





'''simple docstring'''






from __future__ import annotations

import sys



class  lowerCamelCase__				:


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				str:
					_A :				str				=  letter
					_A :				int				=  freq
					_A :				dict[str, str]				=  {}




	def __repr__(					self			)       ->				str:
					return F"""{self.letter}:{self.freq}"""



class  lowerCamelCase__				:


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Dict:
					_A :				int				=  freq
					_A :				Letter | TreeNode				=  left
					_A :				Letter | TreeNode				=  right





def      lowercase  (  lowerCAmelCase :						str):



				"""simple docstring"""


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





def      lowercase  (  lowerCAmelCase :						list[Letter]):



				"""simple docstring"""


				_A :				list[Letter | TreeNode]				=  letters  # type: ignore
				while len(lowerCAmelCase) > 1:
								_A :				Optional[int]				=  response.pop(0)
								_A :				Union[str, Any]				=  response.pop(0)
								_A :				Any				=  left.freq + right.freq
								_A :				List[Any]				=  TreeNode(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
								response.append(lowerCAmelCase)
								response.sort(key=lambda lowerCAmelCase: x.freq)
				return response[0]





def      lowercase  (  lowerCAmelCase :						Letter | TreeNode   , lowerCAmelCase :						str):



				"""simple docstring"""


				if isinstance(lowerCAmelCase   , lowerCAmelCase):
								_A :				List[Any]				=  bitstring
								return [root]
				_A :				TreeNode				=  root  # type: ignore
				_A :				List[Any]				=  []
				letters += traverse_tree(treenode.left   , bitstring + '''0''')
				letters += traverse_tree(treenode.right   , bitstring + '''1''')
				return letters





def      lowercase  (  lowerCAmelCase :						str):



				"""simple docstring"""


				_A :				Any				=  parse_file(lowerCAmelCase)
				_A :				int				=  build_tree(lowerCAmelCase)
				_A :				Optional[int]				=  {
				    k: v for letter in traverse_tree(lowerCAmelCase   , '''''') for k, v in letter.bitstring.items()
				}
				print(f"""Huffman Coding  of {file_path}: """)
				with open(lowerCAmelCase) as f:
								while True:
												_A :				Optional[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])







'''simple docstring'''






from typing import TYPE_CHECKING

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


__UpperCamelCase      :   List[str]								=     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   List[Any]								=     [
	    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTJForCausalLM''',
	    '''GPTJForQuestionAnswering''',
	    '''GPTJForSequenceClassification''',
	    '''GPTJModel''',
	    '''GPTJPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Optional[int]								=     [
	    '''TFGPTJForCausalLM''',
	    '''TFGPTJForQuestionAnswering''',
	    '''TFGPTJForSequenceClassification''',
	    '''TFGPTJModel''',
	    '''TFGPTJPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Union[str, Any]								=     [
	    '''FlaxGPTJForCausalLM''',
	    '''FlaxGPTJModel''',
	    '''FlaxGPTJPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	__UpperCamelCase      :   List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
