code: 
  
  
  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()
  _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					logging.get_logger(__name__)
  
  _SCREAMING_SNAKE_CASE    :				Tuple       =					[
      (
          "protein1",
          "MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
      ),
      ("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
      ("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
      ("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
  ]
  
  _SCREAMING_SNAKE_CASE    :				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,
  }
  
  _SCREAMING_SNAKE_CASE    :				int       =					list("ARNDCQEGHILKMFPSTWYV")
  
  _SCREAMING_SNAKE_CASE    :				Any       =					restypes + ["X"]
  _SCREAMING_SNAKE_CASE    :				List[Any]       =					restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       with TemporaryDirectory() as tempdir:
            snake_case					=       '''\n'''.join(UpperCamelCase_       )
            snake_case					=       Path(UpperCamelCase_       ) / '''vocab.txt'''
            vocab_file.write_text(UpperCamelCase_       )
            snake_case					=       EsmTokenizer(vocab_file=str(UpperCamelCase_       )       )
       snake_case					=       0  # Overlaps with 'A' but that seems to be what they want
       return hf_tokenizer
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if model.startswith('''esmfold'''       ):
            snake_case					=       MODEL_MAPPING[model]()
       else:
            snake_case				,						snake_case					=       MODEL_MAPPING[model]()
       esm.eval()  # disable dropout
  
       if model.startswith('''esmfold'''       ):
            snake_case					=       esm.esm.embed_dim
            snake_case					=       esm.esm.num_layers
            snake_case					=       esm.esm.attention_heads
            snake_case					=       4 * embed_dim
            snake_case					=       esm.esm.token_dropout
            snake_case					=       False  # This code path does not exist in ESM-2
            snake_case					=       '''rotary'''
            snake_case					=       True
            snake_case					=       EsmFoldConfig()
            for key, val in esm.cfg.items():
                 if hasattr(UpperCamelCase_							,UpperCamelCase_       ) and key != "trunk":
                      setattr(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
            for key, val in esm.cfg.trunk.items():
                 if hasattr(esmfold_config.trunk							,UpperCamelCase_       ) and key != "structure_module":
                      setattr(esmfold_config.trunk							,UpperCamelCase_							,UpperCamelCase_       )
            for key, val in esm.cfg.trunk.structure_module.items():
                 if hasattr(esmfold_config.trunk.structure_module							,UpperCamelCase_       ):
                      setattr(esmfold_config.trunk.structure_module							,UpperCamelCase_							,UpperCamelCase_       )
       elif hasattr(UpperCamelCase_							,'''args'''       ):
            # Indicates an ESM-1b or ESM-1v model
            snake_case					=       esm.args.embed_dim
            snake_case					=       esm.args.layers
            snake_case					=       esm.args.attention_heads
            snake_case					=       esm.args.ffn_embed_dim
            snake_case					=       esm.args.token_dropout
            snake_case					=       True if esm.emb_layer_norm_before else False
            snake_case					=       '''absolute'''
            snake_case					=       False
            snake_case					=       None
       else:
            # Indicates an ESM-2 model
            snake_case					=       esm.embed_dim
            snake_case					=       esm.num_layers
            snake_case					=       esm.attention_heads
            snake_case					=       4 * embed_dim  # This is hardcoded in ESM-2
            snake_case					=       esm.token_dropout
            snake_case					=       False  # This code path does not exist in ESM-2
            snake_case					=       '''rotary'''
            snake_case					=       False
            snake_case					=       None
  
       if is_folding_model:
            snake_case					=       esm.esm.alphabet
       snake_case					=       tuple(alphabet.all_toks       )
       snake_case					=       alphabet.mask_idx
       snake_case					=       alphabet.padding_idx
  
       if is_folding_model:
            snake_case					=       esm.esm
       else:
            snake_case					=       esm
  
       snake_case					=       EsmConfig(
           vocab_size=original_esm_model.embed_tokens.num_embeddings							,mask_token_id=UpperCamelCase_							,hidden_size=UpperCamelCase_							,num_hidden_layers=UpperCamelCase_							,num_attention_heads=UpperCamelCase_							,intermediate_size=UpperCamelCase_							,max_position_embeddings=10_26							,layer_norm_eps=1e-5							,attention_probs_dropout_prob=0.0							,hidden_dropout_prob=0.0							,pad_token_id=UpperCamelCase_							,emb_layer_norm_before=UpperCamelCase_							,token_dropout=UpperCamelCase_							,position_embedding_type=UpperCamelCase_							,is_folding_model=UpperCamelCase_							,esmfold_config=UpperCamelCase_							,vocab_list=UpperCamelCase_							,)
       if classification_head:
            snake_case					=       esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
       print('''Our ESM config:'''							,UpperCamelCase_       )
  
