





from collections.abc import Sequence
from queue import Queue





class A_					:




   def __init__(self	:Dict    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:int=None    ,   _UpperCamelCase	:Optional[Any]=None )->					Dict:
         __A          =				start
         __A          =				end
         __A          =				val
         __A          =				(start + end) // 2
         __A          =				left
         __A          =				right






   def __repr__(self	:Any )->					Any:
         return f"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""





class A_					:




   def __init__(self	:str    ,   _UpperCamelCase	:Sequence    ,   _UpperCamelCase	:Optional[int] )->					Tuple:
         __A          =				collection
         __A          =				function
         if self.collection:
               __A          =				self._build_tree(0    ,   len(_UpperCamelCase ) - 1 )




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:Dict )->					List[Any]:
         self._update_tree(self.root    ,   _UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int )->					List[str]:
         return self._query_range(self.root    ,   _UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Tuple )->					Optional[int]:
         if start == end:
               return SegmentTreeNode(_UpperCamelCase    ,   _UpperCamelCase    ,   self.collection[start] )
         __A          =				(start + end) // 2
         __A          =				self._build_tree(_UpperCamelCase    ,   _UpperCamelCase )
         __A          =				self._build_tree(mid + 1    ,   _UpperCamelCase )
         return SegmentTreeNode(_UpperCamelCase    ,   _UpperCamelCase    ,   self.fn(left.val    ,   right.val )    ,   _UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:List[Any] )->					List[Any]:
         if node.start == i and node.end == i:
               __A          =				val
               return
         if i <= node.mid:
               self._update_tree(node.left    ,   _UpperCamelCase    ,   _UpperCamelCase )
         else:
               self._update_tree(node.right    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				self.fn(node.left.val    ,   node.right.val )




   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Any )->					Optional[Any]:
         if node.start == i and node.end == j:
               return node.val

         if i <= node.mid:
               if j <= node.mid:
                     # range in left child tree
                     return self._query_range(node.left    ,   _UpperCamelCase    ,   _UpperCamelCase )
               else:
                     # range in left child tree and right child tree
                     return self.fn(
                         self._query_range(node.left    ,   _UpperCamelCase    ,   node.mid )    ,   self._query_range(node.right    ,   node.mid + 1    ,   _UpperCamelCase )    ,   )
         else:
               # range in right child tree
               return self._query_range(node.right    ,   _UpperCamelCase    ,   _UpperCamelCase )






   def _lowerCAmelCase      (self	:Dict )->					Any:
         if self.root is not None:
               __A          =				Queue()
               queue.put(self.root )
               while not queue.empty():
                     __A          =				queue.get()
                     yield node

                     if node.left is not None:
                           queue.put(node.left )

                     if node.right is not None:
                           queue.put(node.right )


if __name__ == "__main__":
       import operator

       for fn in [operator.add, max, min]:
              print('*' * 50)
              snake_case__					:							Tuple													=     SegmentTree([2, 1, 5, 3, 4], fn)
              for node in arr.traverse():
                     print(node)
              print()

              arr.update(1, 5)
              for node in arr.traverse():
                     print(node)
              print()

              print(arr.query_range(3, 4))  # 7
              print(arr.query_range(2, 2))  # 5
              print(arr.query_range(1, 3))  # 13
              print()












import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case__					:							Dict													=     logging.get_logger(__name__)

snake_case__					:							Optional[Any]													=     {
    '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',
}
snake_case__					:							Optional[int]													=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]



def _a				(	lowerCamelCase:   List[Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   int )	->						List[str]:







      '''simple docstring'''






      for attribute in key.split('''.''' ):
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase )

      if weight_type is not None:
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase ).shape
      else:
            __A          =				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":
            __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 _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Optional[int] )	->						Tuple:







      '''simple docstring'''






      __A          =				[]
      __A          =				fairseq_model.state_dict()

      __A          =				hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            __A          =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				hf_model.config.feat_extract_norm == '''group'''					,				)
                  __A          =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        __A          =				'''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
                              __A          =				True
                              if "*" in mapped_key:
                                    __A          =				name.split(lowerCamelCase )[0].split('''.''' )[-2]
                                    __A          =				mapped_key.replace('''*'''					,				lowerCamelCase )
                              if "weight_g" in name:
                                    __A          =				'''weight_g'''
                              elif "weight_v" in name:
                                    __A          =				'''weight_v'''
                              elif "bias" in name:
                                    __A          =				'''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    __A          =				'''weight'''
                              else:
                                    __A          =				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 _a				(	lowerCamelCase:   int					,				lowerCamelCase:   Any					,				lowerCamelCase:   int					,				lowerCamelCase:   int					,				lowerCamelCase:   List[str] )	->						Union[str, Any]:







      '''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:
                  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.""" )
                  __A          =				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.""" )
                  __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:
                  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.""" )
                  __A          =				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.""" )
                  __A          =				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 _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   int					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[int]=True )	->						List[Any]:







      '''simple docstring'''






      if config_path is not None:
            __A          =				UniSpeechSatConfig.from_pretrained(lowerCamelCase )
      else:
            __A          =				UniSpeechSatConfig()

      __A          =				''''''

      if is_finetuned:
            __A          =				UniSpeechSatForCTC(lowerCamelCase )
      else:
            __A          =				UniSpeechSatForPreTraining(lowerCamelCase )

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

      recursively_load_weights(lowerCamelCase					,				lowerCamelCase )

      hf_wavavec.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
       snake_case__					:							Tuple													=     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'
       )
       snake_case__					:							Any													=     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
       )





