

'''simple docstring'''



import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


UpperCAmelCase					:			int       =	Mapping[str, np.ndarray]
UpperCAmelCase					:			int       =	Mapping[str, Any]  # Is a nested dict.
UpperCAmelCase					:			List[Any]       =	0.0_1







@dataclasses.dataclass(frozen=a   )
class 		lowerCAmelCase__  :



 """simple docstring"""



 lowerCAmelCase__       = 42  # [num_res, num_atom_type, 3]

 # Amino-acid type for each residue represented as an integer between 0 and
 # 20, where 20 is 'X'.
 lowerCAmelCase__       = 42  # [num_res]

 # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
 # is present and 0.0 if not. This should be used for loss masking.
 lowerCAmelCase__       = 42  # [num_res, num_atom_type]

 # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
 lowerCAmelCase__       = 42  # [num_res]

 # B-factors, or temperature factors, of each residue (in sq. angstroms units),
 # representing the displacement of the residue from its ground truth mean
 # value.
 lowerCAmelCase__       = 42  # [num_res, num_atom_type]

 # Chain indices for multi-chain predictions
 lowerCAmelCase__       = None

 # Optional remark about the protein. Included as a comment in output PDB
 # files
 lowerCAmelCase__       = None

 # Templates used to generate this protein (prediction-only)
 lowerCAmelCase__       = None

 # Chain corresponding to each parent
 lowerCAmelCase__       = None







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = R"""(\[[A-Z]+\]\n)"""
     __SCREAMING_SNAKE_CASE      = [tag.strip() for tag in re.split(a__ ,       a__		) if len(a__		) > 0]
     __SCREAMING_SNAKE_CASE      = zip(tags[0::2] ,       [l.split("""\n"""		) for l in tags[1::2]]		)

     __SCREAMING_SNAKE_CASE      = ["N", "CA", "C"]
     __SCREAMING_SNAKE_CASE      = None
     __SCREAMING_SNAKE_CASE      = None
     __SCREAMING_SNAKE_CASE      = None
     for g in groups:
          if "[PRIMARY]" == g[0]:
               __SCREAMING_SNAKE_CASE      = g[1][0].strip()
               for i in range(len(a__		)		):
                    if seq[i] not in residue_constants.restypes:
                         __SCREAMING_SNAKE_CASE      = """X"""  # FIXME: strings are immutable
               __SCREAMING_SNAKE_CASE      = np.array(
                   [residue_constants.restype_order.get(a__ ,       residue_constants.restype_num		) for res_symbol in seq]		)
          elif "[TERTIARY]" == g[0]:
               __SCREAMING_SNAKE_CASE      = []
               for axis in range(3		):
                    tertiary.append(list(map(a__ ,       g[1][axis].split()		)		)		)
               __SCREAMING_SNAKE_CASE      = np.array(a__		)
               __SCREAMING_SNAKE_CASE      = np.zeros((len(tertiary[0]		) // 3, residue_constants.atom_type_num, 3)		).astype(np.floataa		)
               for i, atom in enumerate(a__		):
                    __SCREAMING_SNAKE_CASE      = np.transpose(tertiary_np[:, i::3]		)
               atom_positions *= PICO_TO_ANGSTROM
          elif "[MASK]" == g[0]:
               __SCREAMING_SNAKE_CASE      = np.array(list(map({"""-""": 0, """+""": 1}.get ,       g[1][0].strip()		)		)		)
               __SCREAMING_SNAKE_CASE      = np.zeros(
                   (
                       len(a__		),
                       residue_constants.atom_type_num,
                   )		).astype(np.floataa		)
               for i, atom in enumerate(a__		):
                    __SCREAMING_SNAKE_CASE      = 1
               atom_mask *= mask[..., None]

     assert aatype is not None

     return Protein(
         atom_positions=a__ ,       atom_mask=a__ ,       aatype=a__ ,       residue_index=np.arange(len(a__		)		) ,       b_factors=a__ ,       )







def   a__			(    a__ ,       a__ = 0		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []

