
'''simple docstring'''

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
UpperCamelCase_   	=				"""src/diffusers"""

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


UpperCamelCase_   	=				"""
{0} = None
"""

UpperCamelCase_   	=				"""
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})
"""


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


def 		_UpperCAmelCase (     _lowerCamelCase  :				List[str]						)			->  Dict:
  _lowerCAmelCase				:     Union[str, Any]       =    _re_backend.findall(_lowerCamelCase						)
  if len(_lowerCamelCase						) == 0:
    return None

  return "_and_".join(_lowerCamelCase						)


def 		_UpperCAmelCase (     )			->  Any:
  with open(os.path.join(_lowerCamelCase     ,					"""__init__.py"""						)     ,					"""r"""     ,					encoding="""utf-8"""     ,					newline="""\n"""						) as f:
    _lowerCAmelCase				:     Any       =    f.readlines()

  # Get to the point we do the actual imports for type checking
  _lowerCAmelCase				:     List[Any]       =    0
  _lowerCAmelCase				:     Tuple       =    {}
  # 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
    _lowerCAmelCase				:     Tuple       =    find_backend(lines[line_index]						)
    if backend is not None:
      while not lines[line_index].startswith("""else:"""						):
        line_index += 1
      line_index += 1
      _lowerCAmelCase				:     str       =    []
      # Until we unindent, add backend objects to the list
      while line_index < len(_lowerCamelCase						) and len(lines[line_index]						) > 1:
        _lowerCAmelCase				:     Any       =    lines[line_index]
        _lowerCAmelCase				:     Union[str, Any]       =    _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:
        _lowerCAmelCase				:     List[str]       =    objects
    else:
      line_index += 1

  return backend_specific_objects


def 		_UpperCAmelCase (     _lowerCamelCase  :				List[str]     ,					_lowerCamelCase  :				int						)			->  Optional[int]:
  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 		_UpperCAmelCase (     _lowerCamelCase  :				List[Any]=None						)			->  Union[str, Any]:
  if backend_specific_objects is None:
    _lowerCAmelCase				:     Union[str, Any]       =    read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  _lowerCAmelCase				:     Dict       =    {}

  for backend, objects in backend_specific_objects.items():
    _lowerCAmelCase				:     Any       =    "[" + ", ".join(f'"{b}"' for b in backend.split("""_and_"""						)						) + "]"
    _lowerCAmelCase				:     Union[str, 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]						)
    _lowerCAmelCase				:     Tuple       =    dummy_file

  return dummy_files


def 		_UpperCAmelCase (     _lowerCamelCase  :				int=False						)			->  Optional[int]:
  _lowerCAmelCase				:     Optional[Any]       =    create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  _lowerCAmelCase				:     Tuple       =    {"torch": "pt"}

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

  _lowerCAmelCase				:     Tuple       =    {}
  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:
        _lowerCAmelCase				:     Optional[Any]       =    f.read()
    else:
      _lowerCAmelCase				:     str       =    ""

  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__":
     UpperCamelCase_   	=				argparse.ArgumentParser()
     parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
     UpperCamelCase_   	=				parser.parse_args()

     check_dummies(args.fix_and_overwrite)



import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow



@is_pipeline_test
@require_torch
class   snake_case_       (		unittest.TestCase       ):






   @require_torch
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						List[Any]:
       lowercase__ :   Union[str, Any]    					=				pipeline(
           task="zero-shot-audio-classification"	,						model="hf-internal-testing/tiny-clap-htsat-unfused"  )
       lowercase__ :   List[str]    					=				load_dataset("ashraq/esc50"  )
       lowercase__ :   List[Any]    					=				dataset["train"]["audio"][-1]["array"]
       lowercase__ :   Dict    					=				audio_classifier(lowercase_	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]  )
       self.assertEqual(
           nested_simplify(lowercase_  )	,						[{"score": 0.5_01, "label": "Sound of a dog"}, {"score": 0.4_99, "label": "Sound of vaccum cleaner"}]	,						)






   @unittest.skip("No models are available in TF"  )
   def       __UpperCamelCase     (      self				:     str  )					->						Optional[int]:
       pass






   @slow
   @require_torch
   def       __UpperCamelCase     (      self				:     List[str]  )					->						int:
       lowercase__ :   Tuple    					=				pipeline(
           task="zero-shot-audio-classification"	,						model="laion/clap-htsat-unfused"	,						)
       # This is an audio of a dog
       lowercase__ :   Union[str, Any]    					=				load_dataset("ashraq/esc50"  )
       lowercase__ :   Tuple    					=				dataset["train"]["audio"][-1]["array"]
       lowercase__ :   List[Any]    					=				audio_classifier(lowercase_	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]  )

       self.assertEqual(
           nested_simplify(lowercase_  )	,						[
               {"score": 0.9_99, "label": "Sound of a dog"},
               {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
           ]	,						)

       lowercase__ :   int    					=				audio_classifier([audio] * 5	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]  )
       self.assertEqual(
           nested_simplify(lowercase_  )	,						[
               [
                   {"score": 0.9_99, "label": "Sound of a dog"},
                   {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
               ],
           ]
           * 5	,						)
       lowercase__ :   Tuple    					=				audio_classifier(
           [audio] * 5	,						candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]	,						batch_size=5  )
       self.assertEqual(
           nested_simplify(lowercase_  )	,						[
               [
                   {"score": 0.9_99, "label": "Sound of a dog"},
                   {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
               ],
           ]
           * 5	,						)







   @unittest.skip("No models are available in TF"  )
   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Dict:
       pass



