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 						__snake_case (       lowerCAmelCase       :     List[Any]      ,  lowerCAmelCase       :     int = 512			):
 __UpperCAmelCase         =					x.shape[1] if len(x.shape			) > 1 else 1
 __UpperCAmelCase         =					1 + np.arange(0      ,  feature_num * offset      ,  lowerCAmelCase      ,  dtype=np.intaa			)
 __UpperCAmelCase         =					x + feature_offset
 return x



def 						__snake_case (       lowerCAmelCase       :     List[str]      ,  lowerCAmelCase       :     Optional[int]=True			):
 requires_backends(lowerCAmelCase      ,  ['cython']			)

 if keep_features and "edge_attr" in item.keys():  # edge_attr
  __UpperCAmelCase         =					np.asarray(item['edge_attr']      ,  dtype=np.intaa			)
 else:
  __UpperCAmelCase         =					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         =					np.asarray(item['node_feat']      ,  dtype=np.intaa			)
 else:
  __UpperCAmelCase         =					np.ones((item['num_nodes'], 1)      ,  dtype=np.intaa			)  # same embedding for all

 __UpperCAmelCase         =					np.asarray(item['edge_index']      ,  dtype=np.intaa			)

 __UpperCAmelCase         =					convert_to_single_emb(lowerCAmelCase			) + 1
 __UpperCAmelCase         =					item['num_nodes']

 if len(edge_attr.shape			) == 1:
  __UpperCAmelCase         =					edge_attr[:, None]
 __UpperCAmelCase         =					np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]      ,  dtype=np.intaa			)
 __UpperCAmelCase         =					convert_to_single_emb(lowerCAmelCase			) + 1

 # node adj matrix [num_nodes, num_nodes] bool
 __UpperCAmelCase         =					np.zeros([num_nodes, num_nodes]      ,  dtype=lowerCAmelCase			)
 __UpperCAmelCase         =					True

 __UpperCAmelCase  ,			__UpperCAmelCase         =					algos_graphormer.floyd_warshall(lowerCAmelCase			)
 __UpperCAmelCase         =					np.amax(lowerCAmelCase			)

 __UpperCAmelCase         =					algos_graphormer.gen_edge_input(lowerCAmelCase      ,  lowerCAmelCase      ,  lowerCAmelCase			)
 __UpperCAmelCase         =					np.zeros([num_nodes + 1, num_nodes + 1]      ,  dtype=np.single			)  # with graph token

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

 return item

class   _lowercase:



   """simple docstring"""





   def __init__(							self:    Optional[int]			,a:    Optional[Any]=20			,a:    str=False	):
    if not is_cython_available():
     raise ImportError('Graphormer preprocessing needs Cython (pyximport)'	)

    __UpperCAmelCase         =					spatial_pos_max
    __UpperCAmelCase         =					on_the_fly_processing





   def __call__(							self:    List[str]			,a:    List[dict]	):
    if self.on_the_fly_processing:
     __UpperCAmelCase         =					[preprocess_item(a	) for i in features]

    if not isinstance(features[0]			,a	):
     __UpperCAmelCase         =					[vars(a	) for f in features]
    __UpperCAmelCase         =					{}

    __UpperCAmelCase         =					max(len(i['input_nodes']	) for i in features	)
    __UpperCAmelCase         =					len(features[0]['input_nodes'][0]	)
    __UpperCAmelCase         =					len(features[0]['attn_edge_type'][0][0]	)
    __UpperCAmelCase         =					max(len(i['input_edges'][0][0]	) for i in features	)
    __UpperCAmelCase         =					len(features[0]['input_edges'][0][0][0]	)
    __UpperCAmelCase         =					len(a	)

    __UpperCAmelCase         =					torch.zeros(a			,max_node_num + 1			,max_node_num + 1			,dtype=torch.float	)
    __UpperCAmelCase         =					torch.zeros(a			,a			,a			,a			,dtype=torch.long	)
    __UpperCAmelCase         =					torch.zeros(a			,a			,a			,dtype=torch.long	)
    __UpperCAmelCase         =					torch.zeros(a			,a			,dtype=torch.long	)
    __UpperCAmelCase         =					torch.zeros(a			,a			,a			,dtype=torch.long	)
    __UpperCAmelCase         =					torch.zeros(
        a			,a			,a			,a			,a			,dtype=torch.long	)

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

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

     __UpperCAmelCase         =					f['attn_bias']
     __UpperCAmelCase         =					f[
         'attn_edge_type'
     ]
     __UpperCAmelCase         =					f['spatial_pos']
     __UpperCAmelCase         =					f['in_degree']
     __UpperCAmelCase         =					f['input_nodes']
     __UpperCAmelCase         =					f['input_edges']

    __UpperCAmelCase         =					batch['in_degree']

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

    return batch




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		['MarianTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		[
         'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MarianForCausalLM',
         'MarianModel',
         'MarianMTModel',
         'MarianPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     _UpperCamelCase				:		List[Any]       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