     __SCREAMING_SNAKE_CASE      = prot.remark
     if remark is not None:
          pdb_headers.append(F'REMARK {remark}'		)

     __SCREAMING_SNAKE_CASE      = prot.parents
     __SCREAMING_SNAKE_CASE      = prot.parents_chain_index
     if parents is not None and parents_chain_index is not None:
          __SCREAMING_SNAKE_CASE      = [p for i, p in zip(a__ ,       a__		) if i == chain_id]

     if parents is None or len(a__		) == 0:
          __SCREAMING_SNAKE_CASE      = ["""N/A"""]

     pdb_headers.append(F'PARENT {" ".join(a__		)}'		)

     return pdb_headers







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []
     __SCREAMING_SNAKE_CASE      = pdb_str.split("""\n"""		)

     __SCREAMING_SNAKE_CASE      = prot.remark
     if remark is not None:
          out_pdb_lines.append(F'REMARK {remark}'		)

     __SCREAMING_SNAKE_CASE      = 42
     if prot.parents is not None and len(prot.parents		) > 0:
          __SCREAMING_SNAKE_CASE      = []
          if prot.parents_chain_index is not None:
               __SCREAMING_SNAKE_CASE      = {}
               for p, i in zip(prot.parents ,       prot.parents_chain_index		):
                    parent_dict.setdefault(str(a__		) ,       []		)
                    parent_dict[str(a__		)].append(a__		)

               __SCREAMING_SNAKE_CASE      = max([int(a__		) for chain_idx in parent_dict]		)
               for i in range(max_idx + 1		):
                    __SCREAMING_SNAKE_CASE      = parent_dict.get(str(a__		) ,       ["""N/A"""]		)
                    parents_per_chain.append(a__		)
          else:
               parents_per_chain.append(list(prot.parents		)		)
     else:
          __SCREAMING_SNAKE_CASE      = [["""N/A"""]]

     def make_parent_line(a__		) -> str:
          return F'PARENT {" ".join(a__		)}'

     out_pdb_lines.append(make_parent_line(parents_per_chain[0]		)		)

     __SCREAMING_SNAKE_CASE      = 0
     for i, l in enumerate(a__		):
          if "PARENT" not in l and "REMARK" not in l:
               out_pdb_lines.append(a__		)
          if "TER" in l and "END" not in lines[i + 1]:
               chain_counter += 1
               if not chain_counter >= len(a__		):
                    __SCREAMING_SNAKE_CASE      = parents_per_chain[chain_counter]
               else:
                    __SCREAMING_SNAKE_CASE      = ["""N/A"""]

               out_pdb_lines.append(make_parent_line(a__		)		)

     return "\n".join(a__		)







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = residue_constants.restypes + ["""X"""]

     def res_atoa(a__		) -> str:
          return residue_constants.restype_atoa.get(restypes[r] ,       """UNK"""		)

     __SCREAMING_SNAKE_CASE      = residue_constants.atom_types

     __SCREAMING_SNAKE_CASE      = []

     __SCREAMING_SNAKE_CASE      = prot.atom_mask
     __SCREAMING_SNAKE_CASE      = prot.aatype
     __SCREAMING_SNAKE_CASE      = prot.atom_positions
     __SCREAMING_SNAKE_CASE      = prot.residue_index.astype(np.intaa		)
     __SCREAMING_SNAKE_CASE      = prot.b_factors
     __SCREAMING_SNAKE_CASE      = prot.chain_index

     if np.any(aatype > residue_constants.restype_num		):
          raise ValueError("""Invalid aatypes."""		)

     __SCREAMING_SNAKE_CASE      = get_pdb_headers(a__		)
     if len(a__		) > 0:
          pdb_lines.extend(a__		)

     __SCREAMING_SNAKE_CASE      = aatype.shape[0]
     __SCREAMING_SNAKE_CASE      = 1
     __SCREAMING_SNAKE_CASE      = 0
     __SCREAMING_SNAKE_CASE      = string.ascii_uppercase
     __SCREAMING_SNAKE_CASE      = None
     # Add all atom sites.
     for i in range(a__		):
          __SCREAMING_SNAKE_CASE      = res_atoa(aatype[i]		)
          for atom_name, pos, mask, b_factor in zip(a__ ,       atom_positions[i] ,       atom_mask[i] ,       b_factors[i]		):
               if mask < 0.5:
                    continue

