




"""simple docstring"""

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
		UpperCamelCase__			:     int			       =				None


UpperCamelCase__			:     Optional[int]			       =				logging.get_logger(__name__)

UpperCamelCase__			:     Union[str, Any]			       =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__			:     List[Any]			       =				{
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase__			:     List[Any]			       =				{
    "xlm-roberta-base": 5_12,
    "xlm-roberta-large": 5_12,
    "xlm-roberta-large-finetuned-conll02-dutch": 5_12,
    "xlm-roberta-large-finetuned-conll02-spanish": 5_12,
    "xlm-roberta-large-finetuned-conll03-english": 5_12,
    "xlm-roberta-large-finetuned-conll03-german": 5_12,
}
class      A__   (   lowercase_  ):
	A_				:					List[str]      =   VOCAB_FILES_NAMES
	A_				:					List[Any]      =   PRETRAINED_VOCAB_FILES_MAP
	A_				:					Optional[int]      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_				:					Dict      =   ["""input_ids""", """attention_mask"""]
	A_				:					Union[str, Any]      =   XLMRobertaTokenizer




	def __init__( self	:						str   ,     lowercase	:						str=None   ,     lowercase	:						Union[str, Any]=None   ,     lowercase	:						Dict="<s>"   ,     lowercase	:						Tuple="</s>"   ,     lowercase	:						Optional[int]="</s>"   ,     lowercase	:						Tuple="<s>"   ,     lowercase	:						int="<unk>"   ,     lowercase	:						str="<pad>"   ,     lowercase	:						List[str]="<mask>"   ,     **lowercase	:						Any   ,     )       ->			str:
						# Mask token behave like a normal word, i.e. include the space before it
						__snake_case        =						AddedToken(lowercase   ,     lstrip=lowercase   ,     rstrip=lowercase						) if isinstance(lowercase   ,     lowercase						) else mask_token

						super().__init__(
						    lowercase   ,     tokenizer_file=lowercase   ,     bos_token=lowercase   ,     eos_token=lowercase   ,     sep_token=lowercase   ,     cls_token=lowercase   ,     unk_token=lowercase   ,     pad_token=lowercase   ,     mask_token=lowercase   ,     **lowercase   ,     )

						__snake_case        =						vocab_file
						__snake_case        =						False if not self.vocab_file else True




	def 				a_		( self	:						Optional[int]   ,     lowercase	:						List[int]   ,     lowercase	:						Optional[List[int]] = None						)       ->			List[int]:

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




	def 				a_		( self	:						Optional[Any]   ,     lowercase	:						List[int]   ,     lowercase	:						Optional[List[int]] = None						)       ->			List[int]:
						__snake_case        =						[self.sep_token_id]
						__snake_case        =						[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 + sep + token_ids_a + sep						) * [0]




	def 				a_		( self	:						List[Any]   ,     lowercase	:						str   ,     lowercase	:						Optional[str] = None						)       ->			Tuple[str]:
						if not self.can_save_slow_tokenizer:
											raise ValueError(
											    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
											    """tokenizer."""						)

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

						if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase						):
											copyfile(self.vocab_file   ,     lowercase						)

						return (out_vocab_file,)








"""simple docstring"""

from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims


