
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__      ( __lowerCAmelCase      ,						__lowerCAmelCase = 512							):
    snake_case__  =			x.shape[1] if len(x.shape							) > 1 else 1
    snake_case__  =			1 + np.arange(0      ,						feature_num * offset      ,						__lowerCAmelCase      ,						dtype=np.intaa							)
    snake_case__  =			x + feature_offset
    return x





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase=True							):
    requires_backends(__lowerCAmelCase      ,						["cython"]							)

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

    snake_case__  =			np.asarray(item["edge_index"]      ,						dtype=np.intaa							)

    snake_case__  =			convert_to_single_emb(__lowerCAmelCase							) + 1
    snake_case__  =			item["num_nodes"]

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

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

    snake_case__  ,   snake_case__  =			algos_graphormer.floyd_warshall(__lowerCAmelCase							)
    snake_case__  =			np.amax(__lowerCAmelCase							)

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

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

    return item





class    _SCREAMING_SNAKE_CASE					:
      def __init__(     self						,		lowerCamelCase=20						,		lowerCamelCase=False	):






          if not is_cython_available():
              raise ImportError("Graphormer preprocessing needs Cython (pyximport)"	)

          snake_case__  =			spatial_pos_max
          snake_case__  =			on_the_fly_processing



      def __call__(     self						,		lowerCamelCase	):






          if self.on_the_fly_processing:
              snake_case__  =			[preprocess_item(SCREAMING_SNAKE_CASE_	) for i in features]

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

          snake_case__  =			max(len(i["input_nodes"]	) for i in features	)
          snake_case__  =			len(features[0]["input_nodes"][0]	)
          snake_case__  =			len(features[0]["attn_edge_type"][0][0]	)
          snake_case__  =			max(len(i["input_edges"][0][0]	) for i in features	)
          snake_case__  =			len(features[0]["input_edges"][0][0][0]	)
          snake_case__  =			len(SCREAMING_SNAKE_CASE_	)

          snake_case__  =			torch.zeros(SCREAMING_SNAKE_CASE_						,		max_node_num + 1						,		max_node_num + 1						,		dtype=torch.float	)
          snake_case__  =			torch.zeros(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		dtype=torch.long	)
          snake_case__  =			torch.zeros(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		dtype=torch.long	)
          snake_case__  =			torch.zeros(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		dtype=torch.long	)
          snake_case__  =			torch.zeros(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		dtype=torch.long	)
          snake_case__  =			torch.zeros(
              SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		dtype=torch.long	)

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

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

              snake_case__  =			f["attn_bias"]
              snake_case__  =			f[
                  "attn_edge_type"
              ]
              snake_case__  =			f["spatial_pos"]
              snake_case__  =			f["in_degree"]
              snake_case__  =			f["input_nodes"]
              snake_case__  =			f["input_edges"]

          snake_case__  =			batch["in_degree"]

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

          return batch




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__magic_name__           =					{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__           =					[
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

     __magic_name__           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure)



