


'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase							=logging.get_logger(__name__)

lowercase							={
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
lowercase							=[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							Dict	,   __lowerCamelCase	:							Dict	,   __lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							List[Any]							):







   '''simple docstring'''




   for attribute in key.split('.'							):
      _UpperCAmelCase       :       List[str]							   =getattr(__lowerCamelCase	,   __lowerCamelCase							)

   if weight_type is not None:
      _UpperCAmelCase       :       Optional[int]							   =getattr(__lowerCamelCase	,   __lowerCamelCase							).shape
   else:
      _UpperCAmelCase       :       Optional[Any]							   =hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          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":
      _UpperCAmelCase       :       List[Any]							   =value
   elif weight_type == "weight_g":
      _UpperCAmelCase       :       int							   =value
   elif weight_type == "weight_v":
      _UpperCAmelCase       :       Tuple							   =value
   elif weight_type == "bias":
      _UpperCAmelCase       :       Any							   =value
   elif weight_type == "running_mean":
      _UpperCAmelCase       :       List[str]							   =value
   elif weight_type == "running_var":
      _UpperCAmelCase       :       List[str]							   =value
   elif weight_type == "num_batches_tracked":
      _UpperCAmelCase       :       Optional[int]							   =value
   elif weight_type == "inv_freq":
      _UpperCAmelCase       :       List[Any]							   =value
   else:
      _UpperCAmelCase       :       List[str]							   =value

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





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Dict							):







   '''simple docstring'''




   _UpperCAmelCase       :       Dict							   =[]
   _UpperCAmelCase       :       Tuple							   =fairseq_model.state_dict()

   _UpperCAmelCase       :       Tuple							   =hf_model.wavaveca_conformer.feature_extractor

   for name, value in fairseq_dict.items():
      _UpperCAmelCase       :       str							   =False
      if "conv_layers" in name:
         load_conv_layer(
             __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   hf_model.config.feat_extract_norm == 'group'	,   )
         _UpperCAmelCase       :       str							   =True
      else:
         for key, mapped_key in MAPPING.items():
            _UpperCAmelCase       :       Dict							   ='wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
            if key in name or key.split('w2v_model.'							)[-1] == name.split('.'							)[0]:
               _UpperCAmelCase       :       List[Any]							   =True
               if "*" in mapped_key:
                  _UpperCAmelCase       :       Optional[int]							   =name.split(__lowerCamelCase							)[0].split('.'							)[-2]
                  _UpperCAmelCase       :       Any							   =mapped_key.replace('*'	,   __lowerCamelCase							)
               if "pos_bias_u" in name:
                  _UpperCAmelCase       :       Union[str, Any]							   =None
               elif "pos_bias_v" in name:
                  _UpperCAmelCase       :       Tuple							   =None
               elif "weight_g" in name:
                  _UpperCAmelCase       :       Optional[Any]							   ='weight_g'
               elif "weight_v" in name:
                  _UpperCAmelCase       :       int							   ='weight_v'
               elif "bias" in name:
                  _UpperCAmelCase       :       Any							   ='bias'
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  _UpperCAmelCase       :       Optional[int]							   ='weight'
               elif "running_mean" in name:
                  _UpperCAmelCase       :       List[str]							   ='running_mean'
               elif "inv_freq" in name:
                  _UpperCAmelCase       :       Any							   ='inv_freq'
               elif "running_var" in name:
                  _UpperCAmelCase       :       Optional[Any]							   ='running_var'
               elif "num_batches_tracked" in name:
                  _UpperCAmelCase       :       Dict							   ='num_batches_tracked'
               else:
                  _UpperCAmelCase       :       List[Any]							   =None
               set_recursively(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)
            continue
      if not is_used:
         unused_weights.append(__lowerCamelCase							)