       if model.startswith('''esmfold'''       ):
            snake_case					=       EsmForProteinFolding
       elif classification_head:
            snake_case					=       EsmForSequenceClassification
       else:
            snake_case					=       EsmForMaskedLM
       snake_case					=       model_class(UpperCamelCase_       )
       model.eval()
  
       # Now let's copy all the weights.
       # Embeddings
       snake_case					=       original_esm_model.embed_tokens.weight
       if position_embedding_type == "absolute":
            snake_case					=       original_esm_model.embed_positions.weight
  
       if config.emb_layer_norm_before:
            snake_case					=       original_esm_model.emb_layer_norm_before.weight
            snake_case					=       original_esm_model.emb_layer_norm_before.bias
  
       snake_case					=       original_esm_model.emb_layer_norm_after.weight
       snake_case					=       original_esm_model.emb_layer_norm_after.bias
  
       for i in range(config.num_hidden_layers       ):
            # Encoder: start of layer
            snake_case					=       model.esm.encoder.layer[i]
            # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
            snake_case					=       original_esm_model.layers[i]
  
            # self attention
            snake_case					=       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)       )
            )
  
            snake_case					=       esm_layer.self_attn.q_proj.weight
            snake_case					=       esm_layer.self_attn.q_proj.bias
            snake_case					=       esm_layer.self_attn.k_proj.weight
            snake_case					=       esm_layer.self_attn.k_proj.bias
            snake_case					=       esm_layer.self_attn.v_proj.weight
            snake_case					=       esm_layer.self_attn.v_proj.bias
  
            if getattr(esm_layer.self_attn							,'''rot_emb'''							,UpperCamelCase_       ) 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.
                 snake_case					=       esm_layer.self_attn.rot_emb.inv_freq
  
            # LayerNorm changes for pre-activation
            snake_case					=       esm_layer.self_attn_layer_norm.weight
            snake_case					=       esm_layer.self_attn_layer_norm.bias
            snake_case					=       esm_layer.final_layer_norm.weight
            snake_case					=       esm_layer.final_layer_norm.bias
  
            # self-attention output
            snake_case					=       layer.attention.output
            assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
            snake_case					=       esm_layer.self_attn.out_proj.weight
            snake_case					=       esm_layer.self_attn.out_proj.bias
  
            # intermediate
            snake_case					=       layer.intermediate
            assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
            snake_case					=       esm_layer.fca.weight
            snake_case					=       esm_layer.fca.bias
  
            # output
            snake_case					=       layer.output
            assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
            snake_case					=       esm_layer.fca.weight
            snake_case					=       esm_layer.fca.bias
            # end of layer
  
       if is_folding_model:
            snake_case					=       esm.esm_s_combine.data
            snake_case					=       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:
            snake_case					=       esm.esm.classification_heads['''mnli'''].dense.weight
            snake_case					=       esm.classification_heads['''mnli'''].dense.bias
            snake_case					=       esm.classification_heads['''mnli'''].out_proj.weight
            snake_case					=       esm.classification_heads['''mnli'''].out_proj.bias
       else:
            # LM Head
            snake_case					=       esm.lm_head.dense.weight
            snake_case					=       esm.lm_head.dense.bias
            snake_case					=       esm.lm_head.layer_norm.weight
            snake_case					=       esm.lm_head.layer_norm.bias
            snake_case					=       esm.lm_head.weight
            snake_case					=       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.
            snake_case					=       SAMPLE_DATA[:2]
       else:
            snake_case					=       SAMPLE_DATA
  
