from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
 import pyximport

 pyximport.install(setup_args={'''include_dirs''': np.get_include()})
 from . import algos_graphormer  # noqa E402


def SCREAMING_SNAKE_CASE				(     lowercase_:	int		,						lowercase_:	int = 512				):
       _UpperCamelCase     :       Any  						=  x.shape[1] if len(x.shape				) > 1 else 1
       _UpperCamelCase     :       Tuple  						=  1 + np.arange(0		,						feature_num * offset		,						lowercase_		,						dtype=np.intaa				)
       _UpperCamelCase     :       Dict  						=  x + feature_offset
       return x


def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	Optional[Any]=True				):
       requires_backends(lowercase_		,						['cython']				)

       if keep_features and "edge_attr" in item.keys():  # edge_attr
              _UpperCamelCase     :       Dict  						=  np.asarray(item['edge_attr']		,						dtype=np.intaa				)
       else:
              _UpperCamelCase     :       Optional[Any]  						=  np.ones((len(item['edge_index'][0]				), 1)		,						dtype=np.intaa				)  # same embedding for all

       if keep_features and "node_feat" in item.keys():  # input_nodes
              _UpperCamelCase     :       Tuple  						=  np.asarray(item['node_feat']		,						dtype=np.intaa				)
       else:
              _UpperCamelCase     :       Union[str, Any]  						=  np.ones((item['num_nodes'], 1)		,						dtype=np.intaa				)  # same embedding for all

       _UpperCamelCase     :       Optional[Any]  						=  np.asarray(item['edge_index']		,						dtype=np.intaa				)

       _UpperCamelCase     :       Union[str, Any]  						=  convert_to_single_emb(lowercase_				) + 1
       _UpperCamelCase     :       List[Any]  						=  item['num_nodes']

       if len(edge_attr.shape				) == 1:
              _UpperCamelCase     :       Optional[int]  						=  edge_attr[:, None]
       _UpperCamelCase     :       str  						=  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]		,						dtype=np.intaa				)
       _UpperCamelCase     :       int  						=  convert_to_single_emb(lowercase_				) + 1

       # node adj matrix [num_nodes, num_nodes] bool
       _UpperCamelCase     :       List[str]  						=  np.zeros([num_nodes, num_nodes]		,						dtype=lowercase_				)
       _UpperCamelCase     :       Any  						=  True

       _UpperCamelCase						,      _UpperCamelCase     :       Tuple  						=  algos_graphormer.floyd_warshall(lowercase_				)
       _UpperCamelCase     :       str  						=  np.amax(lowercase_				)

       _UpperCamelCase     :       List[str]  						=  algos_graphormer.gen_edge_input(lowercase_		,						lowercase_		,						lowercase_				)
       _UpperCamelCase     :       List[Any]  						=  np.zeros([num_nodes + 1, num_nodes + 1]		,						dtype=np.single				)  # with graph token

       # combine
       _UpperCamelCase     :       Dict  						=  input_nodes + 1  # we shift all indices by one for padding
       _UpperCamelCase     :       Any  						=  attn_bias
       _UpperCamelCase     :       Any  						=  attn_edge_type
       _UpperCamelCase     :       Optional[int]  						=  shortest_path_result.astype(np.intaa				) + 1  # we shift all indices by one for padding
       _UpperCamelCase     :       Optional[int]  						=  np.sum(lowercase_		,						axis=1				).reshape(-1				) + 1  # we shift all indices by one for padding
       _UpperCamelCase     :       Optional[Any]  						=  item['in_degree']  # for undirected graph
       _UpperCamelCase     :       Union[str, Any]  						=  input_edges + 1  # we shift all indices by one for padding
       if "labels" not in item:
              _UpperCamelCase     :       Dict  						=  item['y']

       return item





class    __lowerCAmelCase  :