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





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							Any							):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =full_name.split('conv_layers.'							)[-1]
   _UpperCAmelCase       :       Tuple							   =name.split('.'							)
   _UpperCAmelCase       :       Tuple							   =int(items[0]							)
   _UpperCAmelCase       :       Optional[int]							   =int(items[1]							)

   if type_id == 0:
      if "bias" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
            raise ValueError(
                f"{full_name} has size {value.shape}, but"
                f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."							)
         _UpperCAmelCase       :       Optional[Any]							   =value
         logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."							)
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
            raise ValueError(
                f"{full_name} has size {value.shape}, but"
                f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."							)
         _UpperCAmelCase       :       Tuple							   =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:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
            raise ValueError(
                f"{full_name} has size {value.shape}, but"
                f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."							)
         _UpperCAmelCase       :       str							   =value
         logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."							)
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
            raise ValueError(
                f"{full_name} has size {value.shape}, but"
                f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."							)
         _UpperCAmelCase       :       Any							   =value
         logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."							)
   else:
      unused_weights.append(__lowerCamelCase							)





@torch.no_grad()
def lowerCamelCase__      (						__lowerCamelCase	:							Dict	,   __lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							Dict=None	,   __lowerCamelCase	:							Tuple=None	,   __lowerCamelCase	:							Dict=True							):







   '''simple docstring'''




   if config_path is not None:
      _UpperCAmelCase       :       Optional[Any]							   =WavaVecaConformerConfig.from_pretrained(__lowerCamelCase	,   hidden_act='swish'							)
   else:
      _UpperCAmelCase       :       int							   =WavaVecaConformerConfig()

   if "rope" in checkpoint_path:
      _UpperCAmelCase       :       Optional[Any]							   ='rotary'

   if is_finetuned:
      if dict_path:
         _UpperCAmelCase       :       Union[str, Any]							   =Dictionary.load(__lowerCamelCase							)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         _UpperCAmelCase       :       Dict							   =target_dict.pad_index
         _UpperCAmelCase       :       Any							   =target_dict.bos_index
         _UpperCAmelCase       :       int							   =target_dict.eos_index
         _UpperCAmelCase       :       List[Any]							   =len(target_dict.symbols							)
         _UpperCAmelCase       :       List[Any]							   =os.path.join(__lowerCamelCase	,   'vocab.json'							)
         if not os.path.isdir(__lowerCamelCase							):
            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__lowerCamelCase							)							)
            return
         os.makedirs(__lowerCamelCase	,   exist_ok=__lowerCamelCase							)
         _UpperCAmelCase       :       int							   =target_dict.indices

         # fairseq has the <pad> and <s> switched
         _UpperCAmelCase       :       Optional[Any]							   =0
         _UpperCAmelCase       :       Tuple							   =1
         with open(__lowerCamelCase	,   'w'	,   encoding='utf-8'							) as vocab_handle:
            json.dump(__lowerCamelCase	,   __lowerCamelCase							)
         _UpperCAmelCase       :       Optional[Any]							   =WavaVecaCTCTokenizer(
             __lowerCamelCase	,   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=__lowerCamelCase	,   )
         _UpperCAmelCase       :       str							   =True if config.feat_extract_norm == 'layer' else False
         _UpperCAmelCase       :       Union[str, Any]							   =WavaVecaFeatureExtractor(
             feature_size=1	,   sampling_rate=1_6_0_0_0	,   padding_value=0	,   do_normalize=__lowerCamelCase	,   return_attention_mask=__lowerCamelCase	,   )
         _UpperCAmelCase       :       List[str]							   =WavaVecaProcessor(feature_extractor=__lowerCamelCase	,   tokenizer=__lowerCamelCase							)
         processor.save_pretrained(__lowerCamelCase							)

      _UpperCAmelCase       :       Any							   =WavaVecaConformerForCTC(__lowerCamelCase							)
   else:
      _UpperCAmelCase       :       List[str]							   =WavaVecaConformerForPreTraining(__lowerCamelCase							)

   if is_finetuned:
      _UpperCAmelCase       :       List[str]							   =fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]	,   arg_overrides={'data': '/'.join(dict_path.split('/'							)[:-1]							)}							)
   else:
      _UpperCAmelCase       :       List[str]							   =argparse.Namespace(task='audio_pretraining'							)
      _UpperCAmelCase       :       List[str]							   =fairseq.tasks.setup_task(__lowerCamelCase							)