       if is_folding_model:
            snake_case					=       get_esmfold_tokenizer()
            snake_case					=       hf_tokenizer(
                [row[1] for row in sample_data]							,return_tensors='''pt'''							,padding=UpperCamelCase_							,add_special_tokens=UpperCamelCase_       )
            snake_case				,						snake_case				,						snake_case				,						snake_case				,						snake_case					=       esmfold_encode_sequences([row[1] for row in sample_data]       )
            snake_case					=       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.
            snake_case					=       alphabet.get_batch_converter()
            snake_case				,						snake_case				,						snake_case					=       batch_converter(UpperCamelCase_       )
            # Prepare tokenizer and make sure it matches
            with TemporaryDirectory() as tempdir:
                 snake_case					=       '''\n'''.join(alphabet.all_toks       )
                 snake_case					=       Path(UpperCamelCase_       ) / '''vocab.txt'''
                 vocab_file.write_text(UpperCamelCase_       )
                 snake_case					=       EsmTokenizer(vocab_file=str(UpperCamelCase_       )       )
  
            snake_case					=       hf_tokenizer([row[1] for row in sample_data]							,return_tensors='''pt'''							,padding=UpperCamelCase_       )
            snake_case					=       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.
                 snake_case					=       esm.cuda().infer([row[1] for row in sample_data]       )
                 snake_case					=       model.cuda()(
                     input_ids=hf_tokens['''input_ids'''].cuda()							,attention_mask=hf_tokens['''attention_mask'''].cuda()       )
            else:
                 snake_case					=       model(**UpperCamelCase_							,output_hidden_states=UpperCamelCase_       )
                 snake_case					=       our_output['''logits''']
                 if classification_head:
                      snake_case					=       esm.model.classification_heads['''mnli'''](esm.extract_features(UpperCamelCase_       )       )
                 else:
                      snake_case					=       esm(hf_tokens['''input_ids''']							,repr_layers=list(range(9_99       )       )       )
                      snake_case					=       their_output['''logits''']
  
            if is_folding_model:
                 snake_case					=       torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']       )       ).item()
                 snake_case					=       torch.allclose(our_output['''positions''']							,their_output['''positions''']							,atol=1e-5       )
            else:
                 snake_case					=       torch.max(torch.abs(our_output - their_output       )       ).item()
                 snake_case					=       torch.allclose(UpperCamelCase_							,UpperCamelCase_							,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
                 snake_case					=       model.predict_contacts(hf_tokens['''input_ids''']							,hf_tokens['''attention_mask''']       )
                 snake_case					=       esm.predict_contacts(hf_tokens['''input_ids''']       )
                 snake_case					=       torch.max(torch.abs(our_output - their_output       )       ).item()
                 snake_case					=       torch.allclose(UpperCamelCase_							,UpperCamelCase_							,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(UpperCamelCase_       ).mkdir(parents=UpperCamelCase_							,exist_ok=UpperCamelCase_       )
            print(F'''Saving model to {pytorch_dump_folder_path}'''       )
            model.save_pretrained(UpperCamelCase_       )
  
            del esm  # Free up some memory before continuing
  
       print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''       )
       hf_tokenizer.save_pretrained(UpperCamelCase_       )
  
       if push_to_repo:
            model.push_to_hub(repo_id=UpperCamelCase_							,use_auth_token=UpperCamelCase_       )
            hf_tokenizer.push_to_hub(repo_id=UpperCamelCase_							,use_auth_token=UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE    :				Dict       =					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.")
   _SCREAMING_SNAKE_CASE    :				Dict       =					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
   )
  
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  import random
  from collections.abc import Iterable
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case					):
           snake_case					=       {literal: None for literal in literals}
      def __str__( self					):
           return "{" + " , ".join(self.literals					) + "}"
      def __len__( self					):
           return len(self.literals					)
      def 	a_    ( self    ,      __snake_case					):
           for literal in self.literals:
                snake_case					=       literal[:2]
                if symbol in model:
                     snake_case					=       model[symbol]
                else:
                     continue
                if value is not None:
                     # Complement assignment if literal is in complemented form
                     if literal.endswith('''\''''					):
                          snake_case					=       not value
                snake_case					=       value
  
  
  
  
      def 	a_    ( self    ,      __snake_case					):
           for literal in self.literals:
                snake_case					=       literal.rstrip('''\''''					) if literal.endswith('''\''''					) else literal + '''\''''
                if symbol in self.literals:
                     return True
  
           self.assign(__snake_case					)
           for value in self.literals.values():
                if value in (True, None):
                     return value
           return any(self.literals.values()					)
  
  
  
  
  
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case					):
           snake_case					=       list(__snake_case					)
  
  
  
  
      def __str__( self					):
           return "{" + " , ".join(str(__snake_case					) for clause in self.clauses					) + "}"
  
  
  
  
  