               __SCREAMING_SNAKE_CASE      = """ATOM"""
               __SCREAMING_SNAKE_CASE      = atom_name if len(a__		) == 4 else F' {atom_name}'
               __SCREAMING_SNAKE_CASE      = """"""
               __SCREAMING_SNAKE_CASE      = """"""
               __SCREAMING_SNAKE_CASE      = 1.00
               __SCREAMING_SNAKE_CASE      = atom_name[0]  # Protein supports only C, N, O, S, this works.
               __SCREAMING_SNAKE_CASE      = """"""

               __SCREAMING_SNAKE_CASE      = """A"""
               if chain_index is not None:
                    __SCREAMING_SNAKE_CASE      = chain_tags[chain_index[i]]

               # PDB is a columnar format, every space matters here!
               __SCREAMING_SNAKE_CASE      = (
                   F'{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'
                   F'{res_name_a:>3} {chain_tag:>1}'
                   F'{residue_index[i]:>4}{insertion_code:>1}   '
                   F'{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'
                   F'{occupancy:>6.2f}{b_factor:>6.2f}          '
                   F'{element:>2}{charge:>2}'
               )
               pdb_lines.append(a__		)
               atom_index += 1

          __SCREAMING_SNAKE_CASE      = i == n - 1
          if chain_index is not None:
               if i != n - 1 and chain_index[i + 1] != prev_chain_index:
                    __SCREAMING_SNAKE_CASE      = True
                    __SCREAMING_SNAKE_CASE      = chain_index[i + 1]

          if should_terminate:
               # Close the chain.
               __SCREAMING_SNAKE_CASE      = """TER"""
               __SCREAMING_SNAKE_CASE      = (
                   F'{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]		):>3} {chain_tag:>1}{residue_index[i]:>4}'
               )
               pdb_lines.append(a__		)
               atom_index += 1

               if i != n - 1:
                    # "prev" is a misnomer here. This happens at the beginning of
                    # each new chain.
                    pdb_lines.extend(get_pdb_headers(a__ ,       a__		)		)

     pdb_lines.append("""END"""		)
     pdb_lines.append(""""""		)
     return "\n".join(a__		)







def   a__			(    a__		):




     """simple docstring"""


     return residue_constants.STANDARD_ATOM_MASK[prot.aatype]







def   a__			(    a__ ,       a__ ,       a__ = None ,       a__ = None ,       a__ = None ,       a__ = None ,       a__ = None ,       ):




     """simple docstring"""


     return Protein(
         aatype=features["""aatype"""] ,       atom_positions=result["""final_atom_positions"""] ,       atom_mask=result["""final_atom_mask"""] ,       residue_index=features["""residue_index"""] + 1 ,       b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""]		) ,       chain_index=a__ ,       remark=a__ ,       parents=a__ ,       parents_chain_index=a__ ,       )









'''simple docstring'''



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
  raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
  raise Exception('requires fairseq < v2')

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

UpperCAmelCase					:			Tuple       =	'Hello, World!'
UpperCAmelCase					:			Any       =	'en_XX'







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Path("""data_bin"""		)
     __SCREAMING_SNAKE_CASE      = FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(a__		).parent		) ,       checkpoint_file=Path(a__		).name ,       _name="""xmod_base""" ,       arch="""xmod_base""" ,       task="""multilingual_masked_lm""" ,       data_name_or_path=str(a__		) ,       bpe="""sentencepiece""" ,       sentencepiece_model=str(Path(a__		).parent / """sentencepiece.bpe.model"""		) ,       src_dict=str(data_dir / """dict.txt"""		) ,       )
     xmod.eval()  # disable dropout
     print(a__		)

