




"""simple docstring"""

import torch

from transformers import AutoTokenizer

class     lowercase (      __lowerCamelCase):







				def __init__(							self				:   Optional[int] ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					A_					:			List[str]						=						AutoTokenizer.from_pretrained(_UpperCamelCase    )







				def    a_					(							self				:   Any ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					if isinstance(_UpperCamelCase ,       _UpperCamelCase    ) and all(isinstance(_x ,       _UpperCamelCase    ) for _x in x    ):
						A_					:			Optional[int]						=						None
						for l in x:
							A_					:			str						=						self.tokenizer(
							    _UpperCamelCase ,       padding='''max_length''' ,       max_length=3_84 ,       truncation=_UpperCamelCase ,       return_tensors='''pt''' ,       )
							A_					:			Optional[int]						=						torch.tensor([len(_UpperCamelCase    )]    )
							if d is not None:
								for k in d.keys():
									A_					:			Dict						=						torch.cat((d[k], t[k]) ,       0    )
							else:
								A_					:			List[Any]						=						t

						A_					:			Optional[Any]						=						torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''    )    )
						A_					:			str						=						torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''    )    )

					elif isinstance(_UpperCamelCase ,       _UpperCamelCase    ) and all(isinstance(_x ,       _UpperCamelCase    ) for _x in x    ):
						A_					:			Dict						=						self.tokenizer(
						    _UpperCamelCase ,       padding='''max_length''' ,       max_length=3_84 ,       truncation=_UpperCamelCase ,       return_tensors='''pt''' ,       )

					else:
						raise Exception(
						    '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
						    ''' strings` for supports are supported.'''    )

					return d







				def    a_					(							self				:   str ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   Optional[Any] ,       _lowerCamelCase				:   List[Any] ,       _lowerCamelCase				:   List[Any]=0.70    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						[]
					for idx in range(len(W_query['''input_ids''']    )    ):
						A_					:			str						=						range(p_start.shape[1]    )

						A_					:			Dict						=						[]
						for start_id in start_indexes:
							for end_id in end_indexes:
								if start_id < end_id:
									output.append(
									    (
									        start_id,
									        end_id,
									        p_start[idx][start_id].item(),
									        p_end[idx][end_id].item(),
									    )    )

						output.sort(key=lambda _lowerCamelCase    : (tup[2] * tup[3]) ,       reverse=_UpperCamelCase    )
						A_					:			Optional[Any]						=						[]
						for k in range(len(_UpperCamelCase    )    ):
							if output[k][2] * output[k][3] >= thresh:
								A_				,	A_					:			Dict						=						output[k][0], output[k][1]
								A_					:			List[Any]						=						self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]    )
								temp.append((decoded, output[k][2] * output[k][3])    )

						final_outputs.append(_UpperCamelCase    )

					return final_outputs











"""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





