





"""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 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      UpperCAmelCase_  : Tuple				      =['image_processor', 'tokenizer']
      UpperCAmelCase_  : Tuple				      ='LayoutLMv2ImageProcessor'
      UpperCAmelCase_  : Optional[int]				      =('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')







      def __init__(	self			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  **UpperCAmelCase				)    ->				int:






        '''simple docstring'''






        __snake_case				:			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."			,  UpperCAmelCase			,  )
          __snake_case				:			Dict            =    kwargs.pop("feature_extractor"				)

        __snake_case				:			int            =    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__(UpperCAmelCase			,  UpperCAmelCase				)







      def __call__(	self			,  UpperCAmelCase			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = True			,  UpperCAmelCase = False			,  UpperCAmelCase = False			,  UpperCAmelCase = None			,  UpperCAmelCase = 0			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = False			,  UpperCAmelCase = False			,  UpperCAmelCase = False			,  UpperCAmelCase = False			,  UpperCAmelCase = True			,  UpperCAmelCase = None			,  **UpperCAmelCase			,  )    ->				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
        __snake_case				:			Any            =    self.image_processor(images=UpperCAmelCase			,  return_tensors=UpperCAmelCase				)

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

        __snake_case				:			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=UpperCAmelCase			,  add_special_tokens=UpperCAmelCase			,  padding=UpperCAmelCase			,  truncation=UpperCAmelCase			,  max_length=UpperCAmelCase			,  stride=UpperCAmelCase			,  pad_to_multiple_of=UpperCAmelCase			,  return_token_type_ids=UpperCAmelCase			,  return_attention_mask=UpperCAmelCase			,  return_overflowing_tokens=UpperCAmelCase			,  return_special_tokens_mask=UpperCAmelCase			,  return_offsets_mapping=UpperCAmelCase			,  return_length=UpperCAmelCase			,  verbose=UpperCAmelCase			,  return_tensors=UpperCAmelCase			,  **UpperCAmelCase			,  )

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

        return encoded_inputs







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Optional[Any]:






        '''simple docstring'''






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

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

        return images_with_overflow







      def   UpperCAmelCase						(	self			,  *UpperCAmelCase			,  **UpperCAmelCase				)    ->				int:






        '''simple docstring'''






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







      def   UpperCAmelCase						(	self			,  *UpperCAmelCase			,  **UpperCAmelCase				)    ->				Optional[int]:






        '''simple docstring'''






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







      @property
      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






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







      @property
      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






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







      @property
      def   UpperCAmelCase						(	self				)    ->				Dict:






        '''simple docstring'''






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








from __future__ import annotations

import sys




class 			_lowerCamelCase	:



      """simple docstring"""






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






        '''simple docstring'''






        __snake_case				:			str            =    letter
        __snake_case				:			int            =    freq
        __snake_case				:			dict[str, str]            =    {}







      def __repr__(	self				)    ->				str:






        '''simple docstring'''






        return F"""{self.letter}:{self.freq}"""




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			int            =    freq
        __snake_case				:			Letter | TreeNode            =    left
        __snake_case				:			Letter | TreeNode            =    right







def  lowerCAmelCase__(		lowercase    :      str			)	->      list[Letter]:
  __snake_case				:			dict[str, int]            =    {}
  with open(lowercase			) as f:
    while True:
      __snake_case				:			List[Any]            =    f.read(1			)
      if not c:
        break
      __snake_case				:			Optional[Any]            =    chars[c] + 1 if c in chars else 1
  return sorted((Letter(lowercase				,       lowercase			) for c, f in chars.items())				,       key=lambda lowercase			: x.freq			)







def  lowerCAmelCase__(		lowercase    :      list[Letter]			)	->      Letter | TreeNode:
  __snake_case				:			list[Letter | TreeNode]            =    letters  # type: ignore
  while len(lowercase			) > 1:
    __snake_case				:			Any            =    response.pop(0			)
    __snake_case				:			Optional[int]            =    response.pop(0			)
    __snake_case				:			str            =    left.freq + right.freq
    __snake_case				:			Dict            =    TreeNode(lowercase				,       lowercase				,       lowercase			)
    response.append(lowercase			)
    response.sort(key=lambda lowercase			: x.freq			)
  return response[0]







def  lowerCAmelCase__(		lowercase    :      Letter | TreeNode				,       lowercase    :      str			)	->      list[Letter]:
  if isinstance(lowercase				,       lowercase			):
    __snake_case				:			List[str]            =    bitstring
    return [root]
  __snake_case				:			TreeNode            =    root  # type: ignore
  __snake_case				:			Union[str, Any]            =    []
  letters += traverse_tree(treenode.left				,       bitstring + "0"			)
  letters += traverse_tree(treenode.right				,       bitstring + "1"			)
  return letters







def  lowerCAmelCase__(		lowercase    :      str			)	->      None:
  __snake_case				:			List[Any]            =    parse_file(lowercase			)
  __snake_case				:			List[str]            =    build_tree(lowercase			)
  __snake_case				:			Optional[int]            =    {
      k: v for letter in traverse_tree(lowercase				,       ""			) for k, v in letter.bitstring.items()
  }
  print(f"""Huffman Coding  of {file_path}: """			)
  with open(lowercase			) as f:
    while True:
      __snake_case				:			int            =    f.read(1			)
      if not c:
        break
      print(letters[c]				,       end=" "			)
  print()


if __name__ == "__main__":
     # pass the file path to the huffman function
     huffman(sys.argv[1])

