




'''simple docstring'''





import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


a		:	Union[str, Any]       =      logging.get_logger(__name__)




@dataclass
class      UpperCamelCase       :
  a     :str
  a     :str
  a     :Optional[str]          =					None
  a     :Optional[str]          =					None



  def     a_							(			self ):







      '''simple docstring'''


      return json.dumps(dataclasses.asdict(self )		,     indent=2 ) + "\n"






@dataclass(frozen=snake_case__       )
class      UpperCamelCase       :
  a     :List[int]
  a     :Optional[List[int]]          =					None
  a     :Optional[List[int]]          =					None
  a     :Optional[Union[int, float]]          =					None



  def     a_							(			self ):







      '''simple docstring'''


      return json.dumps(dataclasses.asdict(self ) ) + "\n"






class      UpperCamelCase       :



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self ):







      '''simple docstring'''


      raise NotImplementedError()



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      if len(self.get_labels() ) > 1:
          _a				:							Optional[Any]							       =      self.get_labels()[int(example.label )]
      return example



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     __lowerCAmelCase=None ):







      '''simple docstring'''


      with open(__lowerCAmelCase		,     """r"""		,     encoding="""utf-8-sig""" ) as f:
          return list(csv.reader(__lowerCAmelCase		,     delimiter="""\t"""		,     quotechar=__lowerCAmelCase ) )






class      UpperCamelCase       (	snake_case__       ):



  def __init__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase="classification"		,     __lowerCAmelCase=False ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      [] if labels is None else labels
      _a				:							Any							       =      [] if examples is None else examples
      _a				:							Tuple							       =      mode
      _a				:							int							       =      verbose



  def __len__(			self ):







      '''simple docstring'''


      return len(self.examples )



  def __getitem__(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          return SingleSentenceClassificationProcessor(labels=self.labels		,     examples=self.examples[idx] )
      return self.examples[idx]



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     __lowerCAmelCase=""		,     __lowerCAmelCase=0		,     __lowerCAmelCase=1		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     **__lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      cls(**__lowerCAmelCase )
      processor.add_examples_from_csv(
          __lowerCAmelCase		,     split_name=__lowerCAmelCase		,     column_label=__lowerCAmelCase		,     column_text=__lowerCAmelCase		,     column_id=__lowerCAmelCase		,     skip_first_row=__lowerCAmelCase		,     overwrite_labels=__lowerCAmelCase		,     overwrite_examples=__lowerCAmelCase		,     )
      return processor



  @classmethod
  def     a_							(			cls		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[Any]							       =      cls(**__lowerCAmelCase )
      processor.add_examples(__lowerCAmelCase		,     labels=__lowerCAmelCase )
      return processor



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=""		,     __lowerCAmelCase=0		,     __lowerCAmelCase=1		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     ):







      '''simple docstring'''


      _a				:							Any							       =      self._read_tsv(__lowerCAmelCase )
      if skip_first_row:
          _a				:							Any							       =      lines[1:]
      _a				:							Tuple							       =      []
      _a				:							Optional[Any]							       =      []
      _a				:							str							       =      []
      for i, line in enumerate(__lowerCAmelCase ):
          texts.append(line[column_text] )
          labels.append(line[column_label] )
          if column_id is not None:
              ids.append(line[column_id] )
          else:
              _a				:							Union[str, Any]							       =      f'''{split_name}-{i}''' if split_name else str(__lowerCAmelCase )
              ids.append(__lowerCAmelCase )

      return self.add_examples(
          __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     overwrite_labels=__lowerCAmelCase		,     overwrite_examples=__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False ):







      '''simple docstring'''


      if labels is not None and len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
          raise ValueError(
              f'''Text and labels have mismatched lengths {len(__lowerCAmelCase )} and {len(__lowerCAmelCase )}''' )
      if ids is not None and len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
          raise ValueError(f'''Text and ids have mismatched lengths {len(__lowerCAmelCase )} and {len(__lowerCAmelCase )}''' )
      if ids is None:
          _a				:							Tuple							       =      [None] * len(__lowerCAmelCase )
      if labels is None:
          _a				:							Dict							       =      [None] * len(__lowerCAmelCase )
      _a				:							List[Any]							       =      []
      _a				:							List[Any]							       =      set()
      for text_or_text_and_label, label, guid in zip(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):
          if isinstance(__lowerCAmelCase		,     (tuple, list) ) and label is None:
              _a       , _a				:							str							       =      text_or_text_and_label
          else:
              _a				:							str							       =      text_or_text_and_label
          added_labels.add(__lowerCAmelCase )
          examples.append(InputExample(guid=__lowerCAmelCase		,     text_a=__lowerCAmelCase		,     text_b=__lowerCAmelCase		,     label=__lowerCAmelCase ) )

      # Update examples
      if overwrite_examples:
          _a				:							Optional[int]							       =      examples
      else:
          self.examples.extend(__lowerCAmelCase )

      # Update labels
      if overwrite_labels:
          _a				:							List[str]							       =      list(__lowerCAmelCase )
      else:
          _a				:							Union[str, Any]							       =      list(set(self.labels ).union(__lowerCAmelCase ) )

      return self.examples



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=0		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     ):







      '''simple docstring'''


      if max_length is None:
          _a				:							List[str]							       =      tokenizer.max_len

      _a				:							Optional[int]							       =      {label: i for i, label in enumerate(self.labels )}

      _a				:							List[Any]							       =      []
      for ex_index, example in enumerate(self.examples ):
          if ex_index % 1_0000 == 0:
              logger.info(f'''Tokenizing example {ex_index}''' )

          _a				:							int							       =      tokenizer.encode(
              example.text_a		,     add_special_tokens=__lowerCAmelCase		,     max_length=min(__lowerCAmelCase		,     tokenizer.max_len )		,     )
          all_input_ids.append(__lowerCAmelCase )

      _a				:							Union[str, Any]							       =      max(len(__lowerCAmelCase ) for input_ids in all_input_ids )

      _a				:							Optional[int]							       =      []
      for ex_index, (input_ids, example) in enumerate(zip(__lowerCAmelCase		,     self.examples ) ):
          if ex_index % 1_0000 == 0:
              logger.info(f'''Writing example {ex_index}/{len(self.examples )}''' )
          # The mask has 1 for real tokens and 0 for padding tokens. Only real
          # tokens are attended to.
          _a				:							Union[str, Any]							       =      [1 if mask_padding_with_zero else 0] * len(__lowerCAmelCase )

          # Zero-pad up to the sequence length.
          _a				:							Optional[Any]							       =      batch_length - len(__lowerCAmelCase )
          if pad_on_left:
              _a				:							Optional[Any]							       =      ([pad_token] * padding_length) + input_ids
              _a				:							Optional[Any]							       =      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
          else:
              _a				:							Union[str, Any]							       =      input_ids + ([pad_token] * padding_length)
              _a				:							List[Any]							       =      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

          if len(__lowerCAmelCase ) != batch_length:
              raise ValueError(f'''Error with input length {len(__lowerCAmelCase )} vs {batch_length}''' )
          if len(__lowerCAmelCase ) != batch_length:
              raise ValueError(f'''Error with input length {len(__lowerCAmelCase )} vs {batch_length}''' )

          if self.mode == "classification":
              _a				:							str							       =      label_map[example.label]
          elif self.mode == "regression":
              _a				:							int							       =      float(example.label )
          else:
              raise ValueError(self.mode )

          if ex_index < 5 and self.verbose:
              logger.info("""*** Example ***""" )
              logger.info(f'''guid: {example.guid}''' )
              logger.info(f'''input_ids: {' '.join([str(__lowerCAmelCase ) for x in input_ids] )}''' )
              logger.info(f'''attention_mask: {' '.join([str(__lowerCAmelCase ) for x in attention_mask] )}''' )
              logger.info(f'''label: {example.label} (id = {label})''' )

          features.append(InputFeatures(input_ids=__lowerCAmelCase		,     attention_mask=__lowerCAmelCase		,     label=__lowerCAmelCase ) )

      if return_tensors is None:
          return features
      elif return_tensors == "tf":
          if not is_tf_available():
              raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported""" )
          import tensorflow as tf

          def gen():
              for ex in features:
                  yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

          _a				:							Any							       =      tf.data.Dataset.from_generator(
              __lowerCAmelCase		,     ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)		,     ({"""input_ids""": tf.TensorShape([None] ), """attention_mask""": tf.TensorShape([None] )}, tf.TensorShape([] ))		,     )
          return dataset
      elif return_tensors == "pt":
          if not is_torch_available():
              raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported""" )
          import torch
          from torch.utils.data import TensorDataset

          _a				:							str							       =      torch.tensor([f.input_ids for f in features]		,     dtype=torch.long )
          _a				:							Any							       =      torch.tensor([f.attention_mask for f in features]		,     dtype=torch.long )
          if self.mode == "classification":
              _a				:							Union[str, Any]							       =      torch.tensor([f.label for f in features]		,     dtype=torch.long )
          elif self.mode == "regression":
              _a				:							List[str]							       =      torch.tensor([f.label for f in features]		,     dtype=torch.float )

          _a				:							Any							       =      TensorDataset(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
          return dataset
      else:
          raise ValueError("""return_tensors should be one of 'tf' or 'pt'""" )








'''simple docstring'''





from __future__ import annotations

import sys




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


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



  def __repr__(			self ):







      '''simple docstring'''


      return f'''{self.letter}:{self.freq}'''






class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


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






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





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							list[Letter | TreeNode]							       =      letters  # type: ignore
    while len(UpperCAmelCase_						) > 1:
        _a				:							List[Any]							       =      response.pop(0						)
        _a				:							List[str]							       =      response.pop(0						)
        _a				:							Dict							       =      left.freq + right.freq
        _a				:							Any							       =      TreeNode(UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						)
        response.append(UpperCAmelCase_						)
        response.sort(key=lambda UpperCAmelCase_						: x.freq						)
    return response[0]





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    if isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
        _a				:							int							       =      bitstring
        return [root]
    _a				:							TreeNode							       =      root  # type: ignore
    _a				:							Optional[Any]							       =      []
    letters += traverse_tree(treenode.left   ,	bitstring + """0"""						)
    letters += traverse_tree(treenode.right   ,	bitstring + """1"""						)
    return letters





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Optional[Any]							       =      parse_file(UpperCAmelCase_						)
    _a				:							Tuple							       =      build_tree(UpperCAmelCase_						)
    _a				:							Union[str, Any]							       =      {
        k: v for letter in traverse_tree(UpperCAmelCase_   ,	""""""						) for k, v in letter.bitstring.items()
    }
    print(F'''Huffman Coding  of {file_path}: '''						)
    with open(UpperCAmelCase_						) as f:
        while True:
            _a				:							Any							       =      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])


