
from __future__ import annotations

import sys
class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      int	)	->					Dict:
					lowercase_					  = letter
					lowercase_					  = freq
					lowercase_					  = {}




	def __repr__(	self  :      Union[str, Any]	)	->					str:
					return f'''{self.letter}:{self.freq}'''

class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      Letter | TreeNode	,							SCREAMING_SNAKE_CASE_  :      Letter | TreeNode	)	->					Any:
					lowercase_					  = freq
					lowercase_					  = left
					lowercase_					  = right




def     a						(							snake_case__: str ):


				'''simple docstring'''
				lowercase_					  = {}
				with open(snake_case__ ) as f:
								while True:
												lowercase_					  = f.read(1 )
												if not c:
																break
												lowercase_					  = chars[c] + 1 if c in chars else 1
				return sorted((Letter(snake_case__						,       snake_case__ ) for c, f in chars.items())						,       key=lambda snake_case__ : x.freq )




def     a						(							snake_case__: list[Letter] ):


				'''simple docstring'''
				lowercase_					  = letters  # type: ignore
				while len(snake_case__ ) > 1:
								lowercase_					  = response.pop(0 )
								lowercase_					  = response.pop(0 )
								lowercase_					  = left.freq + right.freq
								lowercase_					  = TreeNode(snake_case__						,       snake_case__						,       snake_case__ )
								response.append(snake_case__ )
								response.sort(key=lambda snake_case__ : x.freq )
				return response[0]




def     a						(							snake_case__: Letter | TreeNode						,       snake_case__: str ):


				'''simple docstring'''
				if isinstance(snake_case__						,       snake_case__ ):
								lowercase_					  = bitstring
								return [root]
				lowercase_					  = root  # type: ignore
				lowercase_					  = []
				letters += traverse_tree(treenode.left						,       bitstring + '''0''' )
				letters += traverse_tree(treenode.right						,       bitstring + '''1''' )
				return letters




def     a						(							snake_case__: str ):


				'''simple docstring'''
				lowercase_					  = parse_file(snake_case__ )
				lowercase_					  = build_tree(snake_case__ )
				lowercase_					  = {
				    k: v for letter in traverse_tree(snake_case__						,       '''''' ) for k, v in letter.bitstring.items()
				}
				print(F'''Huffman Coding  of {file_path}: ''' )
				with open(snake_case__ ) as f:
								while True:
												lowercase_					  = 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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a        = {
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'Speech2Text2ForCausalLM',
						    'Speech2Text2PreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
						from .processing_speech_to_text_a import SpeechaTextaProcessor
						from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_to_text_a import (
												    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SpeechaTextaForCausalLM,
												    SpeechaTextaPreTrainedModel,
												)

else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




