
'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a__						:				Tuple           =    logging.get_logger(__name__)

a__						:				List[str]           =    {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     str:







      """simple docstring"""
      for attribute in key.split('.'		):
            __A    		=     getattr(UpperCAmelCase ,					UpperCAmelCase		)

      if weight_type is not None:
            __A    		=     getattr(UpperCAmelCase ,					UpperCAmelCase		).shape
      else:
            __A    		=     hf_pointer.shape

      assert hf_shape == value.shape, (
          f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
          f' {value.shape} for {full_name}'
      )

      if weight_type == "weight":
            __A    		=     value
      elif weight_type == "weight_g":
            __A    		=     value
      elif weight_type == "weight_v":
            __A    		=     value
      elif weight_type == "bias":
            __A    		=     value
      else:
            __A    		=     value

      logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     int:







      """simple docstring"""
      __A    		=     []
      __A    		=     fairseq_model.state_dict()

      __A    		=     hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            __A    		=     False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					hf_model.config.feat_extract_norm == 'group' ,					)
                  __A    		=     True
            else:
                  for key, mapped_key in MAPPING.items():
                        __A    		=     'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

                        if key in name or key.split('w2v_model.'		)[-1] == name.split('.'		)[0]:
                              __A    		=     True
                              if "*" in mapped_key:
                                    __A    		=     name.split(UpperCAmelCase		)[0].split('.'		)[-2]
                                    if not layer_index.isnumeric():
                                          continue
                                    __A    		=     mapped_key.replace('*' ,					UpperCAmelCase		)
                              if "weight_g" in name:
                                    __A    		=     'weight_g'
                              elif "weight_v" in name:
                                    __A    		=     'weight_v'
                              elif "weight" in name:
                                    __A    		=     'weight'
                              elif "bias" in name:
                                    __A    		=     'bias'
                              else:
                                    __A    		=     None
                              set_recursively(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)
                        continue
            if not is_used:
                  unused_weights.append(UpperCAmelCase		)

      logger.warning(f'Unused weights: {unused_weights}'		)





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     List[str]:







      """simple docstring"""
      __A    		=     full_name.split('conv_layers.'		)[-1]
      __A    		=     name.split('.'		)
      __A    		=     int(items[0]		)
      __A    		=     int(items[1]		)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f'{full_name} has size {value.shape}, but'
                      f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
                  )
                  __A    		=     value
                  logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f'{full_name} has size {value.shape}, but'
                      f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
                  )
                  __A    		=     value
                  logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                      " found."
                  )
                  __A    		=     value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f'{full_name} has size {value.shape}, but'
                      f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
                  )
                  __A    		=     value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
      else:
            unused_weights.append(UpperCAmelCase		)





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     Union[str, Any]:







      """simple docstring"""
      __A    		=     SEWDConfig()
      if is_finetuned:
            __A    		=     model.wav_encoder.wav_model.cfg
      else:
            __A    		=     model.cfg

      __A    		=     fs_config.conv_bias
      __A    		=     eval(fs_config.conv_feature_layers		)
      __A    		=     [x[0] for x in conv_layers]
      __A    		=     [x[1] for x in conv_layers]
      __A    		=     [x[2] for x in conv_layers]
      __A    		=     'gelu'
      __A    		=     'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
      __A    		=     0.0
      __A    		=     fs_config.activation_fn.name
      __A    		=     fs_config.encoder_embed_dim
      __A    		=     0.02
      __A    		=     fs_config.encoder_ffn_embed_dim
      __A    		=     1e-5
      __A    		=     fs_config.encoder_layerdrop
      __A    		=     fs_config.encoder_attention_heads
      __A    		=     fs_config.conv_pos_groups
      __A    		=     fs_config.conv_pos
      __A    		=     len(UpperCAmelCase		)
      __A    		=     fs_config.encoder_layers
      __A    		=     fs_config.squeeze_factor
      # DeBERTa-specific parameters:
      __A    		=     fs_config.max_position_embeddings
      __A    		=     fs_config.position_buckets
      __A    		=     fs_config.share_att_key
      __A    		=     fs_config.relative_attention
      __A    		=     fs_config.position_biased_input
      __A    		=     tuple(fs_config.pos_att_type.split('|'		)		)
      __A    		=     fs_config.norm_rel_ebd

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            __A    		=     model.cfg
            __A    		=     fs_config.final_dropout
            __A    		=     fs_config.layerdrop
      __A    		=     fs_config.activation_dropout
      __A    		=     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      __A    		=     fs_config.attention_dropout
      __A    		=     fs_config.dropout_input
      __A    		=     fs_config.dropout
      __A    		=     fs_config.mask_channel_length
      __A    		=     fs_config.mask_channel_prob
      __A    		=     fs_config.mask_length
      __A    		=     fs_config.mask_prob

      __A    		=     'Wav2Vec2FeatureExtractor'
      __A    		=     'Wav2Vec2CTCTokenizer'

      return config





@torch.no_grad()
def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase=None ,					UpperCAmelCase=None ,					UpperCAmelCase=True		)->     Dict:







