



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


a_			=					Mapping[str, np.ndarray]
a_			=					Mapping[str, Any]  # Is a nested dict.
a_			=					0.01



@dataclasses.dataclass(frozen=lowerCAmelCase__   )
class  __lowerCAmelCase     :
 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__			(		_UpperCamelCase			: str							):
   __lowerCamelCase													=    R'''(\[[A-Z]+\]\n)'''
   __lowerCamelCase													=    [tag.strip() for tag in re.split(_UpperCamelCase ,_UpperCamelCase							) if len(_UpperCamelCase							) > 0]
   __lowerCamelCase													=    zip(tags[0::2] ,[l.split('''\n'''							) for l in tags[1::2]]							)

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

   assert aatype is not None

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





def 		a__			(		_UpperCamelCase			: Protein ,_UpperCamelCase			: int = 0							):
   __lowerCamelCase													=    []

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

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

   if parents is None or len(_UpperCamelCase							) == 0:
      __lowerCamelCase													=    ['''N/A''']

   pdb_headers.append(F"""PARENT {" ".join(_UpperCamelCase							)}"""							)

   return pdb_headers





def 		a__			(		_UpperCamelCase			: Protein ,_UpperCamelCase			: str							):
   __lowerCamelCase													=    []
   __lowerCamelCase													=    pdb_str.split('''\n'''							)

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

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

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

   def make_parent_line(_UpperCamelCase			: Sequence[str]							) -> str:
      return F"""PARENT {" ".join(_UpperCamelCase							)}"""

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

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

         out_pdb_lines.append(make_parent_line(_UpperCamelCase							)							)

   return "\n".join(_UpperCamelCase							)





def 		a__			(		_UpperCamelCase			: Protein							):
   __lowerCamelCase													=    residue_constants.restypes + ['''X''']

   def res_atoa(_UpperCamelCase			: int							) -> str:
      return residue_constants.restype_atoa.get(restypes[r] ,'''UNK'''							)

   __lowerCamelCase													=    residue_constants.atom_types

   __lowerCamelCase													=    []

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

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

   __lowerCamelCase													=    get_pdb_headers(_UpperCamelCase							)
   if len(_UpperCamelCase							) > 0:
      pdb_lines.extend(_UpperCamelCase							)

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

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

         __lowerCamelCase													=    '''A'''
         if chain_index is not None:
            __lowerCamelCase													=    chain_tags[chain_index[i]]

         # PDB is a columnar format, every space matters here!
         __lowerCamelCase													=    (
             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(_UpperCamelCase							)
         atom_index += 1

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

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

   pdb_lines.append('''END'''							)
   pdb_lines.append(''''''							)
   return "\n".join(_UpperCamelCase							)





def 		a__			(		_UpperCamelCase			: Protein							):
   return residue_constants.STANDARD_ATOM_MASK[prot.aatype]





def 		a__			(		_UpperCamelCase			: FeatureDict ,_UpperCamelCase			: ModelOutput ,_UpperCamelCase			: Optional[np.ndarray] = None ,_UpperCamelCase			: Optional[np.ndarray] = None ,_UpperCamelCase			: Optional[str] = None ,_UpperCamelCase			: Optional[Sequence[str]] = None ,_UpperCamelCase			: Optional[Sequence[int]] = None ,):
   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=_UpperCamelCase ,remark=_UpperCamelCase ,parents=_UpperCamelCase ,parents_chain_index=_UpperCamelCase ,)











import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=3.6				):



    '''simple docstring'''
    __lowerCamelCase													=    tokenizer
    __lowerCamelCase													=    tokenizer.bos_token_id
    __lowerCamelCase													=    dataset
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    seq_length * chars_per_token * num_of_sequences




 def __iter__(						self				):



    '''simple docstring'''
    __lowerCamelCase													=    iter(self.dataset				)
    __lowerCamelCase													=    True
    while more_examples:
       __lowerCamelCase							,__lowerCamelCase													=    [], 0
       while True:
          if buffer_len >= self.input_characters:
             break
          try:
             buffer.append(next(__UpperCAmelCase				)['''content''']				)
             buffer_len += len(buffer[-1]				)
          except StopIteration:
             __lowerCamelCase													=    False
             break
       __lowerCamelCase													=    tokenizer(__UpperCAmelCase	,				truncation=__UpperCAmelCase				)['''input_ids''']
       __lowerCamelCase													=    []
       for tokenized_input in tokenized_inputs:
          all_token_ids.extend(tokenized_input + [self.concat_token_id]				)
       for i in range(0	,				len(__UpperCAmelCase				)	,				self.seq_length				):
          __lowerCamelCase													=    all_token_ids[i : i + self.seq_length]
          if len(__UpperCAmelCase				) == self.seq_length:
             yield torch.tensor(__UpperCAmelCase				)





def 		a__			(		_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    {'''streaming''': True}
   __lowerCamelCase													=    load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase							)
   __lowerCamelCase													=    ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=args.batch_size							)
   return eval_dataloader





def 		a__			(		_UpperCamelCase			: str							):
   model.eval()
   __lowerCamelCase													=    []
   for step, batch in enumerate(_UpperCamelCase							):
      with torch.no_grad():
         __lowerCamelCase													=    model(_UpperCamelCase ,labels=_UpperCamelCase							)
      __lowerCamelCase													=    outputs.loss.repeat(args.batch_size							)
      losses.append(accelerator.gather(_UpperCamelCase							)							)

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   __lowerCamelCase													=    torch.mean(torch.cat(_UpperCamelCase							)							)
   try:
      __lowerCamelCase													=    torch.exp(_UpperCamelCase							)
   except OverflowError:
      __lowerCamelCase													=    float('''inf'''							)
   return loss.item(), perplexity.item()


# Setup Accelerator
a_			=					Accelerator()

# Parse configuration
a_			=					HfArgumentParser(EvaluationArguments)
a_			=					parser.parse_args()
set_seed(args.seed)

# Logging
a_			=					logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
a_			=					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_			=					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a_			=					create_dataloader(args)

# Prepare everything with our `accelerator`.
a_		,						a_			=					accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
a_		,						a_			=					evaluate(args)





logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")






