from __future__ import annotations

import math
import random
from typing import Any



class  UpperCamelCase				:

     '''simple docstring'''

     def __init__(					self						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				0
           _lowerCamelCase			    =				0



     def 		UpperCamelCase_						(					self						) ->				bool:






           """simple docstring"""
           return self.head == self.tail



     def 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           self.data.append(A_						)
           _lowerCamelCase			    =				self.tail + 1



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.data[self.head]
           _lowerCamelCase			    =				self.head + 1
           return ret



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return self.tail - self.head





     def 		UpperCamelCase_						(					self						) ->				None:






           """simple docstring"""
           print(self.data						)
           print('''**************'''						)
           print(self.data[self.head : self.tail]						)





class  UpperCamelCase				:

     '''simple docstring'''

     def __init__(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				data
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				1



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           return self.data



     def 		UpperCamelCase_						(					self						) ->				MyNode | None:






           """simple docstring"""
           return self.left



     def 		UpperCamelCase_						(					self						) ->				MyNode | None:






           """simple docstring"""
           return self.right



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return self.height



     def 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				data



     def 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				node



     def 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				node





     def 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				height





def       __magic_name__(  __UpperCAmelCase		)						->    int:



      '''simple docstring'''

      if node is None:
            return 0
      return node.get_height()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    int:



      '''simple docstring'''

      if a > b:
            return a
      return b
def       __magic_name__(  __UpperCAmelCase		)						->    MyNode:



      '''simple docstring'''

      print('''left rotation node:''' ,						node.get_data()		)
      _lowerCamelCase			    =				node.get_left()
      assert ret is not None
      node.set_left(ret.get_right()		)
      ret.set_right(__UpperCAmelCase		)
      _lowerCamelCase			    =				my_max(get_height(node.get_right()		) ,						get_height(node.get_left()		)		) + 1
      node.set_height(__UpperCAmelCase		)
      _lowerCamelCase			    =				my_max(get_height(ret.get_right()		) ,						get_height(ret.get_left()		)		) + 1
      ret.set_height(__UpperCAmelCase		)
      return ret
def       __magic_name__(  __UpperCAmelCase		)						->    MyNode:



      '''simple docstring'''

      print('''right rotation node:''' ,						node.get_data()		)
      _lowerCamelCase			    =				node.get_right()
      assert ret is not None
      node.set_right(ret.get_left()		)
      ret.set_left(__UpperCAmelCase		)
      _lowerCamelCase			    =				my_max(get_height(node.get_right()		) ,						get_height(node.get_left()		)		) + 1
      node.set_height(__UpperCAmelCase		)
      _lowerCamelCase			    =				my_max(get_height(ret.get_right()		) ,						get_height(ret.get_left()		)		) + 1
      ret.set_height(__UpperCAmelCase		)
      return ret
def       __magic_name__(  __UpperCAmelCase		)						->    MyNode:



      '''simple docstring'''

      _lowerCamelCase			    =				node.get_left()
      assert left_child is not None
      node.set_left(left_rotation(__UpperCAmelCase		)		)
      return right_rotation(__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase		)						->    MyNode:



      '''simple docstring'''

      _lowerCamelCase			    =				node.get_right()
      assert right_child is not None
      node.set_right(right_rotation(__UpperCAmelCase		)		)
      return left_rotation(__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    MyNode | None:



      '''simple docstring'''

      if node is None:
            return MyNode(__UpperCAmelCase		)
      if data < node.get_data():
            node.set_left(insert_node(node.get_left() ,						__UpperCAmelCase		)		)
            if (
                get_height(node.get_left()		) - get_height(node.get_right()		) == 2
            ):  # an unbalance detected
                  _lowerCamelCase			    =				node.get_left()
                  assert left_child is not None
                  if (
                      data < left_child.get_data()
                  ):  # new node is the left child of the left child
                        _lowerCamelCase			    =				right_rotation(__UpperCAmelCase		)
                  else:
                        _lowerCamelCase			    =				lr_rotation(__UpperCAmelCase		)
      else:
            node.set_right(insert_node(node.get_right() ,						__UpperCAmelCase		)		)
            if get_height(node.get_right()		) - get_height(node.get_left()		) == 2:
                  _lowerCamelCase			    =				node.get_right()
                  assert right_child is not None
                  if data < right_child.get_data():
                        _lowerCamelCase			    =				rl_rotation(__UpperCAmelCase		)
                  else:
                        _lowerCamelCase			    =				left_rotation(__UpperCAmelCase		)
      _lowerCamelCase			    =				my_max(get_height(node.get_right()		) ,						get_height(node.get_left()		)		) + 1
      node.set_height(__UpperCAmelCase		)
      return node
def       __magic_name__(  __UpperCAmelCase		)						->    Any:



