
"""simple docstring"""





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 A_			(lowercase__   ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__    :		List[Any]						  =	["""image_processor""", """tokenizer"""]
  SCREAMING_SNAKE_CASE__    :		Any						  =	"""LayoutLMv2ImageProcessor"""
  SCREAMING_SNAKE_CASE__    :		List[str]						  =	("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







  def __init__(	self       ,     lowercase_=None       ,     lowercase_=None       ,     **lowercase_						):

     """simple docstring"""
     UpperCAmelCase_      :      Optional[int]      =					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_       ,     )
        UpperCAmelCase_      :      Any      =					kwargs.pop("feature_extractor"						)

     UpperCAmelCase_      :      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__(lowercase_       ,     lowercase_						)







  def __call__(	self       ,     lowercase_       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = True       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = None       ,     lowercase_ = 0       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = None       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = False       ,     lowercase_ = True       ,     lowercase_ = None       ,     **lowercase_       ,     ):

     """simple docstring"""
     # verify input
     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
     UpperCAmelCase_      :      int      =					self.image_processor(images=lowercase_       ,     return_tensors=lowercase_						)

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

     UpperCAmelCase_      :      List[Any]      =					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=lowercase_       ,     add_special_tokens=lowercase_       ,     padding=lowercase_       ,     truncation=lowercase_       ,     max_length=lowercase_       ,     stride=lowercase_       ,     pad_to_multiple_of=lowercase_       ,     return_token_type_ids=lowercase_       ,     return_attention_mask=lowercase_       ,     return_overflowing_tokens=lowercase_       ,     return_special_tokens_mask=lowercase_       ,     return_offsets_mapping=lowercase_       ,     return_length=lowercase_       ,     verbose=lowercase_       ,     return_tensors=lowercase_       ,     **lowercase_       ,     )

     # add pixel values
     UpperCAmelCase_      :      List[str]      =					features.pop("pixel_values"						)
     if return_overflowing_tokens is True:
        UpperCAmelCase_      :      List[Any]      =					self.get_overflowing_images(lowercase_       ,     encoded_inputs["overflow_to_sample_mapping"]						)
     UpperCAmelCase_      :      Optional[Any]      =					images

     return encoded_inputs







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
     UpperCAmelCase_      :      Dict      =					[]
     for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx]						)

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

     return images_with_overflow







  def  UpperCamelCase__      (	self       ,     *lowercase_       ,     **lowercase_						):

     """simple docstring"""
     return self.tokenizer.batch_decode(*lowercase_       ,     **lowercase_						)







  def  UpperCamelCase__      (	self       ,     *lowercase_       ,     **lowercase_						):

     """simple docstring"""
     return self.tokenizer.decode(*lowercase_       ,     **lowercase_						)







  @property
  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







  @property
  def  UpperCamelCase__      (	self						):

     """simple docstring"""
     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						):

     """simple docstring"""
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,     lowercase_       ,     )
     return self.image_processor




"""simple docstring"""





from .hash_table import HashTable


class A_			(lowercase__   ):




  '''simple docstring'''







  def __init__(	self       ,     *lowercase_       ,     **lowercase_						):

     """simple docstring"""
     super().__init__(*lowercase_       ,     **lowercase_						)







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_=None						):

     """simple docstring"""
     UpperCAmelCase_      :      Tuple      =					1
     UpperCAmelCase_      :      List[str]      =					self.hash_function(key + i * i						)

     while self.values[new_key] is not None and self.values[new_key] != key:
        i += 1
        UpperCAmelCase_      :      List[Any]      =					(
            self.hash_function(key + i * i						)
            if not self.balanced_factor() >= self.lim_charge
            else None
        )

        if new_key is None:
           break

     return new_key


