



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


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


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



def 		lowerCAmelCase__     (       )->	Optional[Any]:
    A__					=       '''https://pypi.org/pypi/diffusers/json'''
    A__					=       json.loads(request.urlopen(UpperCamelCase_						).read()						)['''releases'''].keys()
    return sorted(UpperCamelCase_			,							key=lambda UpperCamelCase_						: version.Version(UpperCamelCase_						)						)



def 		lowerCAmelCase__     (       )->	Union[str, Any]:
    # This function has already been executed if HF_MODULES_CACHE already is in the Python path.
    if HF_MODULES_CACHE in sys.path:
        return

    sys.path.append(UpperCamelCase_						)
    os.makedirs(UpperCamelCase_			,							exist_ok=UpperCamelCase_						)
    A__					=       Path(UpperCamelCase_						) / '''__init__.py'''
    if not init_path.exists():
        init_path.touch()



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, os.PathLike]						)->	Optional[Any]:
    init_hf_modules()
    A__					=       Path(UpperCamelCase_						) / 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(UpperCamelCase_			,							exist_ok=UpperCamelCase_						)
    A__					=       dynamic_module_path / '''__init__.py'''
    if not init_path.exists():
        init_path.touch()



def 		lowerCAmelCase__     (       UpperCamelCase_					:							int						)->	Dict:
    with open(UpperCamelCase_			,							'''r'''			,							encoding='''utf-8'''						) as f:
        A__					=       f.read()

    # Imports of the form `import .xxx`
    A__					=       re.findall('''^\s*import\s+\.(\S+)\s*$'''			,							UpperCamelCase_			,							flags=re.MULTILINE						)
    # Imports of the form `from .xxx import yyy`
    relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import'''			,							UpperCamelCase_			,							flags=re.MULTILINE						)
    # Unique-ify
    return list(set(UpperCamelCase_						)						)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Any						)->	List[Any]:
    A__					=       False
    A__					=       [module_file]
    A__					=       []

    # Let's recurse through all relative imports
    while not no_change:
        A__					=       []
        for f in files_to_check:
            new_imports.extend(get_relative_imports(UpperCamelCase_						)						)

        A__					=       Path(UpperCamelCase_						).parent
        A__					=       [str(module_path / m						) for m in new_imports]
        A__					=       [f for f in new_import_files if f not in all_relative_imports]
        A__					=       [f"{f}.py" for f in new_import_files]

        A__					=       len(UpperCamelCase_						) == 0
        all_relative_imports.extend(UpperCamelCase_						)

    return all_relative_imports



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Tuple						)->	int:
    with open(UpperCamelCase_			,							'''r'''			,							encoding='''utf-8'''						) as f:
        A__					=       f.read()

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

    # Unique-ify and test we got them all
    A__					=       list(set(UpperCamelCase_						)						)
    A__					=       []
    for imp in imports:
        try:
            importlib.import_module(UpperCamelCase_						)
        except ImportError:
            missing_packages.append(UpperCamelCase_						)

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

    return get_relative_imports(UpperCamelCase_						)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str			,							UpperCamelCase_					:							Tuple						)->	Tuple:
    A__					=       module_path.replace(os.path.sep			,							'''.'''						)
    A__					=       importlib.import_module(UpperCamelCase_						)

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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[int]						)->	Optional[Any]:
    from ..pipelines import DiffusionPipeline

    A__					=       dict(inspect.getmembers(UpperCamelCase_			,							inspect.isclass						)						)

    A__					=       None
    for cls_name, cls in cls_members.items():
        if (
            cls_name != DiffusionPipeline.__name__
            and issubclass(cls			,							UpperCamelCase_						)
            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}."						)
            A__					=       cls

    return pipeline_class



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, os.PathLike]			,							UpperCamelCase_					:							str			,							UpperCamelCase_					:							Optional[Union[str, os.PathLike]] = None			,							UpperCamelCase_					:							bool = False			,							UpperCamelCase_					:							bool = False			,							UpperCamelCase_					:							Optional[Dict[str, str]] = None			,							UpperCamelCase_					:							Optional[Union[bool, str]] = None			,							UpperCamelCase_					:							Optional[str] = None			,							UpperCamelCase_					:							bool = False			,							)->	Tuple:
    A__					=       str(UpperCamelCase_						)

    A__					=       os.path.join(UpperCamelCase_			,							UpperCamelCase_						)

    if os.path.isfile(UpperCamelCase_						):
        A__					=       module_file_or_url
        A__					=       '''local'''
    elif pretrained_model_name_or_path.count('''/'''						) == 0:
        A__					=       get_diffusers_versions()
        # cut ".dev0"
        A__					=       '''v''' + '''.'''.join(__version__.split('''.'''						)[:3]						)

