





'''simple docstring'''



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]			):
			__lowercase			  =							[]
			__lowercase			  =							[]
			__lowercase			  =							[]

			for rt in rc.restypes:
						__lowercase			  =							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]			)
						__lowercase			  =							{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			)

			__lowercase			  =							torch.tensor(
			    lowerCamelCase_						,    dtype=torch.intaa						,    device=protein['''aatype'''].device						,    )
			__lowercase			  =							torch.tensor(
			    lowerCamelCase_						,    dtype=torch.intaa						,    device=protein['''aatype'''].device						,    )
			__lowercase			  =							torch.tensor(
			    lowerCamelCase_						,    dtype=torch.floataa						,    device=protein['''aatype'''].device						,    )
			__lowercase			  =							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
			__lowercase			  =							restype_atomaa_to_atomaa[protein_aatype]
			__lowercase			  =							restype_atomaa_mask[protein_aatype]

			__lowercase			  =							residx_atomaa_mask
			__lowercase			  =							residx_atomaa_to_atomaa.long()

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

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

			__lowercase			  =							restype_atomaa_mask[protein_aatype]
			__lowercase			  =							residx_atomaa_mask

			return protein




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Dict[str, torch.Tensor]			):
			__lowercase			  =							tree_map(lambda lowerCamelCase_			: torch.tensor(lowerCamelCase_						,    device=batch['''aatype'''].device			)						,    lowerCamelCase_						,    np.ndarray			)
			__lowercase			  =							tensor_tree_map(lambda lowerCamelCase_			: np.array(lowerCamelCase_			)						,    make_atomaa_masks(lowerCamelCase_			)			)
			return out












'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[str, Any]     =    "gpt_neox_japanese"


	def __init__(self       ,_lowerCamelCase=32000       ,_lowerCamelCase=2560       ,_lowerCamelCase=32       ,_lowerCamelCase=32       ,_lowerCamelCase=4       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=1.0_0       ,_lowerCamelCase=10000       ,_lowerCamelCase=2048       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-5       ,_lowerCamelCase=True       ,_lowerCamelCase=31996       ,_lowerCamelCase=31999       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.0       ,**_lowerCamelCase       ,)							->							Optional[int]:







				'''simple docstring'''





				super().__init__(bos_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,**_lowerCamelCase	)
				__lowercase			  =							vocab_size
				__lowercase			  =							max_position_embeddings
				__lowercase			  =							hidden_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							intermediate_multiple_size
				__lowercase			  =							hidden_act
				__lowercase			  =							rotary_pct
				__lowercase			  =							rotary_emb_base
				__lowercase			  =							initializer_range
				__lowercase			  =							layer_norm_eps
				__lowercase			  =							use_cache
				__lowercase			  =							attention_dropout
				__lowercase			  =							hidden_dropout