      '''simple docstring'''

      while True:
            _lowerCamelCase			    =				root.get_right()
            if right_child is None:
                  break
            _lowerCamelCase			    =				right_child
      return root.get_data()
def       __magic_name__(  __UpperCAmelCase		)						->    Any:



      '''simple docstring'''

      while True:
            _lowerCamelCase			    =				root.get_left()
            if left_child is None:
                  break
            _lowerCamelCase			    =				left_child
      return root.get_data()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    MyNode | None:



      '''simple docstring'''

      _lowerCamelCase			    =				root.get_left()
      _lowerCamelCase			    =				root.get_right()
      if root.get_data() == data:
            if left_child is not None and right_child is not None:
                  _lowerCamelCase			    =				get_left_most(__UpperCAmelCase		)
                  root.set_data(__UpperCAmelCase		)
                  root.set_right(del_node(__UpperCAmelCase ,						__UpperCAmelCase		)		)
            elif left_child is not None:
                  _lowerCamelCase			    =				left_child
            elif right_child is not None:
                  _lowerCamelCase			    =				right_child
            else:
                  return None
      elif root.get_data() > data:
            if left_child is None:
                  print('''No such data'''		)
                  return root
            else:
                  root.set_left(del_node(__UpperCAmelCase ,						__UpperCAmelCase		)		)
      else:  # root.get_data() < data
            if right_child is None:
                  return root
            else:
                  root.set_right(del_node(__UpperCAmelCase ,						__UpperCAmelCase		)		)

      if get_height(__UpperCAmelCase		) - get_height(__UpperCAmelCase		) == 2:
            assert right_child is not None
            if get_height(right_child.get_right()		) > get_height(right_child.get_left()		):
                  _lowerCamelCase			    =				left_rotation(__UpperCAmelCase		)
            else:
                  _lowerCamelCase			    =				rl_rotation(__UpperCAmelCase		)
      elif get_height(__UpperCAmelCase		) - get_height(__UpperCAmelCase		) == -2:
            assert left_child is not None
            if get_height(left_child.get_left()		) > get_height(left_child.get_right()		):
                  _lowerCamelCase			    =				right_rotation(__UpperCAmelCase		)
            else:
                  _lowerCamelCase			    =				lr_rotation(__UpperCAmelCase		)
      _lowerCamelCase			    =				my_max(get_height(root.get_right()		) ,						get_height(root.get_left()		)		) + 1
      root.set_height(__UpperCAmelCase		)
      return root



class  UpperCamelCase				:

     '''simple docstring'''

     def __init__(					self						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				None



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return get_height(self.root						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           print('''insert:''' + str(A_						)						)
           _lowerCamelCase			    =				insert_node(self.root				,				A_						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           print('''delete:''' + str(A_						)						)
           if self.root is None:
                 print('''Tree is empty!'''						)
                 return
           _lowerCamelCase			    =				del_node(self.root				,				A_						)





     def __str__(					self				,				) ->				str:  # a level traversale, gives a more intuitive look on the tree