      """simple docstring"""

      if is_finetuned:
            __A		, __A		, __A    		=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path] ,					arg_overrides={'data': '/'.join(dict_path.split('/'		)[:-1]		)}		)
      else:
            __A		, __A		, __A    		=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

      if config_path is not None:
            __A    		=     SEWDConfig.from_pretrained(UpperCAmelCase		)
      else:
            __A    		=     convert_config(model[0] ,					UpperCAmelCase		)
      __A    		=     model[0].eval()

      __A    		=     True if config.feat_extract_norm == 'layer' else False
      __A    		=     WavaVecaFeatureExtractor(
          feature_size=1 ,					sampling_rate=1_6_0_0_0 ,					padding_value=0 ,					do_normalize=UpperCAmelCase ,					return_attention_mask=UpperCAmelCase ,					)

      if is_finetuned:
            if dict_path:
                  __A    		=     Dictionary.load(UpperCAmelCase		)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  __A    		=     target_dict.pad_index
                  __A    		=     target_dict.bos_index
                  __A    		=     target_dict.pad_index
                  __A    		=     target_dict.bos_index
                  __A    		=     target_dict.eos_index
                  __A    		=     len(target_dict.symbols		)
                  __A    		=     os.path.join(UpperCAmelCase ,					'vocab.json'		)
                  if not os.path.isdir(UpperCAmelCase		):
                        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCAmelCase		)		)
                        return
                  os.makedirs(UpperCAmelCase ,					exist_ok=UpperCAmelCase		)
                  with open(UpperCAmelCase ,					'w' ,					encoding='utf-8'		) as vocab_handle:
                        json.dump(target_dict.indices ,					UpperCAmelCase		)
                  __A    		=     WavaVecaCTCTokenizer(
                      UpperCAmelCase ,					unk_token=target_dict.unk_word ,					pad_token=target_dict.pad_word ,					bos_token=target_dict.bos_word ,					eos_token=target_dict.eos_word ,					word_delimiter_token='|' ,					do_lower_case=UpperCAmelCase ,					)
                  __A    		=     WavaVecaProcessor(feature_extractor=UpperCAmelCase ,					tokenizer=UpperCAmelCase		)
                  processor.save_pretrained(UpperCAmelCase		)

            __A    		=     SEWDForCTC(UpperCAmelCase		)
      else:
            __A    		=     SEWDModel(UpperCAmelCase		)
            feature_extractor.save_pretrained(UpperCAmelCase		)

      recursively_load_weights(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)

      hf_model.save_pretrained(UpperCAmelCase		)


if __name__ == "__main__":
 a__						:				List[Any]           =    argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 a__						:				Optional[int]           =    parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )








'''simple docstring'''





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     Union[str, Any]:







      """simple docstring"""
      print('\nVertex Distance'		)
      for i in range(UpperCAmelCase		):
            if dist[i] != float('inf'		):
                  print(UpperCAmelCase ,					'\t' ,					int(dist[i]		) ,					end='\t'		)
            else:
                  print(UpperCAmelCase ,					'\t' ,					'INF' ,					end='\t'		)
            print()





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     List[Any]:







      """simple docstring"""
      __A    		=     float('inf'		)
      __A    		=     -1
      for i in range(UpperCAmelCase		):
            if (not vset[i]) and mdist[i] < min_val:
                  __A    		=     i
                  __A    		=     mdist[i]
      return min_ind





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     Dict:







      """simple docstring"""
      __A    		=     [float('inf'		) for _ in range(UpperCAmelCase		)]
      __A    		=     [False for _ in range(UpperCAmelCase		)]
      __A    		=     0.0

      for _ in range(v - 1		):
            __A    		=     min_dist(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)
            __A    		=     True

            for i in range(UpperCAmelCase		):
                  if (
                      (not vset[i])
                      and graph[u][i] != float('inf'		)
                      and mdist[u] + graph[u][i] < mdist[i]
                  ):
                        __A    		=     mdist[u] + graph[u][i]

      print_dist(UpperCAmelCase ,					UpperCAmelCase		)


if __name__ == "__main__":
 a__						:				Tuple           =    int(input("Enter number of vertices: ").strip())
 a__						:				List[str]           =    int(input("Enter number of edges: ").strip())

 a__						:				int           =    [[float("inf") for i in range(V)] for j in range(V)]

 for i in range(V):
  a__						:				Optional[int]           =    0.0

 for i in range(E):
  print("\nEdge ", i + 1)
  a__						:				Union[str, Any]           =    int(input("Enter source:").strip())
  a__						:				Tuple           =    int(input("Enter destination:").strip())
  a__						:				int           =    float(input("Enter weight:").strip())
  a__						:				Dict           =    weight

 a__						:				int           =    int(input("\nEnter shortest path source:").strip())
 dijkstra(graph, V, gsrc)






