





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Dict			 =	['image_processor', 'tokenizer']
   _UpperCamelCase   :		Optional[Any]			 =	'LayoutLMv2ImageProcessor'
   _UpperCamelCase   :		int			 =	('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')


   def __init__(   self      :    Any      ,					a      :    Dict=None      ,					a      :    Optional[int]=None      ,					**a      :    List[Any]	)->       List[str]:





     """simple docstring"""







     if "feature_extractor" in kwargs:
       warnings.warn(
           'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
           ' instead.'      ,					a      ,					)
       lowercase__           =  kwargs.pop('feature_extractor'	)

     lowercase__           =  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__(a      ,					a	)


   def __call__(   self      :    int      ,					a      :    Optional[Any]      ,					a      :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,					a      :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,					a      :    Union[List[List[int]], List[List[List[int]]]] = None      ,					a      :    Optional[Union[List[int], List[List[int]]]] = None      ,					a      :    bool = True      ,					a      :    Union[bool, str, PaddingStrategy] = False      ,					a      :    Union[bool, str, TruncationStrategy] = None      ,					a      :    Optional[int] = None      ,					a      :    int = 0      ,					a      :    Optional[int] = None      ,					a      :    Optional[bool] = None      ,					a      :    Optional[bool] = None      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    bool = True      ,					a      :    Optional[Union[str, TensorType]] = None      ,					**a      :    Any      ,					)->       BatchEncoding:





     """simple docstring"""







     if self.image_processor.apply_ocr and (boxes is not None):
       raise ValueError(
           'You cannot provide bounding boxes '
           'if you initialized the image processor with apply_ocr set to True.'	)

     if self.image_processor.apply_ocr and (word_labels is not None):
       raise ValueError(
           'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'	)

     if return_overflowing_tokens is True and return_offsets_mapping is False:
       raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'	)

     # first, apply the image processor
     lowercase__           =  self.image_processor(images=a      ,					return_tensors=a	)

     # second, apply the tokenizer
     if text is not None and self.image_processor.apply_ocr and text_pair is None:
       if isinstance(a      ,					a	):
         lowercase__           =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
       lowercase__           =  features['words']

     lowercase__           =  self.tokenizer(
         text=text if text is not None else features['words']      ,					text_pair=text_pair if text_pair is not None else None      ,					boxes=boxes if boxes is not None else features['boxes']      ,					word_labels=a      ,					add_special_tokens=a      ,					padding=a      ,					truncation=a      ,					max_length=a      ,					stride=a      ,					pad_to_multiple_of=a      ,					return_token_type_ids=a      ,					return_attention_mask=a      ,					return_overflowing_tokens=a      ,					return_special_tokens_mask=a      ,					return_offsets_mapping=a      ,					return_length=a      ,					verbose=a      ,					return_tensors=a      ,					**a      ,					)

     # add pixel values
     lowercase__           =  features.pop('pixel_values'	)
     if return_overflowing_tokens is True:
       lowercase__           =  self.get_overflowing_images(a      ,					encoded_inputs['overflow_to_sample_mapping']	)
     lowercase__           =  images

     return encoded_inputs


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    int      ,					a      :    List[Any]	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  []
     for sample_idx in overflow_to_sample_mapping:
       images_with_overflow.append(images[sample_idx]	)

     if len(a	) != len(a	):
       raise ValueError(
           'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
           f""" {len(a	)} and {len(a	)}"""	)

     return images_with_overflow


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					*a      :    List[str]      ,					**a      :    Optional[int]	)->       int:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					*a      :    Optional[int]      ,					**a      :    Optional[int]	)->       Dict:





     """simple docstring"""







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


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Optional[int]:





     """simple docstring"""







     return ["input_ids", "bbox", "attention_mask", "image"]


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





     """simple docstring"""







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


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Dict:





     """simple docstring"""







