


import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase							:							List[str]    							=					logging.get_logger(__name__)

__UpperCamelCase							:							Optional[Any]    							=					[
    (
        """protein1""",
        """MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA""",
    ),
    ("""protein2""", """MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"""),
    ("""protein3""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"""),
    ("""protein4""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"""),
]

__UpperCamelCase							:							List[Any]    							=					{
    """esm1b_t33_650M_UR50S""": esm_module.pretrained.esm1b_t33_650M_UR50S,
    """esm1v_t33_650M_UR90S_1""": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    """esm1v_t33_650M_UR90S_2""": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    """esm1v_t33_650M_UR90S_3""": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    """esm1v_t33_650M_UR90S_4""": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    """esm1v_t33_650M_UR90S_5""": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    """esm2_t48_15B_UR50D""": esm_module.pretrained.esm2_t48_15B_UR50D,
    """esm2_t36_3B_UR50D""": esm_module.pretrained.esm2_t36_3B_UR50D,
    """esm2_t33_650M_UR50D""": esm_module.pretrained.esm2_t33_650M_UR50D,
    """esm2_t30_150M_UR50D""": esm_module.pretrained.esm2_t30_150M_UR50D,
    """esm2_t12_35M_UR50D""": esm_module.pretrained.esm2_t12_35M_UR50D,
    """esm2_t6_8M_UR50D""": esm_module.pretrained.esm2_t6_8M_UR50D,
    """esmfold_v1""": esmfold_va,
}

__UpperCamelCase							:							List[Any]    							=					list("ARNDCQEGHILKMFPSTWYV")

__UpperCamelCase							:							List[Any]    							=					restypes + ["""X"""]
__UpperCamelCase							:							Optional[int]    							=					restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]

def       _a	(					):







 """simple docstring"""



 with TemporaryDirectory() as tempdir:
  UpperCamelCase__       :     Optional[int] 							= """\n""".join(_a  )
  UpperCamelCase__       :     Optional[Any] 							= Path(_a  ) / """vocab.txt"""
  vocab_file.write_text(_a  )
  UpperCamelCase__       :     List[Any] 							= EsmTokenizer(vocab_file=str(_a  )  )
 UpperCamelCase__       :     int 							= 0  # Overlaps with 'A' but that seems to be what they want
 return hf_tokenizer

def       _a	(					SCREAMING_SNAKE_CASE						:					Dict     ,			SCREAMING_SNAKE_CASE						:					Dict  ):







 """simple docstring"""



 UpperCamelCase__       :     List[str] 							= our_module.load_state_dict(original_module.state_dict()  )
 if status.missing_keys:
  raise ValueError(F"Missing keys: {status.missing_keys}"  )
 if status.unexpected_keys:
  raise ValueError(F"Unexpected keys: {status.unexpected_keys}"  )

def       _a	(					SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					bool     ,			SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					str  ):







 """simple docstring"""



 if model.startswith('''esmfold'''  ):
  UpperCamelCase__       :     List[str] 							= MODEL_MAPPING[model]()
 else:
  UpperCamelCase__       :     Dict 							= MODEL_MAPPING[model]()
 esm.eval()  # disable dropout

