
'''simple docstring'''

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()
lowerCAmelCase_			:			Union[str, Any]			   =     logging.get_logger(__name__)

lowerCAmelCase_			:			List[str]			   =     [
    (
        '''protein1''',
        '''MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA''',
    ),
    ('''protein2''', '''MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'''),
    ('''protein3''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'''),
    ('''protein4''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'''),
]

lowerCAmelCase_			:			List[str]			   =     {
    '''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,
}

lowerCAmelCase_			:			List[Any]			   =     list('''ARNDCQEGHILKMFPSTWYV''')

lowerCAmelCase_			:			Union[str, Any]			   =     restypes + ['''X''']
lowerCAmelCase_			:			List[str]			   =     restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']







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







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_       ):
     _UpperCAmelCase					:  Dict         =    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  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_       ):
     if model.startswith("""esmfold"""       ):
          _UpperCAmelCase					:  List[Any]         =    MODEL_MAPPING[model]()
     else:
          _UpperCAmelCase  ,     _UpperCAmelCase					:  int         =    MODEL_MAPPING[model]()
     esm.eval()  # disable dropout

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

     if is_folding_model:
          _UpperCAmelCase					:  int         =    esm.esm.alphabet
     _UpperCAmelCase					:  int         =    tuple(alphabet.all_toks       )
     _UpperCAmelCase					:  Dict         =    alphabet.mask_idx
     _UpperCAmelCase					:  Dict         =    alphabet.padding_idx

     if is_folding_model:
          _UpperCAmelCase					:  Dict         =    esm.esm
     else:
          _UpperCAmelCase					:  Any         =    esm

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

     if model.startswith("""esmfold"""       ):
          _UpperCAmelCase					:  int         =    EsmForProteinFolding
     elif classification_head:
          _UpperCAmelCase					:  Optional[int]         =    EsmForSequenceClassification
     else:
          _UpperCAmelCase					:  Dict         =    EsmForMaskedLM
     _UpperCAmelCase					:  List[Any]         =    model_class(lowerCAmelCase_       )
     model.eval()

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

     if config.emb_layer_norm_before:
          _UpperCAmelCase					:  int         =    original_esm_model.emb_layer_norm_before.weight
          _UpperCAmelCase					:  Tuple         =    original_esm_model.emb_layer_norm_before.bias

     _UpperCAmelCase					:  Optional[int]         =    original_esm_model.emb_layer_norm_after.weight
     _UpperCAmelCase					:  List[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					:  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					:  Optional[int]         =    esm_layer.self_attn.q_proj.weight
          _UpperCAmelCase					:  Dict         =    esm_layer.self_attn.q_proj.bias
          _UpperCAmelCase					:  Optional[Any]         =    esm_layer.self_attn.k_proj.weight
          _UpperCAmelCase					:  Union[str, Any]         =    esm_layer.self_attn.k_proj.bias
          _UpperCAmelCase					:  List[Any]         =    esm_layer.self_attn.v_proj.weight
          _UpperCAmelCase					:  Optional[Any]         =    esm_layer.self_attn.v_proj.bias

          if getattr(esm_layer.self_attn ,       """rot_emb""" ,       lowerCAmelCase_       ) 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					:  Any         =    esm_layer.self_attn.rot_emb.inv_freq

          # LayerNorm changes for pre-activation
          _UpperCAmelCase					:  Dict         =    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					:  Optional[int]         =    esm_layer.self_attn.out_proj.weight
          _UpperCAmelCase					:  str         =    esm_layer.self_attn.out_proj.bias

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

     if is_folding_model:
          _UpperCAmelCase					:  Optional[int]         =    esm.esm_s_combine.data
          _UpperCAmelCase					:  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					:  Tuple         =    esm.esm.classification_heads["""mnli"""].dense.weight
          _UpperCAmelCase					:  Dict         =    esm.classification_heads["""mnli"""].dense.bias
          _UpperCAmelCase					:  List[str]         =    esm.classification_heads["""mnli"""].out_proj.weight
          _UpperCAmelCase					:  List[str]         =    esm.classification_heads["""mnli"""].out_proj.bias
     else:
          # LM Head
          _UpperCAmelCase					:  Union[str, Any]         =    esm.lm_head.dense.weight
          _UpperCAmelCase					:  Optional[Any]         =    esm.lm_head.dense.bias
          _UpperCAmelCase					:  Optional[Any]         =    esm.lm_head.layer_norm.weight
          _UpperCAmelCase					:  Tuple         =    esm.lm_head.layer_norm.bias
          _UpperCAmelCase					:  Tuple         =    esm.lm_head.weight
          _UpperCAmelCase					:  Any         =    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					:  Optional[Any]         =    SAMPLE_DATA[:2]
     else:
          _UpperCAmelCase					:  Optional[Any]         =    SAMPLE_DATA

