



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_	:	Dict[str, torch.Tensor]):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  []
				lowerCAmelCase__  :       int				=  []
				lowerCAmelCase__  :       Optional[int]				=  []

				for rt in rc.restypes:
								lowerCAmelCase__  :       int				=  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__  :       Any				=  {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] * 14)
				restype_atomaa_to_atomaa_list.append([0] * 37)
				restype_atomaa_mask_list.append([0.0] * 14)

				lowerCAmelCase__  :       Optional[Any]				=  torch.tensor(
				    lowerCamelCase_ ,dtype=torch.intaa ,device=protein['''aatype'''].device ,)
				lowerCAmelCase__  :       Optional[int]				=  torch.tensor(
				    lowerCamelCase_ ,dtype=torch.intaa ,device=protein['''aatype'''].device ,)
				lowerCAmelCase__  :       List[str]				=  torch.tensor(
				    lowerCamelCase_ ,dtype=torch.floataa ,device=protein['''aatype'''].device ,)
				lowerCAmelCase__  :       Optional[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__  :       Optional[int]				=  restype_atomaa_to_atomaa[protein_aatype]
				lowerCAmelCase__  :       List[str]				=  restype_atomaa_mask[protein_aatype]

				lowerCAmelCase__  :       Tuple				=  residx_atomaa_mask
				lowerCAmelCase__  :       Dict				=  residx_atomaa_to_atomaa.long()

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

				# create the corresponding mask
				lowerCAmelCase__  :       List[str]				=  torch.zeros([21, 37] ,dtype=torch.floataa ,device=protein['''aatype'''].device)
				for restype, restype_letter in enumerate(rc.restypes):
								lowerCAmelCase__  :       List[str]				=  rc.restype_atoa[restype_letter]
								lowerCAmelCase__  :       List[str]				=  rc.residue_atoms[restype_name]
								for atom_name in atom_names:
												lowerCAmelCase__  :       List[Any]				=  rc.atom_order[atom_name]
												lowerCAmelCase__  :       Any				=  1

				lowerCAmelCase__  :       int				=  restype_atomaa_mask[protein_aatype]
				lowerCAmelCase__  :       int				=  residx_atomaa_mask

				return protein






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict[str, torch.Tensor]):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[Any]				=  tree_map(lambda lowerCamelCase_: torch.tensor(lowerCamelCase_ ,device=batch['''aatype'''].device) ,lowerCamelCase_ ,np.ndarray)
				lowerCAmelCase__  :       Union[str, Any]				=  tensor_tree_map(lambda lowerCamelCase_: np.array(lowerCamelCase_) ,make_atomaa_masks(lowerCamelCase_))
				return out






import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case       :	List[Any]            =logging.get_logger(__name__)

__snake_case       :	str            ={
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""mvp"""
		snake_case_			=["""past_key_values"""]
		snake_case_			={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(self	,__lowerCamelCase=5_02_67	,__lowerCamelCase=10_24	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase="gelu"	,__lowerCamelCase=10_24	,__lowerCamelCase=0.1	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase=0.02	,__lowerCamelCase=0.0	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=1	,__lowerCamelCase=0	,__lowerCamelCase=2	,__lowerCamelCase=True	,__lowerCamelCase=2	,__lowerCamelCase=2	,__lowerCamelCase=False	,__lowerCamelCase=1_00	,__lowerCamelCase=8_00	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  vocab_size
						lowerCAmelCase__  :       Dict				=  max_position_embeddings
						lowerCAmelCase__  :       Optional[int]				=  d_model
						lowerCAmelCase__  :       Any				=  encoder_ffn_dim
						lowerCAmelCase__  :       str				=  encoder_layers
						lowerCAmelCase__  :       Union[str, Any]				=  encoder_attention_heads
						lowerCAmelCase__  :       Dict				=  decoder_ffn_dim
						lowerCAmelCase__  :       Optional[int]				=  decoder_layers
						lowerCAmelCase__  :       int				=  decoder_attention_heads
						lowerCAmelCase__  :       str				=  dropout
						lowerCAmelCase__  :       int				=  attention_dropout
						lowerCAmelCase__  :       Optional[Any]				=  activation_dropout
						lowerCAmelCase__  :       Optional[int]				=  activation_function
						lowerCAmelCase__  :       int				=  init_std
						lowerCAmelCase__  :       List[Any]				=  encoder_layerdrop
						lowerCAmelCase__  :       Union[str, Any]				=  decoder_layerdrop
						lowerCAmelCase__  :       Tuple				=  classifier_dropout
						lowerCAmelCase__  :       Optional[int]				=  use_cache
						lowerCAmelCase__  :       List[Any]				=  encoder_layers
						lowerCAmelCase__  :       List[str]				=  scale_embedding  # scale factor will be sqrt(d_model) if True
						lowerCAmelCase__  :       Any				=  use_prompt
						lowerCAmelCase__  :       Optional[Any]				=  prompt_length
						lowerCAmelCase__  :       int				=  prompt_mid_dim

						super().__init__(
						    pad_token_id=__lowerCamelCase	,bos_token_id=__lowerCamelCase	,eos_token_id=__lowerCamelCase	,is_encoder_decoder=__lowerCamelCase	,decoder_start_token_id=__lowerCamelCase	,forced_eos_token_id=__lowerCamelCase	,**__lowerCamelCase	,)

						if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''	,__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  self.bos_token_id
										warnings.warn(
										    f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
										    '''The config can simply be saved and uploaded again to be fixed.'''				)

