





'''simple docstring'''

from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__lowerCAmelCase				:      Optional[Any]						      =TypeVar("KEY")
__lowerCAmelCase				:      Dict						      =TypeVar("VAL")






@dataclass(frozen=UpperCamelCase__     ,			slots=UpperCamelCase__							)
class   UpperCAmelCase      (			Generic[KEY, VAL]							):
 __lowercase  						=							42
 __lowercase  						=							42






class   UpperCAmelCase      (			_Item							):
 def __init__(							self :int				)->				None:
     super().__init__(lowercase_		,			lowercase_				)



 def __bool__(							self :Optional[Any]				)->				bool:
     return False

__lowerCAmelCase				:      int						      =_DeletedItem()






class   UpperCAmelCase      (			MutableMapping[KEY, VAL]							):
 def __init__(							self :str		,			lowercase_ :int = 8		,			lowercase_ :float = 0.7_5				)->				None:
     A__							=     initial_block_size
     A__							=     [None] * initial_block_size
     assert 0.0 < capacity_factor < 1.0
     A__							=     capacity_factor
     A__							=     0
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :KEY				)->				int:
     return hash(lowercase_				) % len(self._buckets				)
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :int				)->				int:
     return (ind + 1) % len(self._buckets				)
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :int		,			lowercase_ :KEY		,			lowercase_ :VAL				)->				bool:
     A__							=     self._buckets[ind]
     if not stored:
         A__							=     _Item(lowercase_		,			lowercase_				)
         self._len += 1
         return True
     elif stored.key == key:
         A__							=     _Item(lowercase_		,			lowercase_				)
         return True
     else:
         return False
 def 	UpperCAmelCase_				(							self :List[str]				)->				bool:
     A__							=     len(self._buckets				) * self._capacity_factor
     return len(self				) >= int(lowercase_				)
 def 	UpperCAmelCase_				(							self :List[str]				)->				bool:
     if len(self._buckets				) <= self._initial_block_size:
         return False
     A__							=     len(self._buckets				) * self._capacity_factor / 2
     return len(self				) < limit
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :int				)->				None:
     A__							=     self._buckets
     A__							=     [None] * new_size
     A__							=     0
     for item in old_buckets:
         if item:
             self._add_item(item.key		,			item.val				)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				None:
     self._resize(len(self._buckets				) * 2				)
 def 	UpperCAmelCase_				(							self :List[str]				)->				None:
     self._resize(len(self._buckets				) // 2				)
 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :KEY				)->				Iterator[int]:
     A__							=     self._get_bucket_index(lowercase_				)
     for _ in range(len(self._buckets				)				):
         yield ind
         A__							=     self._get_next_ind(lowercase_				)
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :KEY		,			lowercase_ :VAL				)->				None:
     for ind in self._iterate_buckets(lowercase_				):
         if self._try_set(lowercase_		,			lowercase_		,			lowercase_				):
             break
 def __setitem__(							self :Tuple		,			lowercase_ :KEY		,			lowercase_ :VAL				)->				None:
     if self._is_full():
         self._size_up()

     self._add_item(lowercase_		,			lowercase_				)
 def __delitem__(							self :Union[str, Any]		,			lowercase_ :KEY				)->				None:
     for ind in self._iterate_buckets(lowercase_				):
         A__							=     self._buckets[ind]
         if item is None:
             raise KeyError(lowercase_				)
         if item is _deleted:
             continue
         if item.key == key:
             A__							=     _deleted
             self._len -= 1
             break
     if self._is_sparse():
         self._size_down()
 def __getitem__(							self :Optional[int]		,			lowercase_ :KEY				)->				VAL:
     for ind in self._iterate_buckets(lowercase_				):
         A__							=     self._buckets[ind]
         if item is None:
             break
         if item is _deleted:
             continue
         if item.key == key:
             return item.val
     raise KeyError(lowercase_				)
 def __len__(							self :Tuple				)->				int:
     return self._len
 def __iter__(							self :Tuple				)->				Iterator[KEY]:
     yield from (item.key for item in self._buckets if item)



 def __repr__(							self :List[Any]				)->				str:
     A__							=     " ,".join(
         F"{item.key}: {item.val}" for item in self._buckets if item				)
     return F"HashMap({val_string})"










'''simple docstring'''

import warnings

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






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""image_processor""", """tokenizer"""]
 __lowercase  						=							"""CLIPImageProcessor"""
 __lowercase  						=							("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=None		,			lowercase_ :Any=None		,			**lowercase_ :Union[str, Any]				)->				Optional[Any]:
     A__							=     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_		,			)
         A__							=     kwargs.pop("feature_extractor"				)

     A__							=     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_				)
 def __call__(							self :Optional[Any]		,			lowercase_ :Optional[int]=None		,			lowercase_ :Dict=None		,			lowercase_ :List[Any]=None		,			**lowercase_ :Optional[Any]				)->				Union[str, Any]:

     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:
         A__							=     self.tokenizer(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if images is not None:
         A__							=     self.image_processor(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if text is not None and images is not None:
         A__							=     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 :int		,			*lowercase_ :Tuple		,			**lowercase_ :Any				)->				Dict:
     return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Any		,			*lowercase_ :Any		,			**lowercase_ :Any				)->				List[str]:
     return self.tokenizer.decode(*lowercase_		,			**lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.tokenizer.model_input_names
     A__							=     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,			lowercase_		,			)
     return self.image_processor_class



 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				List[Any]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,			lowercase_		,			)
     return self.image_processor



