"""simple docstring"""

import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase       =  logging.get_logger(__name__)

UpperCAmelCase       =  {"""vocab_file""": """spiece.model"""}

UpperCAmelCase       =  {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

UpperCAmelCase       =  {
    """google/bigbird-roberta-base""": 4_096,
    """google/bigbird-roberta-large""": 4_096,
    """google/bigbird-base-trivia-itc""": 4_096,
}







class      UpperCAmelCase_       (						_UpperCAmelCase):
							snake_case__        =      VOCAB_FILES_NAMES
							snake_case__        =      PRETRAINED_VOCAB_FILES_MAP
							snake_case__        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case__        =      ['''input_ids''', '''attention_mask''']
							snake_case__        =      []






							def __init__(		self :      List[str]     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      str="<unk>"     ,						__UpperCamelCase :      Tuple="<s>"     ,						__UpperCamelCase :      int="</s>"     ,						__UpperCamelCase :      List[str]="<pad>"     ,						__UpperCamelCase :      Tuple="[SEP]"     ,						__UpperCamelCase :      List[str]="[MASK]"     ,						__UpperCamelCase :      Any="[CLS]"     ,						__UpperCamelCase :      Union[str, Any] = None     ,						**__UpperCamelCase :      Optional[int]     ,						)       ->       None:
												_UpperCamelCase      						=	AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_  ) if isinstance(A_     ,						A_  ) else bos_token
												_UpperCamelCase      						=	AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_  ) if isinstance(A_     ,						A_  ) else eos_token
												_UpperCamelCase      						=	AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_  ) if isinstance(A_     ,						A_  ) else unk_token
												_UpperCamelCase      						=	AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_  ) if isinstance(A_     ,						A_  ) else pad_token
												_UpperCamelCase      						=	AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_  ) if isinstance(A_     ,						A_  ) else cls_token
												_UpperCamelCase      						=	AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_  ) if isinstance(A_     ,						A_  ) else sep_token

												# Mask token behave like a normal word, i.e. include the space before it
												_UpperCamelCase      						=	AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_  ) if isinstance(A_     ,						A_  ) else mask_token

												_UpperCamelCase      						=	{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=A_     ,						eos_token=A_     ,						unk_token=A_     ,						pad_token=A_     ,						sep_token=A_     ,						mask_token=A_     ,						cls_token=A_     ,						sp_model_kwargs=self.sp_model_kwargs     ,						**A_     ,						)

												_UpperCamelCase      						=	vocab_file

												_UpperCamelCase      						=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
												self.sp_model.Load(A_  )






							@property
							def       _UpperCamelCase			(		self :      Dict  )       ->       str:
												return self.sp_model.get_piece_size()






							def       _UpperCamelCase			(		self :      List[Any]  )       ->       List[str]:
												_UpperCamelCase      						=	{self.convert_ids_to_tokens(A_  ): i for i in range(self.vocab_size  )}
												vocab.update(self.added_tokens_encoder  )
												return vocab






							def __getstate__(		self :      Any  )       ->       str:
												_UpperCamelCase      						=	self.__dict__.copy()
												_UpperCamelCase      						=	None
												return state






							def __setstate__(		self :      int     ,						__UpperCamelCase :      str  )       ->       Optional[Any]:
												_UpperCamelCase      						=	d

												# for backward compatibility
												if not hasattr(self     ,						'''sp_model_kwargs'''  ):
																	_UpperCamelCase      						=	{}

												_UpperCamelCase      						=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
												self.sp_model.Load(self.vocab_file  )






							def       _UpperCamelCase			(		self :      str     ,						__UpperCamelCase :      int  )       ->       List[str]:
												return self.sp_model.encode(A_     ,						out_type=A_  )






							def       _UpperCamelCase			(		self :      Union[str, Any]     ,						__UpperCamelCase :      List[Any]  )       ->       str:
												return self.sp_model.piece_to_id(A_  )






							def       _UpperCamelCase			(		self :      Any     ,						__UpperCamelCase :      Optional[int]  )       ->       str:
												_UpperCamelCase      						=	self.sp_model.IdToPiece(A_  )
												return token






