'''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
A_	   = "src/transformers"

# Matches is_xxx_available()
A_	   = re.compile(R"is\_([a-z_]*)_available()")
# Matches from xxx import bla
A_	   = re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
A_	   = re.compile(R"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")


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

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


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


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Optional[Any])    ->		List[Any]:



						"""simple docstring"""

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


def        UpperCAmelCase__   (				)    ->		Tuple:



						"""simple docstring"""

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

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

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

																		snake_case :       Tuple				=    []
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8):
																								snake_case :       str				=    lines[line_index]
																								snake_case :       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(" " * 12):
																														objects.append(line[12:-2])
																								line_index += 1

																		snake_case :       Optional[int]				=    objects
												else:
																		line_index += 1

						return backend_specific_objects


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Optional[Any],      _lowerCAmelCase    :			Any)    ->		Union[str, Any]:



						"""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        UpperCAmelCase__   (				_lowerCAmelCase    :			int=None)    ->		Any:



						"""simple docstring"""

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

						for backend, objects in backend_specific_objects.items():
												snake_case :       str				=    "[" + ", ".join(f"""\"{b}\"""" for b in backend.split("_and_")) + "]"
												snake_case :       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])
												snake_case :       Dict				=    dummy_file

						return dummy_files


def        UpperCAmelCase__   (				_lowerCAmelCase    :			List[str]=False)    ->		Optional[int]:



						"""simple docstring"""

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

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

						snake_case :       Dict				=    {}
						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:
																								snake_case :       Optional[int]				=    f.read()
												else:
																		snake_case :       str				=    ""

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    f"""Updating transformers.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"""transformers.utils.dummy_{short_names.get(_lowerCAmelCase,      _lowerCAmelCase)}_objects.py. Run `make fix-copies` """
																								    "to fix this.")


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

						check_dummies(args.fix_and_overwrite)






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Tuple   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				List[str]   							=       """AutoImageProcessor"""
			__magic_name__ :				List[str]   							=       """AutoTokenizer"""



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       Any				=    None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."				,			snake_case_				,			)
															snake_case :       str				=    kwargs.pop("feature_extractor"					)

									snake_case :       Optional[int]				=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("You have to specify an image_processor."					)
									if tokenizer is None:
															raise ValueError("You have to specify a tokenizer."					)

									super().__init__(snake_case_				,			snake_case_					)
									snake_case :       Optional[int]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

									if text is None and images is None:
															raise ValueError("You have to specify either text or images. Both cannot be none."					)

									if text is not None:
															snake_case :       Optional[int]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if images is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if text is not None and images is not None:
															snake_case :       str				=    image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**snake_case_					)				,			tensor_type=snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    self.tokenizer.model_input_names
									snake_case :       Any				=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)



			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			snake_case_				,			)
									return self.image_processor_class






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor





