





import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase    =	get_tests_dir('''fixtures/spiece.model''')




@require_sentencepiece
@require_tokenizers
class 			_lowerCamelCase	(      a	,				unittest.TestCase      ):



      """simple docstring"""






      UpperCAmelCase_  : Optional[int]				      =ErnieMTokenizer
      UpperCAmelCase_  : Tuple				      =False
      UpperCAmelCase_  : str				      =True
      UpperCAmelCase_  : str				      =False
      UpperCAmelCase_  : List[str]				      =False







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






        '''simple docstring'''






        super().setUp()

        # We have a SentencePiece fixture for testing
        __snake_case				:			Any            =    ErnieMTokenizer(UpperCAmelCase			,  unk_token="<unk>"			,  pad_token="<pad>"				)
        tokenizer.save_pretrained(self.tmpdirname				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				List[str]:






        '''simple docstring'''






        __snake_case				:			Any            =    "this is a test"
        __snake_case				:			int            =    "this is a test"
        return input_text, output_text







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






        '''simple docstring'''






        __snake_case				:			str            =    "<pad>"
        __snake_case				:			List[str]            =    0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase				)			,  UpperCAmelCase				)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase				)			,  UpperCAmelCase				)







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






        '''simple docstring'''






        __snake_case				:			Any            =    list(self.get_tokenizer().get_vocab().keys()				)

        self.assertEqual(vocab_keys[0]			,  "<pad>"				)
        self.assertEqual(vocab_keys[1]			,  "<unk>"				)
        self.assertEqual(vocab_keys[-1]			,  "▁eloquent"				)
        self.assertEqual(len(UpperCAmelCase				)			,  30000				)







      def   UpperCAmelCase						(	self				)    ->				List[str]:






        '''simple docstring'''






        self.assertEqual(self.get_tokenizer().vocab_size			,  30000				)







      def   UpperCAmelCase						(	self				)    ->				List[str]:






        '''simple docstring'''






        if not self.test_rust_tokenizer:
          return

        __snake_case				:			List[Any]            =    self.get_tokenizer()
        __snake_case				:			Union[str, Any]            =    self.get_rust_tokenizer()

        __snake_case				:			Optional[int]            =    "I was born in 92000, and this is falsé."

        __snake_case				:			str            =    tokenizer.tokenize(UpperCAmelCase				)
        __snake_case				:			int            =    rust_tokenizer.tokenize(UpperCAmelCase				)
        self.assertListEqual(UpperCAmelCase			,  UpperCAmelCase				)

        __snake_case				:			List[Any]            =    tokenizer.encode(UpperCAmelCase			,  add_special_tokens=UpperCAmelCase				)
        __snake_case				:			Tuple            =    rust_tokenizer.encode(UpperCAmelCase			,  add_special_tokens=UpperCAmelCase				)
        self.assertListEqual(UpperCAmelCase			,  UpperCAmelCase				)

        __snake_case				:			Optional[int]            =    self.get_rust_tokenizer()
        __snake_case				:			List[Any]            =    tokenizer.encode(UpperCAmelCase				)
        __snake_case				:			List[Any]            =    rust_tokenizer.encode(UpperCAmelCase				)
        self.assertListEqual(UpperCAmelCase			,  UpperCAmelCase				)







      def   UpperCAmelCase						(	self				)    ->				int:






        '''simple docstring'''






        __snake_case				:			Tuple            =    ErnieMTokenizer(UpperCAmelCase			,  do_lower_case=UpperCAmelCase			,  unk_token="<unk>"			,  pad_token="<pad>"				)

        __snake_case				:			List[Any]            =    tokenizer.tokenize("This is a test"				)
        self.assertListEqual(UpperCAmelCase			,  ["▁this", "▁is", "▁a", "▁test"]				)

        self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase				)			,  [48, 25, 21, 1289]				)

        __snake_case				:			Optional[Any]            =    tokenizer.tokenize("I was born in 92000, and this is falsé."				)
        # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
        self.assertListEqual(
            UpperCAmelCase			,  ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]				)
        __snake_case				:			Any            =    tokenizer.convert_tokens_to_ids(UpperCAmelCase				)
        self.assertListEqual(UpperCAmelCase			,  [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]				)

        __snake_case				:			Any            =    tokenizer.convert_ids_to_tokens(UpperCAmelCase				)
        self.assertListEqual(
            UpperCAmelCase			,  ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]			,  )







      def   UpperCAmelCase						(	self				)    ->				List[Any]:






        '''simple docstring'''






        __snake_case				:			List[Any]            =    ErnieMTokenizer(UpperCAmelCase			,  unk_token="<unk>"			,  pad_token="<pad>"				)

        __snake_case				:			Optional[int]            =    tokenizer.encode("sequence builders"				)
        __snake_case				:			str            =    tokenizer.encode("multi-sequence build"				)

        __snake_case				:			List[str]            =    tokenizer.build_inputs_with_special_tokens(UpperCAmelCase				)
        __snake_case				:			Any            =    tokenizer.build_inputs_with_special_tokens(UpperCAmelCase			,  UpperCAmelCase				)

        assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
        assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
            tokenizer.sep_token_id
        ] + text_a + [tokenizer.sep_token_id]







      @slow
      def   UpperCAmelCase						(	self				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    {"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=UpperCAmelCase			,  model_name="susnato/ernie-m-base_pytorch"			,  sequences=[
                "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
                "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
                "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
                "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
                "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
                "conditioning on both left and right context in all layers.",
                "The quick brown fox jumps over the lazy dog.",
            ]			,  )








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])