							def       _UpperCamelCase			(		self :      str     ,						__UpperCamelCase :      Any  )       ->       Tuple:
												_UpperCamelCase      						=	[]
												_UpperCamelCase      						=	''''''
												_UpperCamelCase      						=	False
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						if not prev_is_special:
																											out_string += " "
																						out_string += self.sp_model.decode(A_  ) + token
																						_UpperCamelCase      						=	True
																						_UpperCamelCase      						=	[]
																	else:
																						current_sub_tokens.append(A_  )
																						_UpperCamelCase      						=	False
												out_string += self.sp_model.decode(A_  )
												return out_string.strip()






							def       _UpperCamelCase			(		self :      Optional[int]     ,						__UpperCamelCase :      Optional[Any]     ,						__UpperCamelCase :      Any = False     ,						__UpperCamelCase :      int = None     ,						__UpperCamelCase :      List[Any] = True     ,						**__UpperCamelCase :      Union[str, Any]     ,						)       ->       str:
												_UpperCamelCase      						=	kwargs.pop('''use_source_tokenizer'''     ,						A_  )

												_UpperCamelCase      						=	self.convert_ids_to_tokens(A_     ,						skip_special_tokens=A_  )

												# To avoid mixing byte-level and unicode for byte-level BPT
												# we need to build string separately for added tokens and byte-level tokens
												# cf. https://github.com/huggingface/transformers/issues/1133
												_UpperCamelCase      						=	[]
												_UpperCamelCase      						=	[]
												for token in filtered_tokens:
																	if skip_special_tokens and token in self.all_special_ids:
																						continue
																	if token in self.added_tokens_encoder:
																						if current_sub_text:
																											sub_texts.append(self.convert_tokens_to_string(A_  )  )
																											_UpperCamelCase      						=	[]
																						sub_texts.append(A_  )
																	else:
																						current_sub_text.append(A_  )
												if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(A_  )  )

												# Mimic the behavior of the Rust tokenizer:
												# No space before [MASK] and [SEP]
												if spaces_between_special_tokens:
																	_UpperCamelCase      						=	re.sub(R''' (\[(MASK|SEP)\])'''     ,						R'''\1'''     ,						''' '''.join(A_  )  )
												else:
																	_UpperCamelCase      						=	''''''.join(A_  )

												_UpperCamelCase      						=	(
												    clean_up_tokenization_spaces
												    if clean_up_tokenization_spaces is not None
												    else self.clean_up_tokenization_spaces
												)
												if clean_up_tokenization_spaces:
																	_UpperCamelCase      						=	self.clean_up_tokenization(A_  )
																	return clean_text
												else:
																	return text






							def       _UpperCamelCase			(		self :      List[Any]     ,						__UpperCamelCase :      List[Any]     ,						__UpperCamelCase :      int = None  )       ->       Tuple[str]:





												if not os.path.isdir(A_  ):
																	logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
																	return
												_UpperCamelCase      						=	os.path.join(
												    A_     ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

												if os.path.abspath(self.vocab_file  ) != os.path.abspath(A_  ) and os.path.isfile(self.vocab_file  ):
																	copyfile(self.vocab_file     ,						A_  )
												elif not os.path.isfile(self.vocab_file  ):
																	with open(A_     ,						'''wb'''  ) as fi:
																						_UpperCamelCase      						=	self.sp_model.serialized_model_proto()
																						fi.write(A_  )

												return (out_vocab_file,)






							def       _UpperCamelCase			(		self :      str     ,						__UpperCamelCase :      int     ,						__UpperCamelCase :      Optional[int] = None  )       ->       List[int]:





												if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												_UpperCamelCase      						=	[self.cls_token_id]
												_UpperCamelCase      						=	[self.sep_token_id]
												return cls + token_ids_a + sep + token_ids_a + sep






							def       _UpperCamelCase			(		self :      List[str]     ,						__UpperCamelCase :      Union[str, Any]     ,						__UpperCamelCase :      Any = None     ,						__UpperCamelCase :      Any = False  )       ->       List[int]:





