






from typing import TYPE_CHECKING

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


UpperCamelCase__						:      str								=					{
    """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__						:      Union[str, Any]								=					[
				    """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__						:      Optional[Any]								=					[
				    """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__						:      str								=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













from __future__ import annotations

import sys







class 				lowerCamelCase_     :
							def __init__(						self		:					Optional[int]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								a		=			letter
								a		=			freq
								a		=			{}




							def __repr__(						self		:					List[str]					):





								'''simple docstring'''



								return F"""{self.letter}:{self.freq}"""







class 				lowerCamelCase_     :
							def __init__(						self		:					Dict							,__lowerCamelCase		:					int							,__lowerCamelCase		:					Letter | TreeNode							,__lowerCamelCase		:					Letter | TreeNode					):





								'''simple docstring'''



								a		=			freq
								a		=			left
								a		=			right





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



	a		=			{}
	with open(snake_case_  ) as f:
		while True:
			a		=			f.read(1  )
			if not c:
				break
			a		=			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   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  Letter | TreeNode:


	"""simple docstring"""



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





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



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





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  None:


	"""simple docstring"""



	a		=			parse_file(snake_case_  )
	a		=			build_tree(snake_case_  )
	a		=			{
	    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:
			a		=			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])





