'''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


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





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





   """simple docstring"""





   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    __UpperCAmelCase			(  a_:       Dict							):
   _UpperCAmelCase							:	Optional[int]							=   R'''(\[[A-Z]+\]\n)'''
   _UpperCAmelCase							:	List[str]							=   [tag.strip() for tag in re.split(a_,					a_							) if len(a_							) > 0]
   _UpperCAmelCase							:	Iterator[Tuple[str, List[str]]]							=   zip(tags[0::2],					[l.split("\n"							) for l in tags[1::2]]							)

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

   _UpperCAmelCase							:	Optional[int]							=   prot.remark
   if remark is not None:
      pdb_headers.append(f"""REMARK {remark}"""							)

   _UpperCAmelCase							:	Optional[int]							=   prot.parents
   _UpperCAmelCase							:	Union[str, Any]							=   prot.parents_chain_index
   if parents is not None and parents_chain_index is not None:
      _UpperCAmelCase							:	List[Any]							=   [p for i, p in zip(a_,					a_							) if i == chain_id]

   if parents is None or len(a_							) == 0:
      _UpperCAmelCase							:	Tuple							=   ['''N/A''']

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

   return pdb_headers






def    __UpperCAmelCase			(  a_:       Optional[Any],					a_:       Optional[int]							):
   _UpperCAmelCase							:	List[str]							=   []
   _UpperCAmelCase							:	Dict							=   pdb_str.split("\n"							)

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

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

         _UpperCAmelCase							:	Union[str, Any]							=   max([int(a_							) for chain_idx in parent_dict]							)
         for i in range(max_idx + 1							):
            _UpperCAmelCase							:	Union[str, Any]							=   parent_dict.get(str(a_							),					["N/A"]							)
            parents_per_chain.append(a_							)
      else:
         parents_per_chain.append(list(prot.parents							)							)
   else:
      _UpperCAmelCase							:	List[Any]							=   [['''N/A''']]

   def make_parent_line(a_:       List[Any]							) -> str:
      return f"""PARENT {' '.join(a_							)}"""

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

   _UpperCAmelCase							:	Optional[Any]							=   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_							):
            _UpperCAmelCase							:	List[str]							=   parents_per_chain[chain_counter]
         else:
            _UpperCAmelCase							:	Optional[int]							=   ['''N/A''']

         out_pdb_lines.append(make_parent_line(a_							)							)

   return "\n".join(a_							)






def    __UpperCAmelCase			(  a_:       str							):
   _UpperCAmelCase							:	Tuple							=   residue_constants.restypes + ['''X''']

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

   _UpperCAmelCase							:	Optional[int]							=   residue_constants.atom_types

   _UpperCAmelCase							:	List[str]							=   []

   _UpperCAmelCase							:	List[Any]							=   prot.atom_mask
   _UpperCAmelCase							:	List[str]							=   prot.aatype
   _UpperCAmelCase							:	Union[str, Any]							=   prot.atom_positions
   _UpperCAmelCase							:	Any							=   prot.residue_index.astype(np.intaa							)
   _UpperCAmelCase							:	List[str]							=   prot.b_factors
   _UpperCAmelCase							:	List[str]							=   prot.chain_index

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

   _UpperCAmelCase							:	int							=   get_pdb_headers(a_							)
   if len(a_							) > 0:
      pdb_lines.extend(a_							)

   _UpperCAmelCase							:	str							=   aatype.shape[0]
   _UpperCAmelCase							:	Optional[Any]							=   1
   _UpperCAmelCase							:	List[Any]							=   0
   _UpperCAmelCase							:	Any							=   string.ascii_uppercase
   _UpperCAmelCase							:	Any							=   None
   # Add all atom sites.
   for i in range(a_							):
      _UpperCAmelCase							:	List[Any]							=   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

