






import os

import pytest

from transformers.dynamic_module_utils import get_imports


SCREAMING_SNAKE_CASE__   :      List[Any]				=		"""
import os
"""

SCREAMING_SNAKE_CASE__   :      Any				=		"""
def foo():
    import os
    return False
"""

SCREAMING_SNAKE_CASE__   :      int				=		"""
def foo():
    def bar():
        if True:
            import os
        return False
    return bar()
"""

SCREAMING_SNAKE_CASE__   :      Tuple				=		"""
import os

try:
    import bar
except ImportError:
    raise ValueError()
"""

SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		"""
import os

def foo():
    try:
        import bar
    except ImportError:
        raise ValueError()
"""

SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		"""
import os

try:
    import bar
except (ImportError, AttributeError):
    raise ValueError()
"""

SCREAMING_SNAKE_CASE__   :      Optional[int]				=		"""
import os

try:
    import bar
except ImportError as e:
    raise ValueError()
"""

SCREAMING_SNAKE_CASE__   :      Tuple				=		"""
import os

try:
    import bar
except:
    raise ValueError()
"""

SCREAMING_SNAKE_CASE__   :      int				=		"""
import os

try:
    import bar
    import baz
except ImportError:
    raise ValueError()
"""

SCREAMING_SNAKE_CASE__   :      Dict				=		"""
import os

try:
    import bar
    import baz
except ImportError:
    x = 1
    raise ValueError()
"""

SCREAMING_SNAKE_CASE__   :      List[str]				=		[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]

@pytest.mark.parametrize('''case''',	snake_case			)
def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   os.path.join(snake_case,	'''test_file.py'''			)
				with open(snake_case,	'''w'''			) as _tmp_file:
								_tmp_file.write(snake_case			)

				__magic_name__		:Optional[Any]   =   get_imports(snake_case			)
				assert parsed_imports == ["os"]














import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''ChineseCLIPImageProcessor'''
				a__       							=	('''BertTokenizer''', '''BertTokenizerFast''')







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''							,       __lowerCAmelCase							,       )
												__magic_name__		:Optional[Any]   =   kwargs.pop('''feature_extractor'''  )

								__magic_name__		:Tuple   =   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__(__lowerCAmelCase							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   self.image_processor







				def __call__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""

								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:
												__magic_name__		:int   =   self.tokenizer(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if images is not None:
												__magic_name__		:Dict   =   self.image_processor(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if text is not None and images is not None:
												__magic_name__		:Union[str, Any]   =   image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**__lowerCAmelCase  )							,       tensor_type=__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.tokenizer.model_input_names
								__magic_name__		:Any   =   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       __lowerCAmelCase							,       )
								return self.image_processor_class






