





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_lowerCAmelCase 				=     """src/diffusers"""

# Matches is_xxx_available()
_lowerCAmelCase 				=     re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
_lowerCAmelCase 				=     re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


_lowerCAmelCase 				=     """
{0} = None
"""

_lowerCAmelCase 				=     """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


_lowerCAmelCase 				=     """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Any			      =	_re_backend.findall(_lowerCAmelCase     )
   if len(_lowerCAmelCase     ) == 0:
      return None

   return "_and_".join(_lowerCAmelCase     )



def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   with open(os.path.join(_lowerCAmelCase  ,"""__init__.py"""     )  ,"""r"""  ,encoding="""utf-8"""  ,newline="""\n"""     ) as f:
      A_     :				str			      =	f.readlines()

   # Get to the point we do the actual imports for type checking
   A_     :				Optional[int]			      =	0
   A_     :				List[Any]			      =	{}
   # Go through the end of the file
   while line_index < len(_lowerCAmelCase     ):
      # If the line contains is_backend_available, we grab all objects associated with the `else` block
      A_     :				Dict			      =	find_backend(lines[line_index]     )
      if backend is not None:
         while not lines[line_index].startswith("""else:"""     ):
            line_index += 1
         line_index += 1
         A_     :				Any			      =	[]
         # Until we unindent, add backend objects to the list
         while line_index < len(_lowerCAmelCase     ) and len(lines[line_index]     ) > 1:
            A_     :				Union[str, Any]			      =	lines[line_index]
            A_     :				Tuple			      =	_re_single_line_import.search(_lowerCAmelCase     )
            if single_line_import_search is not None:
               objects.extend(single_line_import_search.groups()[0].split(""", """     )     )
            elif line.startswith(""" """ * 8     ):
               objects.append(line[8:-2]     )
            line_index += 1

         if len(_lowerCAmelCase     ) > 0:
            A_     :				Tuple			      =	objects
      else:
         line_index += 1

   return backend_specific_objects



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   if name.isupper():
      return DUMMY_CONSTANT.format(_lowerCAmelCase     )
   elif name.islower():
      return DUMMY_FUNCTION.format(_lowerCAmelCase  ,_lowerCAmelCase     )
   else:
      return DUMMY_CLASS.format(_lowerCAmelCase  ,_lowerCAmelCase     )



def     _lowerCAmelCase		(  _lowerCAmelCase=None     ):
   '''simple docstring'''


   if backend_specific_objects is None:
      A_     :				Dict			      =	read_init()
   # For special correspondence backend to module name as used in the function requires_modulename
   A_     :				Optional[Any]			      =	{}

   for backend, objects in backend_specific_objects.items():
      A_     :				int			      =	"""[""" + """, """.join(f"""\"{b}\"""" for b in backend.split("""_and_"""     )     ) + """]"""
      A_     :				Optional[Any]			      =	"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
      dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
      dummy_file += "\n".join([create_dummy_object(_lowerCAmelCase  ,_lowerCAmelCase     ) for o in objects]     )
      A_     :				List[Any]			      =	dummy_file

   return dummy_files



def     _lowerCAmelCase		(  _lowerCAmelCase=False     ):
   '''simple docstring'''


   A_     :				Any			      =	create_dummy_files()
   # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
   A_     :				str			      =	{"""torch""": """pt"""}

   # Locate actual dummy modules and read their content.
   A_     :				Optional[Any]			      =	os.path.join(_lowerCAmelCase  ,"""utils"""     )
   A_     :				int			      =	{
       backend: os.path.join(_lowerCAmelCase  ,f"""dummy_{short_names.get(_lowerCAmelCase  ,_lowerCAmelCase     )}_objects.py"""     )
       for backend in dummy_files.keys()
   }

   A_     :				Optional[Any]			      =	{}
   for backend, file_path in dummy_file_paths.items():
      if os.path.isfile(_lowerCAmelCase     ):
         with open(_lowerCAmelCase  ,"""r"""  ,encoding="""utf-8"""  ,newline="""\n"""     ) as f:
            A_     :				Optional[int]			      =	f.read()
      else:
         A_     :				Optional[int]			      =	""""""

   for backend in dummy_files.keys():
      if dummy_files[backend] != actual_dummies[backend]:
         if overwrite:
            print(
                f"""Updating diffusers.utils.dummy_{short_names.get(_lowerCAmelCase  ,_lowerCAmelCase     )}_objects.py as the main """
                """__init__ has new objects."""     )
            with open(dummy_file_paths[backend]  ,"""w"""  ,encoding="""utf-8"""  ,newline="""\n"""     ) as f:
               f.write(dummy_files[backend]     )
         else:
            raise ValueError(
                """The main __init__ has objects that are not present in """
                f"""diffusers.utils.dummy_{short_names.get(_lowerCAmelCase  ,_lowerCAmelCase     )}_objects.py. Run `make fix-copies` """
                """to fix this."""     )


if __name__ == "__main__":
      _lowerCAmelCase 				=     argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      _lowerCAmelCase 				=     parser.parse_args()

      check_dummies(args.fix_and_overwrite)













from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