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









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

lowercase_          =		TypeVar("""KEY""")
lowercase_          =		TypeVar("""VAL""")
@dataclass(frozen=UpperCAmelCase      ,     slots=UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (Generic[KEY, VAL]				):
   _UpperCamelCase   :		KEY
   _UpperCamelCase   :		VAL






class     SCREAMING_SNAKE_CASE       (_Item				):


   def __init__(   self      :    Optional[int]	)->       None:





     """simple docstring"""







     super().__init__(a      ,					a	)


   def __bool__(   self      :    str	)->       bool:





     """simple docstring"""







     return False






lowercase_          =		_DeletedItem()
class     SCREAMING_SNAKE_CASE       (MutableMapping[KEY, VAL]				):


   def __init__(   self      :    Tuple      ,					a      :    int = 8      ,					a      :    float = 0.75	)->       None:





     """simple docstring"""







     lowercase__           =  initial_block_size
     lowercase__           =  [None] * initial_block_size
     assert 0.0 < capacity_factor < 1.0
     lowercase__           =  capacity_factor
     lowercase__           =  0


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    KEY	)->       int:





     """simple docstring"""







     return hash(a	) % len(self._buckets	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    int	)->       int:





     """simple docstring"""







     return (ind + 1) % len(self._buckets	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    int      ,					a      :    KEY      ,					a      :    VAL	)->       bool:





     """simple docstring"""







     lowercase__           =  self._buckets[ind]
     if not stored:
       lowercase__           =  _Item(a      ,					a	)
       self._len += 1
       return True
     elif stored.key == key:
       lowercase__           =  _Item(a      ,					a	)
       return True
     else:
       return False


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       bool:





     """simple docstring"""







     lowercase__           =  len(self._buckets	) * self._capacity_factor
     return len(self	) >= int(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       bool:





     """simple docstring"""







     if len(self._buckets	) <= self._initial_block_size:
       return False
     lowercase__           =  len(self._buckets	) * self._capacity_factor / 2
     return len(self	) < limit


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    int	)->       None:





     """simple docstring"""







     lowercase__           =  self._buckets
     lowercase__           =  [None] * new_size
     lowercase__           =  0
     for item in old_buckets:
       if item:
         self._add_item(item.key      ,					item.val	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       None:





     """simple docstring"""







     self._resize(len(self._buckets	) * 2	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       None:





     """simple docstring"""







     self._resize(len(self._buckets	) // 2	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    KEY	)->       Iterator[int]:





     """simple docstring"""







     lowercase__           =  self._get_bucket_index(a	)
     for _ in range(len(self._buckets	)	):
       yield ind
       lowercase__           =  self._get_next_ind(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    KEY      ,					a      :    VAL	)->       None:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       if self._try_set(a      ,					a      ,					a	):
         break


   def __setitem__(   self      :    List[Any]      ,					a      :    KEY      ,					a      :    VAL	)->       None:





     """simple docstring"""







     if self._is_full():
       self._size_up()

     self._add_item(a      ,					a	)


   def __delitem__(   self      :    str      ,					a      :    KEY	)->       None:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       lowercase__           =  self._buckets[ind]
       if item is None:
         raise KeyError(a	)
       if item is _deleted:
         continue
       if item.key == key:
         lowercase__           =  _deleted
         self._len -= 1
         break
     if self._is_sparse():
       self._size_down()


   def __getitem__(   self      :    List[str]      ,					a      :    KEY	)->       VAL:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       lowercase__           =  self._buckets[ind]
       if item is None:
         break
       if item is _deleted:
         continue
       if item.key == key:
         return item.val
     raise KeyError(a	)


   def __len__(   self      :    Tuple	)->       int:





     """simple docstring"""







     return self._len


   def __iter__(   self      :    int	)->       Iterator[KEY]:





     """simple docstring"""







     yield from (item.key for item in self._buckets if item)


   def __repr__(   self      :    Union[str, Any]	)->       str:





     """simple docstring"""







     lowercase__           =  ' ,'.join(
         f"""{item.key}: {item.val}""" for item in self._buckets if item	)
     return f"""HashMap({val_string})"""