 if model.startswith('''esmfold'''  ):
  UpperCamelCase__       :     Union[str, Any] 							= esm.esm.embed_dim
  UpperCamelCase__       :     Any 							= esm.esm.num_layers
  UpperCamelCase__       :     Tuple 							= esm.esm.attention_heads
  UpperCamelCase__       :     Dict 							= 4 * embed_dim
  UpperCamelCase__       :     Dict 							= esm.esm.token_dropout
  UpperCamelCase__       :     List[str] 							= False  # This code path does not exist in ESM-2
  UpperCamelCase__       :     Any 							= """rotary"""
  UpperCamelCase__       :     str 							= True
  UpperCamelCase__       :     Dict 							= EsmFoldConfig()
  for key, val in esm.cfg.items():
   if hasattr(_a     ,			_a  ) and key != "trunk":
    setattr(_a     ,			_a     ,			_a  )
  for key, val in esm.cfg.trunk.items():
   if hasattr(esmfold_config.trunk     ,			_a  ) and key != "structure_module":
    setattr(esmfold_config.trunk     ,			_a     ,			_a  )
  for key, val in esm.cfg.trunk.structure_module.items():
   if hasattr(esmfold_config.trunk.structure_module     ,			_a  ):
    setattr(esmfold_config.trunk.structure_module     ,			_a     ,			_a  )
 elif hasattr(_a     ,			'''args'''  ):
  # Indicates an ESM-1b or ESM-1v model
  UpperCamelCase__       :     Any 							= esm.args.embed_dim
  UpperCamelCase__       :     Dict 							= esm.args.layers
  UpperCamelCase__       :     Optional[int] 							= esm.args.attention_heads
  UpperCamelCase__       :     List[str] 							= esm.args.ffn_embed_dim
  UpperCamelCase__       :     Union[str, Any] 							= esm.args.token_dropout
  UpperCamelCase__       :     Optional[int] 							= True if esm.emb_layer_norm_before else False
  UpperCamelCase__       :     Union[str, Any] 							= """absolute"""
  UpperCamelCase__       :     List[str] 							= False
  UpperCamelCase__       :     List[str] 							= None
 else:
  # Indicates an ESM-2 model
  UpperCamelCase__       :     Any 							= esm.embed_dim
  UpperCamelCase__       :     Any 							= esm.num_layers
  UpperCamelCase__       :     int 							= esm.attention_heads
  UpperCamelCase__       :     Union[str, Any] 							= 4 * embed_dim  # This is hardcoded in ESM-2
  UpperCamelCase__       :     int 							= esm.token_dropout
  UpperCamelCase__       :     Any 							= False  # This code path does not exist in ESM-2
  UpperCamelCase__       :     List[str] 							= """rotary"""
  UpperCamelCase__       :     Tuple 							= False
  UpperCamelCase__       :     Union[str, Any] 							= None

 if is_folding_model:
  UpperCamelCase__       :     Union[str, Any] 							= esm.esm.alphabet
 UpperCamelCase__       :     Any 							= tuple(alphabet.all_toks  )
 UpperCamelCase__       :     Union[str, Any] 							= alphabet.mask_idx
 UpperCamelCase__       :     Optional[Any] 							= alphabet.padding_idx

 if is_folding_model:
  UpperCamelCase__       :     Dict 							= esm.esm
 else:
  UpperCamelCase__       :     List[str] 							= esm

 UpperCamelCase__       :     Any 							= EsmConfig(
     vocab_size=original_esm_model.embed_tokens.num_embeddings     ,			mask_token_id=_a     ,			hidden_size=_a     ,			num_hidden_layers=_a     ,			num_attention_heads=_a     ,			intermediate_size=_a     ,			max_position_embeddings=1026     ,			layer_norm_eps=1E-5     ,			attention_probs_dropout_prob=0.0     ,			hidden_dropout_prob=0.0     ,			pad_token_id=_a     ,			emb_layer_norm_before=_a     ,			token_dropout=_a     ,			position_embedding_type=_a     ,			is_folding_model=_a     ,			esmfold_config=_a     ,			vocab_list=_a     ,			)
 if classification_head:
  UpperCamelCase__       :     Tuple 							= esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
 print('''Our ESM config:'''     ,			_a  )

 if model.startswith('''esmfold'''  ):
  UpperCamelCase__       :     int 							= EsmForProteinFolding
 elif classification_head:
  UpperCamelCase__       :     str 							= EsmForSequenceClassification
 else:
  UpperCamelCase__       :     Optional[int] 							= EsmForMaskedLM
 UpperCamelCase__       :     str 							= model_class(_a  )
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 UpperCamelCase__       :     Union[str, Any] 							= original_esm_model.embed_tokens.weight
 if position_embedding_type == "absolute":
  UpperCamelCase__       :     int 							= original_esm_model.embed_positions.weight

 if config.emb_layer_norm_before:
  UpperCamelCase__       :     List[Any] 							= original_esm_model.emb_layer_norm_before.weight
  UpperCamelCase__       :     Dict 							= original_esm_model.emb_layer_norm_before.bias

 UpperCamelCase__       :     List[Any] 							= original_esm_model.emb_layer_norm_after.weight
 UpperCamelCase__       :     Optional[Any] 							= original_esm_model.emb_layer_norm_after.bias

 for i in range(config.num_hidden_layers  ):
  # Encoder: start of layer
  UpperCamelCase__       :     EsmLayer 							= model.esm.encoder.layer[i]
  # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
  UpperCamelCase__       :     Optional[int] 							= original_esm_model.layers[i]

