


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__							(		UpperCamelCase__  ):
      UpperCAmelCase             =						["image_processor", "tokenizer"]
      UpperCAmelCase             =						"LayoutLMv3ImageProcessor"
      UpperCAmelCase             =						("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")




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


          """simple docstring"""





          _SCREAMING_SNAKE_CASE			  =None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.'''				,    _a				,    )
              _SCREAMING_SNAKE_CASE			  =kwargs.pop('''feature_extractor'''  )

          _SCREAMING_SNAKE_CASE			  =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					:      Optional[int]				,    _a					:      List[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					:      Dict				,    )							->     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.'''  )

          # first, apply the image processor
          _SCREAMING_SNAKE_CASE			  =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  ):
                  _SCREAMING_SNAKE_CASE			  =[text]  # add batch dimension (as the image processor always adds a batch dimension)
              _SCREAMING_SNAKE_CASE			  =features['''words''']

          _SCREAMING_SNAKE_CASE			  =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
          _SCREAMING_SNAKE_CASE			  =features.pop('''pixel_values'''  )
          if return_overflowing_tokens is True:
              _SCREAMING_SNAKE_CASE			  =self.get_overflowing_images(_a				,    encoded_inputs['''overflow_to_sample_mapping''']  )
          _SCREAMING_SNAKE_CASE			  =images

          return encoded_inputs




      def 							__UpperCamelCase				(       self					:      Optional[int]				,    _a					:      Optional[Any]				,    _a					:      Tuple  )							->     Union[str, Any]:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE			  =[]
          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 							__UpperCamelCase				(       self					:      int				,    *_a					:      Any				,    **_a					:      Dict  )							->     List[str]:


          """simple docstring"""





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




      def 							__UpperCamelCase				(       self					:      Union[str, Any]				,    *_a					:      Optional[int]				,    **_a					:      Union[str, Any]  )							->     str:


          """simple docstring"""





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




      @property
      def 							__UpperCamelCase				(       self					:      Any  )							->     str:


          """simple docstring"""





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




      @property
      def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Optional[Any]:


          """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 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Union[str, Any]:


          """simple docstring"""





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



import string
from math import logaa







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =document.translate(
				    str.maketrans(''''''	,''''''	,string.punctuation)).replace('''\n'''	,'''''')
				_SCREAMING_SNAKE_CASE			  =document_without_punctuation.split(''' ''')  # word tokenization
				return len([word for word in tokenize_document if word.lower() == term.lower()])







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =corpus.lower().translate(
				    str.maketrans(''''''	,''''''	,string.punctuation))  # strip all punctuation and replace it with ''
				_SCREAMING_SNAKE_CASE			  =corpus_without_punctuation.split('''\n''')
				_SCREAMING_SNAKE_CASE			  =term.lower()
				return (len([doc for doc in docs if term in doc]), len(a__))







def 						lowerCamelCase(   a__	,a__	,a__=False):
				if smoothing:
								if n == 0:
												raise ValueError('''log10(0) is undefined.''')
								return round(1 + logaa(n / (1 + df))	,3)

				if df == 0:
								raise ZeroDivisionError('''df must be > 0''')
				elif n == 0:
								raise ValueError('''log10(0) is undefined.''')
				return round(logaa(n / df)	,3)







def 						lowerCamelCase(   a__	,a__):
				return round(tf * idf	,3)