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							(UpperCAmelCase__       :							Dict[str, torch.Tensor]					):
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []

					for rt in rc.restypes:
										SCREAMING_SNAKE_CASE           =    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]					)
										SCREAMING_SNAKE_CASE           =    {name: i for i, name in enumerate(UpperCAmelCase__					)}
										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					)

					SCREAMING_SNAKE_CASE           =    torch.tensor(
					    UpperCAmelCase__ ,	dtype=torch.intaa ,	device=protein["aatype"].device ,	)
					SCREAMING_SNAKE_CASE           =    torch.tensor(
					    UpperCAmelCase__ ,	dtype=torch.intaa ,	device=protein["aatype"].device ,	)
					SCREAMING_SNAKE_CASE           =    torch.tensor(
					    UpperCAmelCase__ ,	dtype=torch.floataa ,	device=protein["aatype"].device ,	)
					SCREAMING_SNAKE_CASE           =    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
					SCREAMING_SNAKE_CASE           =    restype_atomaa_to_atomaa[protein_aatype]
					SCREAMING_SNAKE_CASE           =    restype_atomaa_mask[protein_aatype]

					SCREAMING_SNAKE_CASE           =    residx_atomaa_mask
					SCREAMING_SNAKE_CASE           =    residx_atomaa_to_atomaa.long()

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

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

					SCREAMING_SNAKE_CASE           =    restype_atomaa_mask[protein_aatype]
					SCREAMING_SNAKE_CASE           =    residx_atomaa_mask

					return protein





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





from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


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

_lowerCamelCase   :				Any     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				int     			=						{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

_lowerCamelCase   :				Tuple     			=						{'''allegro/herbert-base-cased''': 5_14}
_lowerCamelCase   :				Optional[int]     			=						{}


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							VOCAB_FILES_NAMES
							lowercase__  :    str									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Tuple									=							PRETRAINED_INIT_CONFIGURATION
							lowercase__  :    Optional[int]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    str									=							HerbertTokenizer




							def __init__(  self				:   Dict		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Optional[int]="<s>"		,							_UpperCamelCase				:   Union[str, Any]="<unk>"		,							_UpperCamelCase				:   List[str]="<pad>"		,							_UpperCamelCase				:   List[str]="<mask>"		,							_UpperCamelCase				:   Tuple="</s>"		,							**_UpperCamelCase				:   Any		,							)   ->			str:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							_UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							**_UpperCamelCase		,							)




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



												'''simple docstring'''

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

												return cls + token_ids_a + sep + token_ids_a + sep




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			List[int]:



												'''simple docstring'''

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

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_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 ) * [0] + len(token_ids_a + sep ) * [1]





							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