  # self attention
  UpperCamelCase__       :     EsmSelfAttention 							= layer.attention.self
  assert (
      esm_layer.self_attn.k_proj.weight.data.shape
      == esm_layer.self_attn.q_proj.weight.data.shape
      == esm_layer.self_attn.v_proj.weight.data.shape
      == torch.Size((config.hidden_size, config.hidden_size)  )
  )

  UpperCamelCase__       :     Tuple 							= esm_layer.self_attn.q_proj.weight
  UpperCamelCase__       :     List[str] 							= esm_layer.self_attn.q_proj.bias
  UpperCamelCase__       :     Optional[int] 							= esm_layer.self_attn.k_proj.weight
  UpperCamelCase__       :     List[str] 							= esm_layer.self_attn.k_proj.bias
  UpperCamelCase__       :     List[str] 							= esm_layer.self_attn.v_proj.weight
  UpperCamelCase__       :     Optional[int] 							= esm_layer.self_attn.v_proj.bias

  if getattr(esm_layer.self_attn     ,			'''rot_emb'''     ,			_a  ) is not None:
   # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
   # During the training of ESM-2 the model was converted to float16 precision, which also converts
   # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
   # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
   # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
   # we make sure the new model copies the data from the old inv_freq.
   UpperCamelCase__       :     Optional[Any] 							= esm_layer.self_attn.rot_emb.inv_freq

  # LayerNorm changes for pre-activation
  UpperCamelCase__       :     int 							= esm_layer.self_attn_layer_norm.weight
  UpperCamelCase__       :     List[Any] 							= esm_layer.self_attn_layer_norm.bias
  UpperCamelCase__       :     Tuple 							= esm_layer.final_layer_norm.weight
  UpperCamelCase__       :     Union[str, Any] 							= esm_layer.final_layer_norm.bias

  # self-attention output
  UpperCamelCase__       :     EsmSelfOutput 							= layer.attention.output
  assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
  UpperCamelCase__       :     Any 							= esm_layer.self_attn.out_proj.weight
  UpperCamelCase__       :     List[Any] 							= esm_layer.self_attn.out_proj.bias

  # intermediate
  UpperCamelCase__       :     EsmIntermediate 							= layer.intermediate
  assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
  UpperCamelCase__       :     Optional[int] 							= esm_layer.fca.weight
  UpperCamelCase__       :     Tuple 							= esm_layer.fca.bias

  # output
  UpperCamelCase__       :     EsmOutput 							= layer.output
  assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
  UpperCamelCase__       :     Optional[Any] 							= esm_layer.fca.weight
  UpperCamelCase__       :     Union[str, Any] 							= esm_layer.fca.bias
  # end of layer

 if is_folding_model:
  UpperCamelCase__       :     str 							= esm.esm_s_combine.data
  UpperCamelCase__       :     Optional[int] 							= esm.afa_to_esm.data
  transfer_and_check_weights(esm.embedding     ,			model.embedding  )
  transfer_and_check_weights(esm.esm_s_mlp     ,			model.esm_s_mlp  )
  transfer_and_check_weights(esm.trunk     ,			model.trunk  )
  transfer_and_check_weights(esm.distogram_head     ,			model.distogram_head  )
  transfer_and_check_weights(esm.ptm_head     ,			model.ptm_head  )
  transfer_and_check_weights(esm.lm_head     ,			model.lm_head  )
  transfer_and_check_weights(esm.lddt_head     ,			model.lddt_head  )

 elif classification_head:
  UpperCamelCase__       :     List[str] 							= esm.esm.classification_heads["""mnli"""].dense.weight
  UpperCamelCase__       :     List[Any] 							= esm.classification_heads["""mnli"""].dense.bias
  UpperCamelCase__       :     Optional[Any] 							= esm.classification_heads["""mnli"""].out_proj.weight
  UpperCamelCase__       :     List[Any] 							= esm.classification_heads["""mnli"""].out_proj.bias
 else:
  # LM Head
  UpperCamelCase__       :     List[str] 							= esm.lm_head.dense.weight
  UpperCamelCase__       :     Dict 							= esm.lm_head.dense.bias
  UpperCamelCase__       :     Tuple 							= esm.lm_head.layer_norm.weight
  UpperCamelCase__       :     List[str] 							= esm.lm_head.layer_norm.bias
  UpperCamelCase__       :     List[Any] 							= esm.lm_head.weight
  UpperCamelCase__       :     int 							= esm.lm_head.bias

