
'''simple docstring'''





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_							(				_a  :				Any			,       _a  :				int = 512      ):
      snake_case_    							=				x.shape[1] if len(x.shape      ) > 1 else 1
      snake_case_    							=				1 + np.arange(0			,       feature_num * offset			,       _a			,       dtype=np.intaa      )
      snake_case_    							=				x + feature_offset
      return x
def 	snake_case_							(				_a  :				Any			,       _a  :				List[str]=True      ):
      requires_backends(_a			,       ["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(_a      ) + 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(_a      ) + 1

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

      snake_case_							,						snake_case_    							=				algos_graphormer.floyd_warshall(_a      )
      snake_case_    							=				np.amax(_a      )

      snake_case_    							=				algos_graphormer.gen_edge_input(_a			,       _a			,       _a      )
      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(_a			,       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 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			int						,			lowerCAmelCase_	:			Optional[int]=20						,			lowerCAmelCase_	:			Any=False     ) ->							Tuple:
        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	:			Union[str, Any]						,			lowerCAmelCase_	:			List[dict]     ) ->							Dict[str, Any]:
        if self.on_the_fly_processing:
              snake_case_    							=				[preprocess_item(lowerCAmelCase_     ) for i in features]

        if not isinstance(features[0]						,			lowerCAmelCase_     ):
              snake_case_    							=				[vars(lowerCAmelCase_     ) 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(lowerCAmelCase_     )

        snake_case_    							=				torch.zeros(lowerCAmelCase_						,			max_node_num + 1						,			max_node_num + 1						,			dtype=torch.float     )
        snake_case_    							=				torch.zeros(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=torch.long     )
        snake_case_    							=				torch.zeros(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=torch.long     )
        snake_case_    							=				torch.zeros(lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=torch.long     )
        snake_case_    							=				torch.zeros(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=torch.long     )
        snake_case_    							=				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"]:
                    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(lowerCAmelCase_     ) == 1:  # one task
              if isinstance(sample[0]						,			lowerCAmelCase_     ):  # 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



'''simple docstring'''





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				WavLMForSequenceClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["projector.weight"]
      snake_case_    							=				downstream_dict["projector.bias"]
      snake_case_    							=				downstream_dict["model.post_net.linear.weight"]
      snake_case_    							=				downstream_dict["model.post_net.linear.bias"]
      return model
def 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[Any]			,       _a  :				List[str]      ):
      snake_case_    							=				WavLMForAudioFrameClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["model.linear.weight"]
      snake_case_    							=				downstream_dict["model.linear.bias"]
      return model
def 	snake_case_							(				_a  :				Dict			,       _a  :				Tuple			,       _a  :				Any      ):
      snake_case_    							=				WavLMForXVector.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["connector.weight"]
      snake_case_    							=				downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
            snake_case_    							=				downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            snake_case_    							=				downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      snake_case_    							=				downstream_dict["objective.W"]
      return model
@torch.no_grad()
def 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				torch.load(_a			,       map_location="cpu"      )

      snake_case_    							=				checkpoint["Downstream"]

      snake_case_    							=				WavLMConfig.from_pretrained(_a      )
      snake_case_    							=				WavaVecaFeatureExtractor.from_pretrained(
          _a			,       return_attention_mask=_a			,       do_normalize=_a      )

      snake_case_    							=				hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"      ):
            snake_case_    							=				convert_classification(_a			,       _a			,       _a      )
      elif arch.endswith("ForAudioFrameClassification"      ):
            snake_case_    							=				convert_diarization(_a			,       _a			,       _a      )
      elif arch.endswith("ForXVector"      ):
            snake_case_    							=				convert_xvector(_a			,       _a			,       _a      )
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''      )

      if hf_config.use_weighted_layer_sum:
            snake_case_    							=				checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(_a      )
      hf_model.save_pretrained(_a      )


if __name__ == "__main__":
     A							     = argparse.ArgumentParser()
     parser.add_argument(
         "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
     )
     parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
     parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
     parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
     A							     = parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

