from __future__ import annotations

import sys



class    lowercase_    :



							def __init__(						self      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     Union[str, Any]				=    letter
													_snake_case						:     Union[str, Any]				=    freq
													_snake_case						:     List[Any]				=    {}



							def __repr__(						self				):
													return f"""{self.letter}:{self.freq}"""




class    lowercase_    :



							def __init__(						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     Any				=    freq
													_snake_case						:     Union[str, Any]				=    left
													_snake_case						:     List[Any]				=    right




def 					snake_case						(__lowercase						)       ->       list[Letter]:

						'''simple docstring'''



						_snake_case						:     str				=    {}
						with open(__lowercase						) as f:
												while True:
																		_snake_case						:     List[str]				=    f.read(1						)
																		if not c:
																								break
																		_snake_case						:     Union[str, 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 					snake_case						(__lowercase						)       ->       Letter | TreeNode:

						'''simple docstring'''



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

						'''simple docstring'''



						if isinstance(__lowercase					,			__lowercase						):
												_snake_case						:     Union[str, Any]				=    bitstring
												return [root]
						_snake_case						:     List[Any]				=    root  # type: ignore
						_snake_case						:     Tuple				=    []
						letters += traverse_tree(treenode.left					,			bitstring + "0"						)
						letters += traverse_tree(treenode.right					,			bitstring + "1"						)
						return letters
def 					snake_case						(__lowercase						)       ->       None:

						'''simple docstring'''



						_snake_case						:     List[str]				=    parse_file(__lowercase						)
						_snake_case						:     int				=    build_tree(__lowercase						)
						_snake_case						:     Optional[Any]				=    {
						    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						:     List[str]				=    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])



"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a_											=			None


a_											=			logging.get_logger(__name__)


a_											=			"▁"

a_											=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=			{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

a_											=			{
    "google/reformer-crime-and-punishment": 52_42_88,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	ReformerTokenizer







		def __init__(    self					:				Dict			,      a__					:				List[Any]=None			,      a__					:				int=None			,      a__					:				int="</s>"			,      a__					:				List[Any]="<unk>"			,      a__					:				Optional[Any]=[]			,      **a__					:				Tuple			,      )				->  Tuple:





						'''simple docstring'''







						super().__init__(
						    a__			,      tokenizer_file=a__			,      eos_token=a__			,      unk_token=a__			,      additional_special_tokens=a__			,      **a__			,      )

						_A         =	vocab_file
						_A         =	False if not self.vocab_file else True







		def        a_       (    self					:				Any			,      a__					:				str			,      a__					:				Optional[str] = None						)				->  Tuple[str]:





						'''simple docstring'''







						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."						)

						if not os.path.isdir(a__						):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
										return
						_A         =	os.path.join(
						    a__			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(a__						):
										copyfile(self.vocab_file			,      a__						)

						return (out_vocab_file,)