




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowerCAmelCase_						=			{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
lowerCAmelCase_						=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							Tuple:





     '''simple docstring'''


     for attribute in key.split('''.'''   ):
          lowercase :       Optional[int]       =			getattr(__lowerCAmelCase			,		__lowerCAmelCase   )

     if weight_type is not None:
          lowercase :       Any       =			getattr(__lowerCAmelCase			,		__lowerCAmelCase   ).shape
     else:
          lowercase :       List[str]       =			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":
          lowercase :       Optional[int]       =			value
     elif weight_type == "weight_g":
          lowercase :       List[str]       =			value
     elif weight_type == "weight_v":
          lowercase :       Tuple       =			value
     elif weight_type == "bias":
          lowercase :       Any       =			value
     else:
          lowercase :       Optional[int]       =			value

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






def  snake_case(    __magic_name__			,		__magic_name__   )			->							int:





     '''simple docstring'''


     lowercase :       int       =			[]
     lowercase :       List[Any]       =			fairseq_model.state_dict()

     lowercase :       Union[str, Any]       =			hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          lowercase :       int       =			False
          if "conv_layers" in name:
               load_conv_layer(
                   __lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		hf_model.config.feat_extract_norm == '''group'''			,		)
               lowercase :       str       =			True
          else:
               for key, mapped_key in MAPPING.items():
                    lowercase :       Union[str, Any]       =			'''unispeech_sat.''' + 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]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('''.'''   )[:-1]   ) != key):
                              # special case since naming is very similar
                              continue
                         lowercase :       str       =			True
                         if "*" in mapped_key:
                              lowercase :       List[Any]       =			name.split(__lowerCAmelCase   )[0].split('''.'''   )[-2]
                              lowercase :       Optional[int]       =			mapped_key.replace('''*'''			,		__lowerCAmelCase   )
                         if "weight_g" in name:
                              lowercase :       Dict       =			'''weight_g'''
                         elif "weight_v" in name:
                              lowercase :       int       =			'''weight_v'''
                         elif "bias" in name:
                              lowercase :       Tuple       =			'''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              lowercase :       Optional[int]       =			'''weight'''
                         else:
                              lowercase :       str       =			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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							Optional[int]:





     '''simple docstring'''


     lowercase :       int       =			full_name.split('''conv_layers.'''   )[-1]
     lowercase :       str       =			name.split('''.'''   )
     lowercase :       Any       =			int(items[0]   )
     lowercase :       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."""   )
               lowercase :       Union[str, 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."""   )
               lowercase :       Optional[Any]       =			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[layer_id].layer_norm.bias.data.shape} was found."""   )
               lowercase :       Optional[int]       =			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[layer_id].layer_norm.weight.data.shape} was found."""   )
               lowercase :       Optional[int]       =			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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__=None			,		__magic_name__=None			,		__magic_name__=True   )			->							Tuple:





     '''simple docstring'''


     if config_path is not None:
          lowercase :       List[Any]       =			UniSpeechSatConfig.from_pretrained(__lowerCAmelCase   )
     else:
          lowercase :       Optional[Any]       =			UniSpeechSatConfig()

     lowercase :       Optional[Any]       =			''''''

     if is_finetuned:
          lowercase :       Union[str, Any]       =			UniSpeechSatForCTC(__lowerCAmelCase   )
     else:
          lowercase :       str       =			UniSpeechSatForPreTraining(__lowerCAmelCase   )

     lowercase      ,	lowercase      ,	lowercase :       Optional[Any]       =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]			,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''   )[:-1]   )}   )
     lowercase :       Union[str, Any]       =			model[0].eval()

     recursively_load_weights(__lowerCAmelCase			,		__lowerCAmelCase   )

     hf_wavavec.save_pretrained(__lowerCAmelCase   )


if __name__ == "__main__":
 lowerCAmelCase_						=			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'
 )
 lowerCAmelCase_						=			parser.parse_args()
 convert_unispeech_sat_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )





from __future__ import annotations

import time

lowerCAmelCase_						=			list[tuple[int, int]]

lowerCAmelCase_						=			[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase_						=			[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right
class 						_A :






  def __init__(  self		:     List[Any]				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     Node | None				)		->   str:






       """simple docstring"""







       lowercase :       Any       =			pos_x
       lowercase :       Union[str, Any]       =			pos_y
       lowercase :       int       =			(pos_y, pos_x)
       lowercase :       List[str]       =			goal_x
       lowercase :       Optional[int]       =			goal_y
       lowercase :       Union[str, Any]       =			parent


class 						_A :






  def __init__(  self		:     Optional[Any]				,						_A		:     tuple[int, int]				,						_A		:     tuple[int, int]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       int       =			Node(start[1]				,						start[0]				,						goal[1]				,						goal[0]				,						_A				)
       lowercase :       int       =			Node(goal[1]				,						goal[0]				,						goal[1]				,						goal[0]				,						_A				)

       lowercase :       List[str]       =			[self.start]
       lowercase :       Optional[int]       =			False






  def  __a      (  self		:     Dict				)		->   Path | None:






       """simple docstring"""







       while self.node_queue:
            lowercase :       Optional[Any]       =			self.node_queue.pop(0				)

            if current_node.pos == self.target.pos:
                 lowercase :       int       =			True
                 return self.retrace_path(_A				)

            lowercase :       Union[str, Any]       =			self.get_successors(_A				)

            for node in successors:
                 self.node_queue.append(_A				)

       if not self.reached:
            return [self.start.pos]
       return None






  def  __a      (  self		:     str				,						_A		:     Node				)		->   list[Node]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			[]
       for action in delta:
            lowercase :       List[str]       =			parent.pos_x + action[1]
            lowercase :       str       =			parent.pos_y + action[0]
            if not (0 <= pos_x <= len(grid[0]				) - 1 and 0 <= pos_y <= len(_A				) - 1):
                 continue

            if grid[pos_y][pos_x] != 0:
                 continue

            successors.append(
                Node(_A				,						_A				,						self.target.pos_y				,						self.target.pos_x				,						_A				)				)
       return successors






  def  __a      (  self		:     Any				,						_A		:     Node | None				)		->   Path:






       """simple docstring"""







       lowercase :       Optional[int]       =			node
       lowercase :       Dict       =			[]
       while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x)				)
            lowercase :       Tuple       =			current_node.parent
       path.reverse()
       return path


class 						_A :






  def __init__(  self		:     Optional[int]				,						_A		:     List[str]				,						_A		:     List[str]				)		->   int:






       """simple docstring"""







       lowercase :       str       =			BreadthFirstSearch(_A				,						_A				)
       lowercase :       Optional[Any]       =			BreadthFirstSearch(_A				,						_A				)
       lowercase :       List[str]       =			False






  def  __a      (  self		:     Tuple				)		->   Path | None:






       """simple docstring"""







       while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
            lowercase :       str       =			self.fwd_bfs.node_queue.pop(0				)
            lowercase :       Tuple       =			self.bwd_bfs.node_queue.pop(0				)

            if current_bwd_node.pos == current_fwd_node.pos:
                 lowercase :       Optional[Any]       =			True
                 return self.retrace_bidirectional_path(
                     _A				,						_A				)

            lowercase :       Optional[int]       =			current_bwd_node
            lowercase :       Optional[int]       =			current_fwd_node

            lowercase :       Union[str, Any]       =			{
                self.fwd_bfs: self.fwd_bfs.get_successors(_A				),
                self.bwd_bfs: self.bwd_bfs.get_successors(_A				),
            }

            for bfs in [self.fwd_bfs, self.bwd_bfs]:
                 for node in successors[bfs]:
                      bfs.node_queue.append(_A				)

       if not self.reached:
            return [self.fwd_bfs.start.pos]
       return None






  def  __a      (  self		:     List[Any]				,						_A		:     Node				,						_A		:     Node				)		->   Path:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.fwd_bfs.retrace_path(_A				)
       lowercase :       Union[str, Any]       =			self.bwd_bfs.retrace_path(_A				)
       bwd_path.pop()
       bwd_path.reverse()
       lowercase :       Optional[int]       =			fwd_path + bwd_path
       return path


if __name__ == "__main__":
 # all coordinates are given in format [y,x]
 import doctest

 doctest.testmod()
 lowerCAmelCase_						=			(0, 0)
 lowerCAmelCase_						=			(len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 lowerCAmelCase_						=			time.time()
 lowerCAmelCase_						=			BreadthFirstSearch(init, goal)
 lowerCAmelCase_						=			bfs.search()
 lowerCAmelCase_						=			time.time() - start_bfs_time

 print('Unidirectional BFS computation time : ', bfs_time)

 lowerCAmelCase_						=			time.time()
 lowerCAmelCase_						=			BidirectionalBreadthFirstSearch(init, goal)
 lowerCAmelCase_						=			bd_bfs.search()
 lowerCAmelCase_						=			time.time() - start_bd_bfs_time

 print('Bidirectional BFS computation time : ', bd_bfs_time)