      _UpperCAmelCase       :       List[Any]							   =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	,   task=__lowerCamelCase							)

   _UpperCAmelCase       :       Tuple							   =model[0].eval()

   recursively_load_weights(__lowerCamelCase	,   __lowerCamelCase	,   not is_finetuned							)

   hf_wavavec.save_pretrained(__lowerCamelCase							)


if __name__ == "__main__":
 lowercase							=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(
     '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
 )
 lowercase							=parser.parse_args()
 convert_wavaveca_conformer_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )



'''simple docstring'''






import numpy as np






class  __magic_name__							:






       def __init__(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =(0, 0)
          _UpperCAmelCase       :       Any							   =None
          _UpperCAmelCase       :       List[str]							   =0
          _UpperCAmelCase       :       Dict							   =0
          _UpperCAmelCase       :       Dict							   =0






       def __eq__(      self  ,							snake_case)						->  Dict:




          '''simple docstring'''
          return self.position == cell.position






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          print(self.position)






class  __magic_name__							:






       def __init__(      self  ,							snake_case=(5, 5))						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =np.zeros(snake_case)
          _UpperCAmelCase       :       Optional[Any]							   =world_size[0]
          _UpperCAmelCase       :       Any							   =world_size[1]






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          print(self.w)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =[
              (-1, -1),
              (-1, 0),
              (-1, 1),
              (0, -1),
              (0, 1),
              (1, -1),
              (1, 0),
              (1, 1),
          ]
          _UpperCAmelCase       :       int							   =cell.position[0]
          _UpperCAmelCase       :       Optional[Any]							   =cell.position[1]
          _UpperCAmelCase       :       Dict							   =[]
          for n in neughbour_cord:
             _UpperCAmelCase       :       List[str]							   =current_x + n[0]
             _UpperCAmelCase       :       int							   =current_y + n[1]
             if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                _UpperCAmelCase       :       List[Any]							   =Cell()
                _UpperCAmelCase       :       Dict							   =(x, y)
                _UpperCAmelCase       :       Any							   =cell
                neighbours.append(snake_case)
          return neighbours






def lowerCamelCase__      (						__lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =[]
   _UpperCAmelCase       :       Union[str, Any]							   =[]
   _open.append(__lowerCamelCase							)

   while _open:
      _UpperCAmelCase       :       List[str]							   =np.argmin([n.f for n in _open]							)
      _UpperCAmelCase       :       Optional[int]							   =_open[min_f]
      _closed.append(_open.pop(__lowerCamelCase							)							)
      if current == goal:
         break
      for n in world.get_neigbours(__lowerCamelCase							):
         for c in _closed:
            if c == n:
               continue
         _UpperCAmelCase       :       Tuple							   =current.g + 1
         _UpperCAmelCase   ,	_UpperCAmelCase       :       Any							   =n.position
         _UpperCAmelCase   ,	_UpperCAmelCase       :       List[str]							   =goal.position
         _UpperCAmelCase       :       Optional[int]							   =(ya - ya) ** 2 + (xa - xa) ** 2
         _UpperCAmelCase       :       Optional[Any]							   =n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(__lowerCamelCase							)
   _UpperCAmelCase       :       str							   =[]
   while current.parent is not None:
      path.append(current.position							)
      _UpperCAmelCase       :       Any							   =current.parent
   path.append(current.position							)
   return path[::-1]


if __name__ == "__main__":
 lowercase							=Gridworld()
 # Start position and goal
 lowercase							=Cell()
 lowercase							=(0, 0)
 lowercase							=Cell()
 lowercase							=(4, 4)
 print(F"""path from {start.position} to {goal.position}""")
 lowercase							=astar(world, start, goal)
 #   Just for visual reasons.
 for i in s:
  lowercase							=1
 print(world.w)