 '''simple docstring'''

 def __init__(			self       :		str       ,			lowerCamelCase       :		Optional[int]=2_0       ,			lowerCamelCase       :		Tuple=False  ) ->							str:
        if not is_cython_available():
               raise ImportError('Graphormer preprocessing needs Cython (pyximport)'  )

        _UpperCamelCase     :       Optional[Any]  						=  spatial_pos_max
        _UpperCamelCase     :       Any  						=  on_the_fly_processing

 def __call__(			self       :		Optional[int]       ,			lowerCamelCase       :		List[dict]  ) ->							Dict[str, Any]:
        if self.on_the_fly_processing:
               _UpperCamelCase     :       Dict  						=  [preprocess_item(lowerCamelCase  ) for i in features]

        if not isinstance(features[0]       ,			lowerCamelCase  ):
               _UpperCamelCase     :       Dict  						=  [vars(lowerCamelCase  ) for f in features]
        _UpperCamelCase     :       Dict  						=  {}

        _UpperCamelCase     :       Optional[Any]  						=  max(len(i['input_nodes']  ) for i in features  )
        _UpperCamelCase     :       List[str]  						=  len(features[0]['input_nodes'][0]  )
        _UpperCamelCase     :       Tuple  						=  len(features[0]['attn_edge_type'][0][0]  )
        _UpperCamelCase     :       str  						=  max(len(i['input_edges'][0][0]  ) for i in features  )
        _UpperCamelCase     :       Any  						=  len(features[0]['input_edges'][0][0][0]  )
        _UpperCamelCase     :       Union[str, Any]  						=  len(lowerCamelCase  )

        _UpperCamelCase     :       List[Any]  						=  torch.zeros(lowerCamelCase       ,			max_node_num + 1       ,			max_node_num + 1       ,			dtype=torch.float  )
        _UpperCamelCase     :       List[str]  						=  torch.zeros(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			dtype=torch.long  )
        _UpperCamelCase     :       Optional[Any]  						=  torch.zeros(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			dtype=torch.long  )
        _UpperCamelCase     :       Union[str, Any]  						=  torch.zeros(lowerCamelCase       ,			lowerCamelCase       ,			dtype=torch.long  )
        _UpperCamelCase     :       Tuple  						=  torch.zeros(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			dtype=torch.long  )
        _UpperCamelCase     :       Optional[Any]  						=  torch.zeros(
            lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			dtype=torch.long  )

        for ix, f in enumerate(lowerCamelCase  ):
               for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                      _UpperCamelCase     :       int  						=  torch.tensor(f[k]  )

               if len(f['attn_bias'][1:, 1:][f['spatial_pos'] >= self.spatial_pos_max]  ) > 0:
                      _UpperCamelCase     :       int  						=  float('-inf'  )

               _UpperCamelCase     :       List[Any]  						=  f['attn_bias']
               _UpperCamelCase     :       Optional[int]  						=  f[
                   'attn_edge_type'
               ]
               _UpperCamelCase     :       Union[str, Any]  						=  f['spatial_pos']
               _UpperCamelCase     :       List[str]  						=  f['in_degree']
               _UpperCamelCase     :       List[Any]  						=  f['input_nodes']
               _UpperCamelCase     :       List[str]  						=  f['input_edges']

        _UpperCamelCase     :       List[str]  						=  batch['in_degree']

        _UpperCamelCase     :       Dict  						=  features[0]['labels']
        if len(lowerCamelCase  ) == 1:  # one task
               if isinstance(sample[0]       ,			lowerCamelCase  ):  # regression
                      _UpperCamelCase     :       List[str]  						=  torch.from_numpy(np.concatenate([i['labels'] for i in features]  )  )
               else:  # binary classification
                      _UpperCamelCase     :       Any  						=  torch.from_numpy(np.concatenate([i['labels'] for i in features]  )  )
        else:  # multi task classification, left to float to keep the NaNs
               _UpperCamelCase     :       List[str]  						=  torch.from_numpy(np.stack([i['labels'] for i in features]       ,			axis=0  )  )

        return batch




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