         _UpperCAmelCase							:	str							=   '''ATOM'''
         _UpperCAmelCase							:	Tuple							=   atom_name if len(a_							) == 4 else f""" {atom_name}"""
         _UpperCAmelCase							:	Optional[Any]							=   ''''''
         _UpperCAmelCase							:	int							=   ''''''
         _UpperCAmelCase							:	int							=   1.00
         _UpperCAmelCase							:	int							=   atom_name[0]  # Protein supports only C, N, O, S, this works.
         _UpperCAmelCase							:	int							=   ''''''

         _UpperCAmelCase							:	Dict							=   '''A'''
         if chain_index is not None:
            _UpperCAmelCase							:	Union[str, Any]							=   chain_tags[chain_index[i]]

         # PDB is a columnar format, every space matters here!
         _UpperCAmelCase							:	str							=   (
             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

      _UpperCAmelCase							:	Dict							=   i == n - 1
      if chain_index is not None:
         if i != n - 1 and chain_index[i + 1] != prev_chain_index:
            _UpperCAmelCase							:	int							=   True
            _UpperCAmelCase							:	List[str]							=   chain_index[i + 1]

      if should_terminate:
         # Close the chain.
         _UpperCAmelCase							:	Optional[Any]							=   '''TER'''
         _UpperCAmelCase							:	Optional[int]							=   (
             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_:       Optional[Any]							):
   return residue_constants.STANDARD_ATOM_MASK[prot.aatype]






def    __UpperCAmelCase			(  a_:       List[Any],					a_:       List[Any],					a_:       Union[str, Any] = None,					a_:       List[str] = None,					a_:       str = None,					a_:       Optional[int] = None,					a_:       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=a_,					remark=a_,					parents=a_,					parents_chain_index=a_,					)
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{'''tokenizer_file''': '''tokenizer.json'''}

__lowercase							 =					{
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						int						=      VOCAB_FILES_NAMES
    a__	:						Tuple						=      PRETRAINED_VOCAB_FILES_MAP
    a__	:						List[str]						=      ["""input_ids""", """attention_mask"""]
    a__	:						int						=      None







    def __init__(	self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase="<unk>" , __lowercase="<s>" , __lowercase="</s>" , __lowercase="<pad>" , __lowercase=False , __lowercase=False , **__lowercase , )       ->	List[str]:
        super().__init__(
            __lowercase , __lowercase , tokenizer_file=__lowercase , unk_token=__lowercase , bos_token=__lowercase , eos_token=__lowercase , pad_token=__lowercase , add_prefix_space=__lowercase , clean_up_tokenization_spaces=__lowercase , **__lowercase , )
        __UpperCamelCase     :int			  =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('''add_prefix_space''' , __lowercase) != add_prefix_space:
            __UpperCamelCase     :Any			  =		getattr(__lowercase , pre_tok_state.pop('''type'''))
            __UpperCamelCase     :str			  =		add_prefix_space
            __UpperCamelCase     :List[str]			  =		pre_tok_class(**__lowercase)

        __UpperCamelCase     :Tuple			  =		add_prefix_space







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	BatchEncoding:
        __UpperCamelCase     :Tuple			  =		kwargs.get('''is_split_into_words''' , __lowercase)
        if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.''')

        return super()._batch_encode_plus(*__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	BatchEncoding:
        __UpperCamelCase     :List[str]			  =		kwargs.get('''is_split_into_words''' , __lowercase)

        if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.''')

        return super()._encode_plus(*__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	Tuple[str]:
        __UpperCamelCase     :Optional[Any]			  =		self._tokenizer.model.save(__lowercase , name=__lowercase)
        return tuple(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	List[int]:
        __UpperCamelCase     :str			  =		[]
        for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(__lowercase , add_special_tokens=__lowercase) + [self.eos_token_id])

        if len(__lowercase) > self.model_max_length:
            __UpperCamelCase     :Any			  =		input_ids[-self.model_max_length :]
        return input_ids





