




'''simple docstring'''
import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
  from .testing_utils import (
      floats_tensor,
      load_hf_numpy,
      load_image,
      load_numpy,
      load_pt,
      nightly,
      parse_flag_from_env,
      print_tensor_test,
      require_torch_a,
      require_torch_gpu,
      skip_mps,
      slow,
      torch_all_close,
      torch_device,
  )
  from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A				: Optional[int]					      =			get_logger(__name__)
def 							snake_case_    (  a__						:      Tuple     ):



    """simple docstring"""




    if version.parse(lowercase_     ) < version.parse(lowercase_     ):
        if "dev" in min_version:
            __lowercase     							=		(
                """This example requires a source install from HuggingFace diffusers (see """
                """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
            )
        else:
            __lowercase     							=		f'This example requires a minimum version of {min_version},'
        error_message += f' but the version found is {__version__}.\n'
        raise ImportError(lowercase_     )










"""simple docstring"""



import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


SCREAMING_SNAKE_CASE							=			2 << 20  # 2mb
SCREAMING_SNAKE_CASE							=			get_logger(__name__)




class       UpperCAmelCase_    (		A_				):




    def 				__magic_name__				(       self		:			Tuple ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							self._options.get_all_options().get("runner" )
     return runner in [None, "DirectRunner", "PortableRunner"]







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_=False		)		->						Dict:
 A__			=							FileSystems
 if fs.exists(lowercase_		):
  if force_upload:
   logger.info(f"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""		)
  else:
   logger.info(f"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""		)
   return
 with fs.create(lowercase_		) as remote_file:
  with open(lowercase_     ,					"rb"		) as local_file:
   A__			=							local_file.read(lowercase_		)
   while chunk:
    remote_file.write(lowercase_		)
    A__			=							local_file.read(lowercase_		)







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_=False		)		->						int:
 A__			=							FileSystems
 if os.path.exists(lowercase_		):
  if force_download:
   logger.info(f"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""		)
  else:
   logger.info(f"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""		)
   return
 with fs.open(lowercase_		) as remote_file:
  with open(lowercase_     ,					"wb"		) as local_file:
   A__			=							remote_file.read(lowercase_		)
   while chunk:
    local_file.write(lowercase_		)
    A__			=							remote_file.read(lowercase_		)





