


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


_snake_case					:		Optional[Any]      =    logging.get_logger(__name__)

_snake_case					:		int      =    {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_snake_case					:		Union[str, Any]      =    {
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

_snake_case					:		str      =    {
    "junnyu/roformer_chinese_small": 1_536,
    "junnyu/roformer_chinese_base": 1_536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


_snake_case					:		Tuple      =    {
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Any					  =  VOCAB_FILES_NAMES
							__UpperCAmelCase   :							int					  =  PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase   :							Optional[int]					  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase   :							Optional[int]					  =  PRETRAINED_INIT_CONFIGURATION
							__UpperCAmelCase   :							Tuple					  =  RoFormerTokenizer

							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Optional[int]=None   ,     lowerCamelCase	:  List[Any]=None   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  Any="[UNK]"   ,     lowerCamelCase	:  Dict="[SEP]"   ,     lowerCamelCase	:  str="[PAD]"   ,     lowerCamelCase	:  Optional[int]="[CLS]"   ,     lowerCamelCase	:  List[str]="[MASK]"   ,     lowerCamelCase	:  str=True   ,     lowerCamelCase	:  Any=None   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   Dict:
														super().__init__(
														    lowerCamelCase   ,     tokenizer_file=lowerCamelCase   ,     do_lower_case=lowerCamelCase   ,     unk_token=lowerCamelCase   ,     sep_token=lowerCamelCase   ,     pad_token=lowerCamelCase   ,     cls_token=lowerCamelCase   ,     mask_token=lowerCamelCase   ,     tokenize_chinese_chars=lowerCamelCase   ,     strip_accents=lowerCamelCase   ,     **lowerCamelCase   ,     )

														__snake_case		:      str										=       json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
														if (
														    pre_tok_state.get("lowercase"   ,     lowerCamelCase    ) != do_lower_case
														    or pre_tok_state.get("strip_accents"   ,     lowerCamelCase    ) != strip_accents
														):
																					__snake_case		:      Any										=       getattr(lowerCamelCase   ,     pre_tok_state.pop("type"    )    )
																					__snake_case		:      List[Any]										=       do_lower_case
																					__snake_case		:      Dict										=       strip_accents
																					__snake_case		:      Optional[Any]										=       pre_tok_class(**lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       do_lower_case

							def __getstate__(							self	:  Optional[int]    )     ->   Union[str, Any]:
														__snake_case		:      Tuple										=       self.__dict__.copy()
														__snake_case		:      Dict										=       BertPreTokenizer()
														return state

							def __setstate__(							self	:  Dict   ,     lowerCamelCase	:  Optional[Any]    )     ->   Union[str, Any]:
														__snake_case		:      Tuple										=       d
														__snake_case		:      Tuple										=       self.__dict__["_tokenizer"].get_vocab()
														__snake_case		:      Any										=       PreTokenizer.custom(JiebaPreTokenizer(lowerCamelCase    )    )

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  int=None    )     ->   List[Any]:
														__snake_case		:      int										=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  List[int]   ,     lowerCamelCase	:  Optional[List[int]] = None    )     ->   List[int]:
														__snake_case		:      List[str]										=       [self.sep_token_id]
														__snake_case		:      Tuple										=       [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]

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






							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  Dict=None   ,     lowerCamelCase	:  str=None   ,     lowerCamelCase	:  Union[str, Any]=False   ,     **lowerCamelCase	:  Optional[Any]   ,     )     ->   Union[str, Any]:
														__snake_case		:      Dict										=       BertPreTokenizer()
														return super().save_pretrained(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     **lowerCamelCase    )





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     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Any										=       []
							__snake_case		:      Optional[Any]										=       []
							__snake_case		:      List[Any]										=       []

							for rt in rc.restypes:
														__snake_case		:      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]     )
														__snake_case		:      Tuple										=       {name: i for i, name in enumerate(__lowerCamelCase     )}
														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     )

							__snake_case		:      int										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      List[str]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[Any]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.floataa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[int]										=       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
							__snake_case		:      Optional[Any]										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Tuple										=       restype_atomaa_mask[protein_aatype]

							__snake_case		:      Optional[Any]										=       residx_atomaa_mask
							__snake_case		:      Union[str, Any]										=       residx_atomaa_to_atomaa.long()

							# create the gather indices for mapping back
							__snake_case		:      Dict										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Dict										=       residx_atomaa_to_atomaa.long()

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

							__snake_case		:      List[str]										=       restype_atomaa_mask[protein_aatype]
							__snake_case		:      List[str]										=       residx_atomaa_mask

							return protein







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      str										=       tree_map(lambda __lowerCamelCase     : torch.tensor(__lowerCamelCase							,     device=batch["aatype"].device     )							,     __lowerCamelCase							,     np.ndarray     )
							__snake_case		:      str										=       tensor_tree_map(lambda __lowerCamelCase     : np.array(__lowerCamelCase     )							,     make_atomaa_masks(__lowerCamelCase     )     )
							return out

