

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


_SCREAMING_SNAKE_CASE  :						Dict         =    Mapping[str, np.ndarray]
_SCREAMING_SNAKE_CASE  :						Any         =    Mapping[str, Any]  # Is a nested dict.
_SCREAMING_SNAKE_CASE  :						str         =    0.0_1






@dataclasses.dataclass(frozen=A__ )
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      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'.
  a     =      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.
  a     =      42  # [num_res, num_atom_type]

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

  # Chain indices for multi-chain predictions
  a     =      None

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

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

  # Chain corresponding to each parent
  a     =      None



def        UpperCAmelCase_     (		_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        UpperCAmelCase_     (		_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        UpperCAmelCase_     (		_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        UpperCAmelCase_     (		_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__					=					1.0_0
               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        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return residue_constants.STANDARD_ATOM_MASK[prot.aatype]







def        UpperCAmelCase_     (		_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   ,	)





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


_SCREAMING_SNAKE_CASE  :						Any         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						List[str]         =    {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "longformer"






  def __init__(							self		:	int			,     __lowerCamelCase		:	Union[List[int], int] = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 1			,     __lowerCamelCase		:	int = 0			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 3_0522			,     __lowerCamelCase		:	int = 768			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 3072			,     __lowerCamelCase		:	str = "gelu"			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	int = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	float = 0.02			,     __lowerCamelCase		:	float = 1e-12			,     __lowerCamelCase		:	bool = False			,     **__lowerCamelCase		:	Tuple			,     )	->							List[Any]:
       super().__init__(pad_token_id=__lowerCamelCase			,     **__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					attention_window
       SCREAMING_SNAKE_CASE__					=					sep_token_id
       SCREAMING_SNAKE_CASE__					=					bos_token_id
       SCREAMING_SNAKE_CASE__					=					eos_token_id
       SCREAMING_SNAKE_CASE__					=					vocab_size
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					max_position_embeddings
       SCREAMING_SNAKE_CASE__					=					type_vocab_size
       SCREAMING_SNAKE_CASE__					=					initializer_range
       SCREAMING_SNAKE_CASE__					=					layer_norm_eps
       SCREAMING_SNAKE_CASE__					=					onnx_export






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	Dict			,     __lowerCamelCase		:	"PretrainedConfig"			,     __lowerCamelCase		:	str = "default"			,     __lowerCamelCase		:	"List[PatchingSpec]" = None      )	->							List[str]:
       super().__init__(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					True






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
               ('''global_attention_mask''', dynamic_axis),
           ]      )






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							Mapping[str, Mapping[int, str]]:
       SCREAMING_SNAKE_CASE__					=					super().outputs
       if self.task == "default":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch'''}
       return outputs






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							float:
       return 1e-4






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							int:
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset			,     14      )







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	"PreTrainedTokenizerBase"			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	Optional[TensorType] = None			,     )	->							Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__					=					super().generate_dummy_inputs(
           preprocessor=__lowerCamelCase			,     batch_size=__lowerCamelCase			,     seq_length=__lowerCamelCase			,     is_pair=__lowerCamelCase			,     framework=__lowerCamelCase      )
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       SCREAMING_SNAKE_CASE__					=					torch.zeros_like(inputs['''input_ids''']      )
       # make every second token global
       SCREAMING_SNAKE_CASE__					=					1

       return inputs