 # Contact prediction head
 transfer_and_check_weights(esm.contact_head     ,			model.esm.contact_head  )

 # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
 if is_folding_model:
  # Folding models aren't trained on masked inputs and don't like mask tokens.
  UpperCamelCase__       :     Union[str, Any] 							= SAMPLE_DATA[:2]
 else:
  UpperCamelCase__       :     Optional[int] 							= SAMPLE_DATA

 if is_folding_model:
  UpperCamelCase__       :     Optional[Any] 							= get_esmfold_tokenizer()
  UpperCamelCase__       :     Dict 							= hf_tokenizer(
      [row[1] for row in sample_data]     ,			return_tensors='''pt'''     ,			padding=_a     ,			add_special_tokens=_a  )
  UpperCamelCase__       :     Dict 							= esmfold_encode_sequences([row[1] for row in sample_data]  )
  UpperCamelCase__       :     Optional[int] 							= torch.all(hf_tokens['''input_ids'''] == esmfold_aas  ) and torch.all(
      hf_tokens['''attention_mask'''] == esmfold_mask  )
 else:
  # Let's check that we get the same results.
  UpperCamelCase__       :     Tuple 							= alphabet.get_batch_converter()
  UpperCamelCase__       :     Tuple 							= batch_converter(_a  )
  # Prepare tokenizer and make sure it matches
  with TemporaryDirectory() as tempdir:
   UpperCamelCase__       :     Any 							= """\n""".join(alphabet.all_toks  )
   UpperCamelCase__       :     List[str] 							= Path(_a  ) / """vocab.txt"""
   vocab_file.write_text(_a  )
   UpperCamelCase__       :     Union[str, Any] 							= EsmTokenizer(vocab_file=str(_a  )  )

  UpperCamelCase__       :     Dict 							= hf_tokenizer([row[1] for row in sample_data]     ,			return_tensors='''pt'''     ,			padding=_a  )
  UpperCamelCase__       :     List[Any] 							= torch.all(hf_tokens['''input_ids'''] == batch_tokens  )

 print('''Do both models tokenizers output the same tokens?'''     ,			'''🔥''' if success else '''💩'''  )
 if not success:
  raise Exception('''Tokenization does not match!'''  )

 with torch.no_grad():
  if is_folding_model:
   # Let's test the model in parts
   # ESMFold always converts the ESM stem to float16, which requires float16 ops
   # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
   # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
   # original and the converted model on the GPU at the same time.
   UpperCamelCase__       :     Optional[int] 							= esm.cuda().infer([row[1] for row in sample_data]  )
   UpperCamelCase__       :     Union[str, Any] 							= model.cuda()(
       input_ids=hf_tokens['''input_ids'''].cuda()     ,			attention_mask=hf_tokens['''attention_mask'''].cuda()  )
  else:
   UpperCamelCase__       :     Dict 							= model(**_a     ,			output_hidden_states=_a  )
   UpperCamelCase__       :     List[Any] 							= our_output["""logits"""]
   if classification_head:
    UpperCamelCase__       :     List[str] 							= esm.model.classification_heads["""mnli"""](esm.extract_features(_a  )  )
   else:
    UpperCamelCase__       :     Dict 							= esm(hf_tokens['''input_ids''']     ,			repr_layers=list(range(999  )  )  )
    UpperCamelCase__       :     Dict 							= their_output["""logits"""]

  if is_folding_model:
   UpperCamelCase__       :     Optional[Any] 							= torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']  )  ).item()
   UpperCamelCase__       :     str 							= torch.allclose(our_output['''positions''']     ,			their_output['''positions''']     ,			atol=1E-5  )
  else:
   UpperCamelCase__       :     List[Any] 							= torch.max(torch.abs(our_output - their_output  )  ).item()
   UpperCamelCase__       :     str 							= torch.allclose(_a     ,			_a     ,			atol=1E-5  )

  print(F"max_absolute_diff = {max_absolute_diff}"  )  # ~ 1e-5
  print('''Do both models output the same tensors?'''     ,			'''🔥''' if success else '''💩'''  )

