





'''simple docstring'''





from __future__ import annotations

import sys


class 			__lowerCAmelCase				:


	'''simple docstring'''





	def __init__(	self:	Tuple,				lowerCamelCase_:	str,				lowerCamelCase_:	int ):
			lowercase__				:			str            =		letter
			lowercase__				:			int            =		freq
			lowercase__				:			dict[str, str]            =		{}





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


class 			__lowerCAmelCase				:


	'''simple docstring'''





	def __init__(	self:	int,				lowerCamelCase_:	int,				lowerCamelCase_:	Letter | TreeNode,				lowerCamelCase_:	Letter | TreeNode ):
			lowercase__				:			int            =		freq
			lowercase__				:			Letter | TreeNode            =		left
			lowercase__				:			Letter | TreeNode            =		right


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   str )      ->       list[Letter]:




		'''simple docstring'''
		lowercase__				:			dict[str, int]            =		{}
		with open(_lowercase ) as f:
				while True:
						lowercase__				:			Dict            =		f.read(1 )
						if not c:
								break
						lowercase__				:			Tuple            =		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 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   list[Letter] )      ->       Letter | TreeNode:




		'''simple docstring'''
		lowercase__				:			list[Letter | TreeNode]            =		letters  # type: ignore
		while len(_lowercase ) > 1:
				lowercase__				:			int            =		response.pop(0 )
				lowercase__				:			List[Any]            =		response.pop(0 )
				lowercase__				:			Optional[Any]            =		left.freq + right.freq
				lowercase__				:			Dict            =		TreeNode(_lowercase      ,						_lowercase      ,						_lowercase )
				response.append(_lowercase )
				response.sort(key=lambda _lowercase : x.freq )
		return response[0]


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Letter | TreeNode      ,						_lowercase    :   str )      ->       list[Letter]:




		'''simple docstring'''
		if isinstance(_lowercase      ,						_lowercase ):
				lowercase__				:			Optional[int]            =		bitstring
				return [root]
		lowercase__				:			TreeNode            =		root  # type: ignore
		lowercase__				:			Any            =		[]
		letters += traverse_tree(treenode.left      ,						bitstring + '0' )
		letters += traverse_tree(treenode.right      ,						bitstring + '1' )
		return letters


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   str )      ->       None:




		'''simple docstring'''
		lowercase__				:			int            =		parse_file(_lowercase )
		lowercase__				:			List[Any]            =		build_tree(_lowercase )
		lowercase__				:			Dict            =		{
		    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:
						lowercase__				:			Dict            =		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])









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase:  Dict   =   {
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  List[Any]   =   ["""DebertaV2TokenizerFast"""]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  str   =   [
			    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFDebertaV2ForMaskedLM""",
			    """TFDebertaV2ForQuestionAnswering""",
			    """TFDebertaV2ForSequenceClassification""",
			    """TFDebertaV2ForTokenClassification""",
			    """TFDebertaV2Model""",
			    """TFDebertaV2PreTrainedModel""",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   [
			    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DebertaV2ForMaskedLM""",
			    """DebertaV2ForMultipleChoice""",
			    """DebertaV2ForQuestionAnswering""",
			    """DebertaV2ForSequenceClassification""",
			    """DebertaV2ForTokenClassification""",
			    """DebertaV2Model""",
			    """DebertaV2PreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_deberta_va import (
			    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DebertaVaConfig,
			    DebertaVaOnnxConfig,
			)
			from .tokenization_deberta_va import DebertaVaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_deberta_va import (
						    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						    TFDebertaVaPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deberta_va import (
						    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DebertaVaForMaskedLM,
						    DebertaVaForMultipleChoice,
						    DebertaVaForQuestionAnswering,
						    DebertaVaForSequenceClassification,
						    DebertaVaForTokenClassification,
						    DebertaVaModel,
						    DebertaVaPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:  Optional[Any]   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


