





import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	str			     = ['''image_processor''', '''tokenizer''']
			lowerCamelCase_	:	Dict			     = '''FlavaImageProcessor'''
			lowerCamelCase_	:	List[str]			     = ('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(self   ,				__magic_name__=None   ,				__magic_name__=None   ,				**__magic_name__       )	->  List[Any]:


							'''simple docstring'''



							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.'''   ,				__magic_name__   ,				)
											snake_case_		:       Optional[Any]           =    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 need to specify an `image_processor`.'''       )
							if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''       )

							super().__init__(__magic_name__   ,				__magic_name__       )
							snake_case_		:       Tuple           =    self.image_processor






			def __call__(self   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = True   ,				__magic_name__ = False   ,				__magic_name__ = False   ,				__magic_name__ = None   ,				__magic_name__ = 0   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = False   ,				__magic_name__ = False   ,				__magic_name__ = False   ,				__magic_name__ = False   ,				__magic_name__ = True   ,				__magic_name__ = None   ,				**__magic_name__   ,				)	->  Dict:


							'''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:
											snake_case_		:       Optional[int]           =    self.tokenizer(
											    text=__magic_name__   ,				add_special_tokens=__magic_name__   ,				padding=__magic_name__   ,				truncation=__magic_name__   ,				max_length=__magic_name__   ,				stride=__magic_name__   ,				pad_to_multiple_of=__magic_name__   ,				return_token_type_ids=__magic_name__   ,				return_attention_mask=__magic_name__   ,				return_overflowing_tokens=__magic_name__   ,				return_special_tokens_mask=__magic_name__   ,				return_offsets_mapping=__magic_name__   ,				return_length=__magic_name__   ,				verbose=__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__   ,				)
							if images is not None:
											snake_case_		:       Any           =    self.image_processor(
											    __magic_name__   ,				return_image_mask=__magic_name__   ,				return_codebook_pixels=__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__   ,				)

							if text is not None and images is not None:
											encoding.update(__magic_name__       )
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**__magic_name__       )   ,				tensor_type=__magic_name__       )






			def      lowerCamelCase    (self   ,				*__magic_name__   ,				**__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							return self.tokenizer.batch_decode(*__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				*__magic_name__   ,				**__magic_name__       )	->  List[Any]:


							'''simple docstring'''



							return self.tokenizer.decode(*__magic_name__   ,				**__magic_name__       )






			@property
			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							snake_case_		:       Tuple           =    self.tokenizer.model_input_names
							snake_case_		:       List[Any]           =    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )






			@property
			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,				__magic_name__   ,				)
							return self.image_processor_class






			@property
			def      lowerCamelCase    (self       )	->  List[str]:


							'''simple docstring'''



							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,				__magic_name__   ,				)
							return self.image_processor







from __future__ import annotations

from cmath import sqrt

def   __snake_case    (       _lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int						)			->    tuple[complex, complex]:

   if a == 0:
      raise ValueError("Coefficient 'a' must not be zero."						)
   A_       :					Tuple        =      b * b - 4 * a * c

   A_       :					Any        =      (-b + sqrt(_lowerCAmelCase						)) / (2 * a)
   A_       :					Union[str, Any]        =      (-b - sqrt(_lowerCAmelCase						)) / (2 * a)

   return (
       root_a.real if not root_a.imag else root_a,
       root_a.real if not root_a.imag else root_a,
   )

def   __snake_case    (       )			->    List[Any]:
   A_   ,    A_       :					Optional[Any]        =      quadratic_roots(a=5					,					b=6					,					c=1						)
   print(f"The solutions are: {solutiona} and {solutiona}"						)


if __name__ == "__main__":
 main()