												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A_     ,						token_ids_a=A_     ,						already_has_special_tokens=A_  )

												if token_ids_a is None:
																	return [1] + ([0] * len(A_  )) + [1]
												return [1] + ([0] * len(A_  )) + [1] + ([0] * len(A_  )) + [1]






							def       _UpperCamelCase			(		self :      List[Any]     ,						__UpperCamelCase :      List[str]     ,						__UpperCamelCase :      Union[str, Any] = None  )       ->       List[int]:
												_UpperCamelCase      						=	[self.sep_token_id]
												_UpperCamelCase      						=	[self.cls_token_id]
												if token_ids_a is None:
																	return len(cls + token_ids_a + sep  ) * [0]
												return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]



from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						str					=					[]
      __lowerCAmelCase					:						List[Any]					=					[]
      __lowerCAmelCase					:						str					=					[]

      for rt in rc.restypes:
            __lowerCAmelCase					:						List[Any]					=					rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
            restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]						)
            __lowerCAmelCase					:						List[str]					=					{name: i for i, name in enumerate(lowercase__						)}
            restype_atomaa_to_atomaa_list.append(
                [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]						)

            restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]						)

      # Add dummy mapping for restype 'UNK'
      restype_atomaa_to_atomaa_list.append([0] * 1_4						)
      restype_atomaa_to_atomaa_list.append([0] * 3_7						)
      restype_atomaa_mask_list.append([0.0] * 1_4						)

      __lowerCAmelCase					:						List[Any]					=					torch.tensor(
          lowercase__   ,	dtype=torch.intaa   ,	device=protein['''aatype'''].device   ,	)
      __lowerCAmelCase					:						Optional[Any]					=					torch.tensor(
          lowercase__   ,	dtype=torch.intaa   ,	device=protein['''aatype'''].device   ,	)
      __lowerCAmelCase					:						Tuple					=					torch.tensor(
          lowercase__   ,	dtype=torch.floataa   ,	device=protein['''aatype'''].device   ,	)
      __lowerCAmelCase					:						List[Any]					=					protein['''aatype'''].to(torch.long						)

      # create the mapping for (residx, atom14) --> atom37, i.e. an array
      # with shape (num_res, 14) containing the atom37 indices for this protein
      __lowerCAmelCase					:						Any					=					restype_atomaa_to_atomaa[protein_aatype]
      __lowerCAmelCase					:						Union[str, Any]					=					restype_atomaa_mask[protein_aatype]

      __lowerCAmelCase					:						int					=					residx_atomaa_mask
      __lowerCAmelCase					:						List[str]					=					residx_atomaa_to_atomaa.long()

      # create the gather indices for mapping back
      __lowerCAmelCase					:						int					=					restype_atomaa_to_atomaa[protein_aatype]
      __lowerCAmelCase					:						Union[str, Any]					=					residx_atomaa_to_atomaa.long()

      # create the corresponding mask
      __lowerCAmelCase					:						str					=					torch.zeros([2_1, 3_7]   ,	dtype=torch.floataa   ,	device=protein['''aatype'''].device						)
      for restype, restype_letter in enumerate(rc.restypes						):
            __lowerCAmelCase					:						Optional[int]					=					rc.restype_atoa[restype_letter]
            __lowerCAmelCase					:						Optional[Any]					=					rc.residue_atoms[restype_name]
            for atom_name in atom_names:
                  __lowerCAmelCase					:						str					=					rc.atom_order[atom_name]
                  __lowerCAmelCase					:						List[Any]					=					1

      __lowerCAmelCase					:						Union[str, Any]					=					restype_atomaa_mask[protein_aatype]
      __lowerCAmelCase					:						Any					=					residx_atomaa_mask

      return protein







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Dict					=					tree_map(lambda lowercase__						: torch.tensor(lowercase__   ,	device=batch['''aatype'''].device						)   ,	lowercase__   ,	np.ndarray						)
      __lowerCAmelCase					:						Tuple					=					tensor_tree_map(lambda lowercase__						: np.array(lowercase__						)   ,	make_atomaa_masks(lowercase__						)						)
      return out