     if is_folding_model:
          _UpperCAmelCase					:  int         =    get_esmfold_tokenizer()
          _UpperCAmelCase					:  Optional[int]         =    hf_tokenizer(
              [row[1] for row in sample_data] ,       return_tensors="""pt""" ,       padding=lowerCAmelCase_ ,       add_special_tokens=lowerCAmelCase_       )
          _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  Dict         =    esmfold_encode_sequences([row[1] for row in sample_data]       )
          _UpperCAmelCase					:  Union[str, Any]         =    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					:  Optional[Any]         =    alphabet.get_batch_converter()
          _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  Optional[int]         =    batch_converter(lowerCAmelCase_       )
          # Prepare tokenizer and make sure it matches
          with TemporaryDirectory() as tempdir:
               _UpperCAmelCase					:  Union[str, Any]         =    """\n""".join(alphabet.all_toks       )
               _UpperCAmelCase					:  List[str]         =    Path(lowerCAmelCase_       ) / """vocab.txt"""
               vocab_file.write_text(lowerCAmelCase_       )
               _UpperCAmelCase					:  Any         =    EsmTokenizer(vocab_file=str(lowerCAmelCase_       )       )

          _UpperCAmelCase					:  Union[str, Any]         =    hf_tokenizer([row[1] for row in sample_data] ,       return_tensors="""pt""" ,       padding=lowerCAmelCase_       )
          _UpperCAmelCase					:  Dict         =    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					:  Union[str, Any]         =    esm.cuda().infer([row[1] for row in sample_data]       )
               _UpperCAmelCase					:  List[str]         =    model.cuda()(
                   input_ids=hf_tokens["""input_ids"""].cuda() ,       attention_mask=hf_tokens["""attention_mask"""].cuda()       )
          else:
               _UpperCAmelCase					:  Any         =    model(**lowerCAmelCase_ ,       output_hidden_states=lowerCAmelCase_       )
               _UpperCAmelCase					:  List[str]         =    our_output["""logits"""]
               if classification_head:
                    _UpperCAmelCase					:  Tuple         =    esm.model.classification_heads["""mnli"""](esm.extract_features(lowerCAmelCase_       )       )
               else:
                    _UpperCAmelCase					:  Optional[Any]         =    esm(hf_tokens["""input_ids"""] ,       repr_layers=list(range(999       )       )       )
                    _UpperCAmelCase					:  int         =    their_output["""logits"""]

          if is_folding_model:
               _UpperCAmelCase					:  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					:  List[Any]         =    torch.allclose(lowerCAmelCase_ ,       lowerCAmelCase_ ,       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					:  Tuple         =    esm.predict_contacts(hf_tokens["""input_ids"""]       )
               _UpperCAmelCase					:  Optional[int]         =    torch.max(torch.abs(our_output - their_output       )       ).item()
               _UpperCAmelCase					:  Optional[int]         =    torch.allclose(lowerCAmelCase_ ,       lowerCAmelCase_ ,       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(lowerCAmelCase_       ).mkdir(parents=lowerCAmelCase_ ,       exist_ok=lowerCAmelCase_       )
          print(f"Saving model to {pytorch_dump_folder_path}"       )
          model.save_pretrained(lowerCAmelCase_       )

          del esm  # Free up some memory before continuing

     print(f"Saving tokenizer to {pytorch_dump_folder_path}"       )
     hf_tokenizer.save_pretrained(lowerCAmelCase_       )

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


if __name__ == "__main__":
   lowerCAmelCase_			:			Any			   =     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.''')
   lowerCAmelCase_			:			Union[str, Any]			   =     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
   )




'''simple docstring'''

from itertools import combinations, product







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_       ):
     if xa == ya == 0 or xa == ya == 0:
          return False
     _UpperCAmelCase					:  Any         =    xa * xa + ya * ya
     _UpperCAmelCase					:  Any         =    xa * xa + ya * ya
     _UpperCAmelCase					:  Tuple         =    (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
     return (
         a_square + b_square == c_square
         or a_square + c_square == b_square
         or b_square + c_square == a_square
     )







def __A  (    lowerCAmelCase_ = 50       ):
     return sum(
         1
         for pta, pta in combinations(product(range(limit + 1       ) ,       repeat=2       ) ,       2       )
         if is_right(*lowerCAmelCase_ ,       *lowerCAmelCase_       )       )


if __name__ == "__main__":
   print(F"{solution() = }")