  
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       random.randint(1							,5       )
       snake_case					=       '''A'''
       snake_case					=       0
       while i < no_of_literals:
            snake_case					=       random.randint(1							,5       )
            snake_case					=       base_var + str(UpperCamelCase_       )
            snake_case					=       random.randint(0							,1       )
            if var_complement == 1:
                 var_name += "'"
            if var_name in literals:
                 i -= 1
            else:
                 literals.append(UpperCamelCase_       )
            i += 1
       return Clause(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       set()
       snake_case					=       random.randint(1							,10       )
       while len(UpperCamelCase_       ) < no_of_clauses:
            clauses.add(generate_clause()       )
       return Formula(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       formula.clauses
       snake_case					=       []
       for clause in formula.clauses:
            for literal in clause.literals:
                 snake_case					=       literal[:2]
                 if symbol not in symbols_set:
                      symbols_set.append(UpperCamelCase_       )
       return clauses, symbols_set
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       {}
       snake_case					=       []
  
       for clause in clauses:
            if clause.evaluate(UpperCamelCase_       ):
                 continue
            for literal in clause.literals:
                 literals.append(UpperCamelCase_       )
  
       for s in symbols:
            snake_case					=       s + '''\''''
            if (s in literals and sym not in literals) or (
                s not in literals and sym in literals
            ):
                 pure_symbols.append(UpperCamelCase_       )
       for p in pure_symbols:
            snake_case					=       None
       for s in pure_symbols:
            snake_case					=       s + '''\''''
            if s in literals:
                 snake_case					=       True
            elif sym in literals:
                 snake_case					=       False
       return pure_symbols, assignment
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       for clause in clauses:
            if len(UpperCamelCase_       ) == 1:
                 unit_symbols.append(list(clause.literals.keys()       )[0]       )
            else:
                 snake_case				,						snake_case					=       0, 0
                 for literal, value in clause.literals.items():
                      if value is False:
                           f_count += 1
                      elif value is None:
                           snake_case					=       literal
                           n_count += 1
                 if f_count == len(UpperCamelCase_       ) - 1 and n_count == 1:
                      unit_symbols.append(UpperCamelCase_       )
       snake_case					=       {}
       for i in unit_symbols:
            snake_case					=       i[:2]
            snake_case					=       len(UpperCamelCase_       ) == 2
       snake_case					=       [i[:2] for i in unit_symbols]
  
       return unit_symbols, assignment
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       True
       for clause in clauses:
            snake_case					=       clause.evaluate(UpperCamelCase_       )
            if clause_check is False:
                 return False, None
            elif clause_check is None:
                 snake_case					=       False
                 continue
  
       if check_clause_all_true:
            return True, model
  
       try:
            snake_case				,						snake_case					=       find_pure_symbols(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
       except RecursionError:
            print('''raises a RecursionError and is'''       )
            return None, {}
       snake_case					=       None
       if len(UpperCamelCase_       ) > 0:
            snake_case				,						snake_case					=       pure_symbols[0], assignment[pure_symbols[0]]
  
       if p:
            snake_case					=       model
            snake_case					=       value
            snake_case					=       list(UpperCamelCase_       )
            if p in tmp_symbols:
                 tmp_symbols.remove(UpperCamelCase_       )
            return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
       snake_case				,						snake_case					=       find_unit_clauses(UpperCamelCase_							,UpperCamelCase_       )
       snake_case					=       None
       if len(UpperCamelCase_       ) > 0:
            snake_case				,						snake_case					=       unit_symbols[0], assignment[unit_symbols[0]]
       if p:
            snake_case					=       model
            snake_case					=       value
            snake_case					=       list(UpperCamelCase_       )
            if p in tmp_symbols:
                 tmp_symbols.remove(UpperCamelCase_       )
            return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
       snake_case					=       symbols[0]
       snake_case					=       symbols[1:]
       snake_case				,						snake_case					=       model, model
       snake_case				,						snake_case					=       True, False
  
       return dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ) or dpll_algorithm(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
   _SCREAMING_SNAKE_CASE    :				Dict       =					generate_formula()
   print(f'''The formula {formula} is''', end=" ")
  
   _SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :				Any       =					generate_parameters(formula)
   _SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					dpll_algorithm(clauses, symbols, {})
  
   if solution:
    print(f'''satisfiable with the assignment {model}.''')
   else:
    print("not satisfiable.")
  
  
  
  
  