  if not success:
   raise Exception('''Something went wRoNg'''  )

  if not is_folding_model:
   # Let's check contact prediction too
   UpperCamelCase__       :     Union[str, Any] 							= model.predict_contacts(hf_tokens['''input_ids''']     ,			hf_tokens['''attention_mask''']  )
   UpperCamelCase__       :     Optional[Any] 							= esm.predict_contacts(hf_tokens['''input_ids''']  )
   UpperCamelCase__       :     Dict 							= torch.max(torch.abs(our_output - their_output  )  ).item()
   UpperCamelCase__       :     List[str] 							= torch.allclose(_a     ,			_a     ,			atol=1E-5  )

   print('''Contact prediction testing:'''  )
   print(F"max_absolute_diff = {max_absolute_diff}"  )  # ~ 1e-5
   print('''Do both models output the same tensors?'''     ,			'''🔥''' if success else '''💩'''  )

   if not success:
    raise Exception('''Something went wRoNg'''  )

  pathlib.Path(_a  ).mkdir(parents=_a     ,			exist_ok=_a  )
  print(F"Saving model to {pytorch_dump_folder_path}"  )
  model.save_pretrained(_a  )

  del esm  # Free up some memory before continuing

 print(F"Saving tokenizer to {pytorch_dump_folder_path}"  )
 hf_tokenizer.save_pretrained(_a  )

 if push_to_repo:
  model.push_to_hub(repo_id=_a     ,			use_auth_token=_a  )
  hf_tokenizer.push_to_hub(repo_id=_a     ,			use_auth_token=_a  )


if __name__ == "__main__":
       __UpperCamelCase							:							Optional[int]    							=					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--pytorch_dump_folder_path", type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--classification_head", action="store_true", help="Whether to convert a final classification head."
       )
       parser.add_argument("--model", default=None, type=str, required=True, help="Name of model to convert.")
       parser.add_argument("--push_to_repo", type=str, help="Repo to upload to (including username!).")
       parser.add_argument("--auth_token", type=str, help="HuggingFace auth token.")
       __UpperCamelCase							:							List[str]    							=					parser.parse_args()
       convert_esm_checkpoint_to_pytorch(
           args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
       )









from __future__ import annotations

import sys





class   lowercase__     :







  def __init__(						self     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							str			 =		letter
       lowerCAmelCase_       :							int			 =		freq
       lowerCAmelCase_       :							dict[str, str]			 =		{}







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





class   lowercase__     :







  def __init__(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							int			 =		freq
       lowerCAmelCase_       :							Letter | TreeNode			 =		left
       lowerCAmelCase_       :							Letter | TreeNode			 =		right






def 				_lowerCAmelCase			(	_a :       str      )						->      list[Letter]:
     lowerCAmelCase_       :							dict[str, int]			 =		{}
     with open(_a      ) as f:
          while True:
               lowerCAmelCase_       :							Any			 =		f.read(1      )
               if not c:
                    break
               lowerCAmelCase_       :							List[str]			 =		chars[c] + 1 if c in chars else 1
     return sorted((Letter(_a       , _a      ) for c, f in chars.items())       , key=lambda _a      : x.freq      )






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






def 				_lowerCAmelCase			(	_a :       Letter | TreeNode       , _a :       str      )						->      list[Letter]:
     if isinstance(_a       , _a      ):
          lowerCAmelCase_       :							Union[str, Any]			 =		bitstring
          return [root]
     lowerCAmelCase_       :							TreeNode			 =		root  # type: ignore
     lowerCAmelCase_       :							Tuple			 =		[]
     letters += traverse_tree(treenode.left       , bitstring + """0"""      )
     letters += traverse_tree(treenode.right       , bitstring + """1"""      )
     return letters






def 				_lowerCAmelCase			(	_a :       str      )						->      None:
     lowerCAmelCase_       :							Optional[Any]			 =		parse_file(_a      )
     lowerCAmelCase_       :							Optional[int]			 =		build_tree(_a      )
     lowerCAmelCase_       :							str			 =		{
         k: v for letter in traverse_tree(_a       , """"""      ) for k, v in letter.bitstring.items()
     }
     print(F'Huffman Coding  of {file_path}: '      )
     with open(_a      ) as f:
          while True:
               lowerCAmelCase_       :							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])