        # retrieve github version that matches
        if revision is None:
            A__					=       latest_version if latest_version[1:] in available_versions else '''main'''
            logger.info(f"Defaulting to latest_version: {revision}."						)
        elif revision in available_versions:
            A__					=       f"v{revision}"
        elif revision == "main":
            A__					=       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
        A__					=       COMMUNITY_PIPELINES_URL.format(revision=UpperCamelCase_			,							pipeline=UpperCamelCase_						)
        try:
            A__					=       cached_download(
                UpperCamelCase_			,							cache_dir=UpperCamelCase_			,							force_download=UpperCamelCase_			,							proxies=UpperCamelCase_			,							resume_download=UpperCamelCase_			,							local_files_only=UpperCamelCase_			,							use_auth_token=UpperCamelCase_			,							)
            A__					=       '''git'''
            A__					=       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
            A__					=       hf_hub_download(
                UpperCamelCase_			,							UpperCamelCase_			,							cache_dir=UpperCamelCase_			,							force_download=UpperCamelCase_			,							proxies=UpperCamelCase_			,							resume_download=UpperCamelCase_			,							local_files_only=UpperCamelCase_			,							use_auth_token=UpperCamelCase_			,							)
            A__					=       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
    A__					=       check_imports(UpperCamelCase_						)

    # Now we move the module inside our cached dynamic modules.
    A__					=       DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
    create_dynamic_module(UpperCamelCase_						)
    A__					=       Path(UpperCamelCase_						) / 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(UpperCamelCase_			,							submodule_path / module_file						)
        for module_needed in modules_needed:
            A__					=       f"{module_needed}.py"
            shutil.copy(os.path.join(UpperCamelCase_			,							UpperCamelCase_						)			,							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(UpperCamelCase_			,							UpperCamelCase_						):
            A__					=       use_auth_token
        elif use_auth_token is True:
            A__					=       HfFolder.get_token()
        else:
            A__					=       None

        A__					=       model_info(UpperCamelCase_			,							revision=UpperCamelCase_			,							token=UpperCamelCase_						).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.
        A__					=       submodule_path / commit_hash
        A__					=       full_submodule + os.path.sep + commit_hash
        create_dynamic_module(UpperCamelCase_						)

        if not (submodule_path / module_file).exists():
            shutil.copy(UpperCamelCase_			,							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(
                    UpperCamelCase_			,							f"{module_needed}.py"			,							cache_dir=UpperCamelCase_			,							force_download=UpperCamelCase_			,							resume_download=UpperCamelCase_			,							proxies=UpperCamelCase_			,							use_auth_token=UpperCamelCase_			,							revision=UpperCamelCase_			,							local_files_only=UpperCamelCase_			,							)
    return os.path.join(UpperCamelCase_			,							UpperCamelCase_						)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, os.PathLike]			,							UpperCamelCase_					:							str			,							UpperCamelCase_					:							Optional[str] = None			,							UpperCamelCase_					:							Optional[Union[str, os.PathLike]] = None			,							UpperCamelCase_					:							bool = False			,							UpperCamelCase_					:							bool = False			,							UpperCamelCase_					:							Optional[Dict[str, str]] = None			,							UpperCamelCase_					:							Optional[Union[bool, str]] = None			,							UpperCamelCase_					:							Optional[str] = None			,							UpperCamelCase_					:							bool = False			,							**UpperCamelCase_					:							Tuple			,							)->	Dict:
    A__					=       get_cached_module_file(
        UpperCamelCase_			,							UpperCamelCase_			,							cache_dir=UpperCamelCase_			,							force_download=UpperCamelCase_			,							resume_download=UpperCamelCase_			,							proxies=UpperCamelCase_			,							use_auth_token=UpperCamelCase_			,							revision=UpperCamelCase_			,							local_files_only=UpperCamelCase_			,							)
    return get_class_in_module(UpperCamelCase_			,							final_module.replace('''.py'''			,							''''''						)						)





import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					0
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					3.0







class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()			,    {})
         self.assertDictEqual(MockClass(a=2).to_kwargs()			,    {'''a''': 2})
         self.assertDictEqual(MockClass(a=2			,    b=a__).to_kwargs()			,    {'''a''': 2, '''b''': True})
         self.assertDictEqual(MockClass(a=2			,    c=2.2_5).to_kwargs()			,    {'''a''': 2, '''c''': 2.2_5})




     @require_cuda
     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         A__					=       GradScalerKwargs(init_scale=1_0_2_4			,    growth_factor=2)
         AcceleratorState._reset_state()
         A__					=       Accelerator(mixed_precision='''fp16'''			,    kwargs_handlers=[scaler_handler])
         print(accelerator.use_fpaa)
         A__					=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale			,    1_0_2_4.0)
         self.assertEqual(scaler._growth_factor			,    2.0)

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor			,    0.5)
         self.assertEqual(scaler._growth_interval			,    2_0_0_0)
         self.assertEqual(scaler._enabled			,    a__)




     @require_multi_gpu
     def 		snake_case_		(					self):
         A__					=       ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__)]
         execute_subprocess_async(a__			,    env=os.environ.copy())


if __name__ == "__main__":
   _lowercase    	=		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _lowercase    	=		Accelerator(kwargs_handlers=[ddp_scaler])
   _lowercase    	=		torch.nn.Linear(100, 200)
   _lowercase    	=		accelerator.prepare(model)

   # Check the values changed in kwargs
   _lowercase    	=		""
   _lowercase    	=		model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)
