



"""simple docstring"""

import importlib
import inspect
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_config_docstrings.py
a_			   =					'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
a_			   =					importlib.util.spec_from_file_location(
    '''transformers''',
    os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
a_			   =					spec.loader.load_module()

a_			   =					transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
a_			   =					re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


a_			   =					{
    '''CLIPConfigMixin''',
    '''DecisionTransformerConfigMixin''',
    '''EncoderDecoderConfigMixin''',
    '''RagConfigMixin''',
    '''SpeechEncoderDecoderConfigMixin''',
    '''VisionEncoderDecoderConfigMixin''',
    '''VisionTextDualEncoderConfigMixin''',
}







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					[]

					for config_class in list(CONFIG_MAPPING.values()			):
										snake_case_					:		List[str] 					=					False

										# source code of `config_class`
										snake_case_					:		Dict 					=					inspect.getsource(SCREAMING_SNAKE_CASE__			)
										snake_case_					:		Dict 					=					_re_checkpoint.findall(SCREAMING_SNAKE_CASE__			)

										for checkpoint in checkpoints:
															# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
															# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
															snake_case_					,			snake_case_					:		int 					=					checkpoint

															# verify the checkpoint name corresponds to the checkpoint link
															snake_case_					:		List[Any] 					=					f'https://huggingface.co/{ckpt_name}'
															if ckpt_link == ckpt_link_from_name:
																				snake_case_					:		Optional[Any] 					=					True
																				break

										snake_case_					:		int 					=					config_class.__name__
										if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
															configs_without_checkpoint.append(SCREAMING_SNAKE_CASE__			)

					if len(SCREAMING_SNAKE_CASE__			) > 0:
										snake_case_					:		str 					=					"""\n""".join(sorted(SCREAMING_SNAKE_CASE__			)			)
										raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}'			)


if __name__ == "__main__":
			check_config_docstrings_have_checkpoints()









"""simple docstring"""

import warnings

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







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""image_processor""", """tokenizer"""]
						_A		:							str							       =		"""ChineseCLIPImageProcessor"""
						_A		:							Tuple							       =		("""BertTokenizer""", """BertTokenizerFast""")







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):
											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."""   ,		lowercase__   ,		)
																snake_case_					:		Optional[Any] 					=					kwargs.pop("""feature_extractor"""       )

											snake_case_					:		str 					=					image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																raise ValueError("""You need to specify an `image_processor`."""       )
											if tokenizer is None:
																raise ValueError("""You need to specify a `tokenizer`."""       )

											super().__init__(lowercase__   ,		lowercase__       )
											snake_case_					:		Union[str, Any] 					=					self.image_processor







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

											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_					:		Any 					=					self.tokenizer(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if images is not None:
																snake_case_					:		Tuple 					=					self.image_processor(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if text is not None and images is not None:
																snake_case_					:		List[Any] 					=					image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**lowercase__       )   ,		tensor_type=lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.batch_decode(*lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.decode(*lowercase__   ,		**lowercase__       )







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.tokenizer.model_input_names
											snake_case_					:		Union[str, Any] 					=					self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







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




