


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
 )








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.")




