


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      List[str]      =		['''image_processor''', '''tokenizer''']
				SCREAMING_SNAKE_CASE__    :      Tuple      =		'''AutoImageProcessor'''
				SCREAMING_SNAKE_CASE__    :      int      =		'''AutoTokenizer'''


				def __init__(	self      :Optional[Any]      ,      lowerCAmelCase__      :Any=None      ,      lowerCAmelCase__      :int=None      ,      **lowerCAmelCase__      :Union[str, Any]     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		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__      ,      )
																__SCREAMING_SNAKE_CASE						:					Dict		      =		kwargs.pop('''feature_extractor'''     )

										__SCREAMING_SNAKE_CASE						:					str		      =		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__(lowerCAmelCase__      ,      lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.image_processor


				def __call__(	self      :Tuple      ,      lowerCAmelCase__      :Any=None      ,      lowerCAmelCase__      :Union[str, Any]=None      ,      lowerCAmelCase__      :Tuple=None      ,      **lowerCAmelCase__      :int     )		->      str:

										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:
																__SCREAMING_SNAKE_CASE						:					str		      =		self.tokenizer(lowerCAmelCase__      ,      return_tensors=lowerCAmelCase__      ,      **lowerCAmelCase__     )

										if images is not None:
																__SCREAMING_SNAKE_CASE						:					str		      =		self.image_processor(lowerCAmelCase__      ,      return_tensors=lowerCAmelCase__      ,      **lowerCAmelCase__     )

										if text is not None and images is not None:
																__SCREAMING_SNAKE_CASE						:					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 		__magic_name__(	self      :List[str]      ,      *lowerCAmelCase__      :List[Any]      ,      **lowerCAmelCase__      :Any     )		->      List[Any]:
										return self.tokenizer.batch_decode(*lowerCAmelCase__      ,      **lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[Any]      ,      *lowerCAmelCase__      :Any      ,      **lowerCAmelCase__      :Tuple     )		->      Tuple:
										return self.tokenizer.decode(*lowerCAmelCase__      ,      **lowerCAmelCase__     )


				@property
				def 		__magic_name__(	self      :List[str]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					str		      =		self.tokenizer.model_input_names
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )


				@property
				def 		__magic_name__(	self      :Dict     )		->      List[Any]:
										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''      ,      lowerCAmelCase__      ,      )
										return self.image_processor_class



				@property
				def 		__magic_name__(	self      :Union[str, Any]     )		->      Optional[int]:
										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''      ,      lowerCAmelCase__      ,      )
										return self.image_processor




from .number_theory.prime_numbers import next_prime
class 	_lowercase				:






				'''simple docstring'''



				def __init__(	self      :Tuple      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :int | None = None      ,      lowerCAmelCase__      :float | None = None      ,      )		->      None:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		size_table
										__SCREAMING_SNAKE_CASE						:					Dict		      =		[None] * self.size_table
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		0.75 if lim_charge is None else lim_charge
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		1 if charge_factor is None else charge_factor
										__SCREAMING_SNAKE_CASE						:					list		      =		[]
										__SCREAMING_SNAKE_CASE						:					dict		      =		{}


				def 		__magic_name__(	self      :List[Any]     )		->      Tuple:
										return self._keys


				def 		__magic_name__(	self      :Any     )		->      Dict:
										return sum(1 for slot in self.values if slot is not None     ) / (
										    self.size_table * self.charge_factor
										)


				def 		__magic_name__(	self      :Optional[Any]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      Optional[Any]:
										return key % self.size_table


				def 		__magic_name__(	self      :int      ,      lowerCAmelCase__      :Union[str, Any]     )		->      List[Any]:
										print(f'''step {step_ord}'''     )
										print(list(range(len(self.values     )     )     )     )
										print(self.values     )


				def 		__magic_name__(	self      :List[str]      ,      lowerCAmelCase__      :Optional[Any]     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		1
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		values
										for value in values:
																self.insert_data(lowerCAmelCase__     )
																self._step_by_step(lowerCAmelCase__     )
																i += 1


				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		data
										__SCREAMING_SNAKE_CASE						:					Any		      =		data


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :List[str]=None     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.hash_function(key + 1     )

										while self.values[new_key] is not None and self.values[new_key] != key:
																if self.values.count(lowerCAmelCase__     ) > 0:
																						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.hash_function(new_key + 1     )
																else:
																						__SCREAMING_SNAKE_CASE						:					int		      =		None
																						break

										return new_key


				def 		__magic_name__(	self      :Optional[int]     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					str		      =		[value for value in self.values if value is not None]
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		next_prime(self.size_table      ,      factor=2     )
										self._keys.clear()
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		[None] * self.size_table  # hell's pointers D: don't DRY ;/
										for value in survivor_values:
																self.insert_data(lowerCAmelCase__     )



				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :str     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.hash_function(lowerCAmelCase__     )

										if self.values[key] is None:
																self._set_value(lowerCAmelCase__      ,      lowerCAmelCase__     )

										elif self.values[key] == data:
																pass

										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		self._collision_resolution(lowerCAmelCase__      ,      lowerCAmelCase__     )
																if collision_resolution is not None:
																						self._set_value(lowerCAmelCase__      ,      lowerCAmelCase__     )
																else:
																						self.rehashing()
																						self.insert_data(lowerCAmelCase__     )
