have every file with relative
    for module_needed in modules_needed:
      if not (submodule_path / module_needed).exists():
        get_cached_module_file(
            _lowercase,			F"{module_needed}.py",			cache_dir=_lowercase,			force_download=_lowercase,			resume_download=_lowercase,			proxies=_lowercase,			use_auth_token=_lowercase,			revision=_lowercase,			local_files_only=_lowercase,			)
  return os.path.join(_lowercase,			_lowercase   )







def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase = None,			lowerCamelCase = None,			lowerCamelCase = False,			lowerCamelCase = False,			lowerCamelCase = None,			lowerCamelCase = None,			lowerCamelCase = None,			lowerCamelCase = False,			**lowerCamelCase,			):
  lowercase    :List[Any]      	=						get_cached_module_file(
      _lowercase,			_lowercase,			cache_dir=_lowercase,			force_download=_lowercase,			resume_download=_lowercase,			proxies=_lowercase,			use_auth_token=_lowercase,			revision=_lowercase,			local_files_only=_lowercase,			)
  return get_class_in_module(_lowercase,			final_module.replace(".py",			""   )   )









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	:						Optional[Any]			=     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	:						Dict			=     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	:						List[Any]			=     rh.cluster(
              name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
          )
     else:
          _UpperCAmelCase	:						Tuple			=     rh.cluster(
              name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
          )
     _UpperCAmelCase	:						Any			=     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)



