






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/transformers"

# 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      =				re.compile(R"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")


__UpperCAmelCase      =				"\n{0} = None\n"

__UpperCAmelCase      =				"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


__UpperCAmelCase      =				"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"





def 						A_					(						lowercase_			)				->Dict:







 """simple docstring"""

 if _re_test_backend.search(lowercase_			) is None:
  return None
 SCREAMING_SNAKE_CASE				    =		[b[0] for b in _re_backend.findall(lowercase_			)]
 backends.sort()
 return "_and_".join(lowercase_			)





def 						A_					(						)				->Tuple:







 """simple docstring"""

 with open(os.path.join(lowercase_	,  '__init__.py'			)	,  'r'	,  encoding='utf-8'	,  newline='\n'			) as f:
  SCREAMING_SNAKE_CASE				    =		f.readlines()

 # Get to the point we do the actual imports for type checking
 SCREAMING_SNAKE_CASE				    =		0
 while not lines[line_index].startswith('if TYPE_CHECKING'			):
  line_index += 1

 SCREAMING_SNAKE_CASE				    =		{}
 # Go through the end of the file
 while line_index < len(lowercase_			):
  # If the line is an if is_backend_available, we grab all objects associated.
  SCREAMING_SNAKE_CASE				    =		find_backend(lines[line_index]			)
  if backend is not None:
   while not lines[line_index].startswith('    else:'			):
    line_index += 1
   line_index += 1

   SCREAMING_SNAKE_CASE				    =		[]
   # Until we unindent, add backend objects to the list
   while len(lines[line_index]			) <= 1 or lines[line_index].startswith(' ' * 8			):
    SCREAMING_SNAKE_CASE				    =		lines[line_index]
    SCREAMING_SNAKE_CASE				    =		_re_single_line_import.search(lowercase_			)
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(', '			)			)
    elif line.startswith(' ' * 1_2			):
     objects.append(line[1_2:-2]			)
    line_index += 1

   SCREAMING_SNAKE_CASE				    =		objects
  else:
   line_index += 1

 return backend_specific_objects





def 						A_					(						lowercase_	,  lowercase_			)				->int:







 """simple docstring"""

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





def 						A_					(						lowercase_=None			)				->Tuple:







 """simple docstring"""

 if backend_specific_objects is None:
  SCREAMING_SNAKE_CASE				    =		read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 SCREAMING_SNAKE_CASE				    =		{}

 for backend, objects in backend_specific_objects.items():
  SCREAMING_SNAKE_CASE				    =		'[' + ', '.join(f'''"{b}"''' for b in backend.split('_and_'			)			) + ']'
  SCREAMING_SNAKE_CASE				    =		'# 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(lowercase_	,  lowercase_			) for o in objects]			)
  SCREAMING_SNAKE_CASE				    =		dummy_file

 return dummy_files





def 						A_					(						lowercase_=False			)				->Optional[Any]:







 """simple docstring"""

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

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

 SCREAMING_SNAKE_CASE				    =		{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(lowercase_			):
   with open(lowercase_	,  'r'	,  encoding='utf-8'	,  newline='\n'			) as f:
    SCREAMING_SNAKE_CASE				    =		f.read()
  else:
   SCREAMING_SNAKE_CASE				    =		''

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        f'''Updating transformers.utils.dummy_{short_names.get(lowercase_	,  lowercase_			)}_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'''transformers.utils.dummy_{short_names.get(lowercase_	,  lowercase_			)}_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)





'''simple docstring'''


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  str  = min(re.search('^ +'      ,	lowerCamelCase_  ).end() if t.startswith(' '  ) else 0 for t in string.splitlines()  )
     return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowerCamelCase_  )]  )


snake_case__	:     Dict      =    '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

snake_case__	:     Union[str, Any]      =    '''\
---
---
# Begin of markdown

Some cool dataset card
'''


snake_case__	:     int      =    '''\
# Begin of markdown

Some cool dataset card
'''






class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase_				:  Tuple  = Path(snake_case_		) / 'README.md'
            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  Optional[Any]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertDictEqual(snake_case_ ,						{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}		)

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  Union[str, Any]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertDictEqual(snake_case_ ,						{}		)

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  List[str]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertEqual(snake_case_ ,						{}		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '		)
       assert DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  int  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '		)
       with self.assertRaises(snake_case_		):
            DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  str  = _dedent(
           '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '		)
       assert DatasetMetadata.from_yaml_string(snake_case_		)