     __SCREAMING_SNAKE_CASE      = xmod.model.encoder.sentence_encoder
     __SCREAMING_SNAKE_CASE      = XmodConfig(
         vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,       hidden_size=xmod.cfg.model.encoder_embed_dim ,       num_hidden_layers=xmod.cfg.model.encoder_layers ,       num_attention_heads=xmod.cfg.model.encoder_attention_heads ,       intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,       max_position_embeddings=5_14 ,       type_vocab_size=1 ,       layer_norm_eps=1E-5 ,       pre_norm=xmod.cfg.model.encoder_normalize_before ,       adapter_reduction_factor=getattr(xmod.cfg.model ,       """bottleneck""" ,       2		) ,       adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,       adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,       ln_before_adapter=xmod.cfg.model.ln_before_adapter ,       languages=xmod.cfg.model.languages ,       )
     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

     print("""Our X-MOD config:""" ,       a__		)

     __SCREAMING_SNAKE_CASE      = XmodForSequenceClassification(a__		) if classification_head else XmodForMaskedLM(a__		)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.embed_tokens.weight
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.embed_positions.weight
     __SCREAMING_SNAKE_CASE      = torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c xmod doesn't use them.

     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.weight
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers		):
          # Encoder: start of layer
          __SCREAMING_SNAKE_CASE      = model.roberta.encoder.layer[i]
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layers[i]

          # self attention
          __SCREAMING_SNAKE_CASE      = layer.attention.self
          if not (
              xmod_layer.self_attn.k_proj.weight.data.shape
              == xmod_layer.self_attn.q_proj.weight.data.shape
              == xmod_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)		)
          ):
               raise AssertionError("""Dimensions of self-attention weights do not match."""		)

          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.q_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.q_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.k_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.k_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.v_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.v_proj.bias

          # self-attention output
          __SCREAMING_SNAKE_CASE      = layer.attention.output
          if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
               raise AssertionError("""Dimensions of self-attention output weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.out_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.out_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn_layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn_layer_norm.bias

          # intermediate
          __SCREAMING_SNAKE_CASE      = layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of intermediate weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.bias

          # output
          __SCREAMING_SNAKE_CASE      = layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of feed-forward weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.final_layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_layer_norm.weight
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_layer_norm.bias

          if sorted(bert_output.adapter_modules.keys()		) != sorted(xmod_layer.adapter_modules.keys()		):
               raise AssertionError("""Lists of language adapters do not match."""		)
          for lang_code, adapter in xmod_layer.adapter_modules.items():
               __SCREAMING_SNAKE_CASE      = bert_output.adapter_modules[lang_code]
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_modules[lang_code]
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.weight
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.bias
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.weight
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layer_norm.bias

     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].dense.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].dense.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.bias
     else:
          # LM Head
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.dense.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.dense.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.layer_norm.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     __SCREAMING_SNAKE_CASE      = xmod.encode(a__		).unsqueeze(0		)  # batch of size 1
     model.roberta.set_default_language(a__		)

     __SCREAMING_SNAKE_CASE      = model(a__		)[0]
     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""](xmod.extract_features(a__		)		)
     else:
          __SCREAMING_SNAKE_CASE      = xmod.model(a__ ,       lang_id=[SAMPLE_LANGUAGE]		)[0]
     print(our_output.shape ,       their_output.shape		)
     __SCREAMING_SNAKE_CASE      = torch.max(torch.abs(our_output - their_output		)		).item()
     print(F'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
     __SCREAMING_SNAKE_CASE      = torch.allclose(a__ ,       a__ ,       atol=1E-3		)
     print("""Do both models output the same tensors?""" ,       """🔥""" if success else """💩"""		)
     if not success:
          raise Exception("""Something went wRoNg"""		)

     Path(a__		).mkdir(parents=a__ ,       exist_ok=a__		)
     print(F'Saving model to {pytorch_dump_folder_path}'		)
     model.save_pretrained(a__		)


if __name__ == "__main__":
  UpperCAmelCase					:			Tuple       =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, 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.'
  )
  UpperCAmelCase					:			Optional[int]       =	parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )






