

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		:Union[str, Any]						=     Mapping[str, np.ndarray]
SCREAMING_SNAKE_CASE		:Union[str, Any]						=     Mapping[str, Any]  # Is a nested dict.
SCREAMING_SNAKE_CASE		:Optional[int]						=     0.0_1






@dataclasses.dataclass(frozen=snake_case				)
class 						__magic_name__					:
     UpperCamelCase_	:np.ndarray  # [num_res, num_atom_type, 3]

     # Amino-acid type for each residue represented as an integer between 0 and
     # 20, where 20 is 'X'.
     UpperCamelCase_	:np.ndarray  # [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.
     UpperCamelCase_	:np.ndarray  # [num_res, num_atom_type]

     # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
     UpperCamelCase_	:np.ndarray  # [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.
     UpperCamelCase_	:np.ndarray  # [num_res, num_atom_type]

     # Chain indices for multi-chain predictions
     UpperCamelCase_	:Optional[np.ndarray]										=      None

     # Optional remark about the protein. Included as a comment in output PDB
     # files
     UpperCamelCase_	:Optional[str]										=      None

     # Templates used to generate this protein (prediction-only)
     UpperCamelCase_	:Optional[Sequence[str]]										=      None

     # Chain corresponding to each parent
     UpperCamelCase_	:Optional[Sequence[int]]										=      None




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Protein:



    """simple docstring"""






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

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

    assert aatype is not None

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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_ = 0			)->						List[str]:



    """simple docstring"""






    UpperCamelCase_			  =						[]

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

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

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

    pdb_headers.append(f"PARENT {' '.join(SCREAMING_SNAKE_CASE_			)}"			)

    return pdb_headers




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






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

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

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

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

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

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

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

            out_pdb_lines.append(make_parent_line(SCREAMING_SNAKE_CASE_			)			)

    return "\n".join(SCREAMING_SNAKE_CASE_			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    UpperCamelCase_			  =						residue_constants.restypes + ["X"]

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

    UpperCamelCase_			  =						residue_constants.atom_types

    UpperCamelCase_			  =						[]

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

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

    UpperCamelCase_			  =						get_pdb_headers(SCREAMING_SNAKE_CASE_			)
    if len(SCREAMING_SNAKE_CASE_			) > 0:
        pdb_lines.extend(SCREAMING_SNAKE_CASE_			)

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

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

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

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

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

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

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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						np.ndarray:



    """simple docstring"""






    return residue_constants.STANDARD_ATOM_MASK[prot.aatype]




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_ = None			,						SCREAMING_SNAKE_CASE_ = None			,						SCREAMING_SNAKE_CASE_ = None			,						SCREAMING_SNAKE_CASE_ = None			,						SCREAMING_SNAKE_CASE_ = None			,						)->						Protein:



    """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=SCREAMING_SNAKE_CASE_			,						remark=SCREAMING_SNAKE_CASE_			,						parents=SCREAMING_SNAKE_CASE_			,						parents_chain_index=SCREAMING_SNAKE_CASE_			,						)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str						=     ["""RemBertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     ["""RemBertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict						=     [
     """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RemBertForCausalLM""",
     """RemBertForMaskedLM""",
     """RemBertForMultipleChoice""",
     """RemBertForQuestionAnswering""",
     """RemBertForSequenceClassification""",
     """RemBertForTokenClassification""",
     """RemBertLayer""",
     """RemBertModel""",
     """RemBertPreTrainedModel""",
     """load_tf_weights_in_rembert""",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[int]						=     [
     """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFRemBertForCausalLM""",
     """TFRemBertForMaskedLM""",
     """TFRemBertForMultipleChoice""",
     """TFRemBertForQuestionAnswering""",
     """TFRemBertForSequenceClassification""",
     """TFRemBertForTokenClassification""",
     """TFRemBertLayer""",
     """TFRemBertModel""",
     """TFRemBertPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert import RemBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert_fast import RemBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rembert import (
      REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      RemBertForCausalLM,
      RemBertForMaskedLM,
      RemBertForMultipleChoice,
      RemBertForQuestionAnswering,
      RemBertForSequenceClassification,
      RemBertForTokenClassification,
      RemBertLayer,
      RemBertModel,
      RemBertPreTrainedModel,
      load_tf_weights_in_rembert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rembert import (
      TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRemBertForCausalLM,
      TFRemBertForMaskedLM,
      TFRemBertForMultipleChoice,
      TFRemBertForQuestionAnswering,
      TFRemBertForSequenceClassification,
      TFRemBertForTokenClassification,
      TFRemBertLayer,
      TFRemBertModel,
      TFRemBertPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