           """simple docstring"""
           _lowerCamelCase			    =				''''''
           _lowerCamelCase			    =				MyQueue()
           q.push(self.root						)
           _lowerCamelCase			    =				self.get_height()
           if layer == 0:
                 return output
           _lowerCamelCase			    =				0
           while not q.is_empty():
                 _lowerCamelCase			    =				q.pop()
                 _lowerCamelCase			    =				''' ''' * int(math.pow(2				,				layer - 1						)						)
                 output += space
                 if node is None:
                       output += "*"
                       q.push(A_						)
                       q.push(A_						)
                 else:
                       output += str(node.get_data()						)
                       q.push(node.get_left()						)
                       q.push(node.get_right()						)
                 output += space
                 _lowerCamelCase			    =				cnt + 1
                 for i in range(1_00						):
                       if cnt == math.pow(2				,				A_						) - 1:
                             _lowerCamelCase			    =				layer - 1
                             if layer == 0:
                                   output += "\n*************************************"
                                   return output
                             output += "\n"
                             break
           output += "\n*************************************"
           return output





def       __magic_name__(  )						->    None:



      '''simple docstring'''

      import doctest

      doctest.testmod()


if __name__ == "__main__":
     _test()
     snake_case__				= AVLtree()
     snake_case__				= list(range(10))
     random.shuffle(lst)
     for i in lst:
          t.insert(i)
          print(str(t))
     random.shuffle(lst)
     for i in lst:
          t.del_node(i)
          print(str(t))
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

snake_case__				= {
    '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',
    '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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
snake_case__				= [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Optional[Any]:



      '''simple docstring'''

      for attribute in key.split('''.'''		):
            if is_finetuned:
                  if attribute in ["quantizer", "project_q", "project_hid"]:
                        # those layers are only relevant for pretraining and should be dropped
                        return

                  if attribute == "ctc_proj":
                        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                        _lowerCamelCase			    =				'''lm_head'''

            _lowerCamelCase			    =				getattr(__UpperCAmelCase ,						__UpperCAmelCase		)

      if weight_type is not None:
            _lowerCamelCase			    =				getattr(__UpperCAmelCase ,						__UpperCAmelCase		).shape
      else:
            _lowerCamelCase			    =				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":
            _lowerCamelCase			    =				value
      elif weight_type == "weight_g":
            _lowerCamelCase			    =				value
      elif weight_type == "weight_v":
            _lowerCamelCase			    =				value
      elif weight_type == "bias":
            _lowerCamelCase			    =				value
      else:
            _lowerCamelCase			    =				value

      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      _lowerCamelCase			    =				fairseq_model.state_dict()

      _lowerCamelCase			    =				hf_model.unispeech.feature_extractor

      for name, value in fairseq_dict.items():
            _lowerCamelCase			    =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						hf_model.config.feat_extract_norm == '''group''' ,						)
                  _lowerCamelCase			    =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        _lowerCamelCase			    =				'''unispeech.''' + 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]:
                              _lowerCamelCase			    =				True
                              if "*" in mapped_key:
                                    _lowerCamelCase			    =				name.split(__UpperCAmelCase		)[0].split('''.'''		)[-2]
                                    _lowerCamelCase			    =				mapped_key.replace('''*''' ,						__UpperCAmelCase		)
                              if "weight_g" in name:
                                    _lowerCamelCase			    =				'''weight_g'''
                              elif "weight_v" in name:
                                    _lowerCamelCase			    =				'''weight_v'''
                              elif "bias" in name:
                                    _lowerCamelCase			    =				'''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    _lowerCamelCase			    =				'''weight'''
                              else:
                                    _lowerCamelCase			    =				None
                              set_recursively(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
                        continue
            if not is_used:
                  unused_weights.append(__UpperCAmelCase		)

      logger.warning(F'Unused weights: {unused_weights}'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				full_name.split('''conv_layers.'''		)[-1]
      _lowerCamelCase			    =				name.split('''.'''		)
      _lowerCamelCase			    =				int(items[0]		)
      _lowerCamelCase			    =				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.'
                  )
                  _lowerCamelCase			    =				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.'
                  )
                  _lowerCamelCase			    =				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."
                  )
                  _lowerCamelCase			    =				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.'
                  )
                  _lowerCamelCase			    =				value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
      else:
            unused_weights.append(__UpperCAmelCase		)
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=True		)						->    Union[str, Any]:



      '''simple docstring'''

      if config_path is not None:
            _lowerCamelCase			    =				UniSpeechConfig.from_pretrained(__UpperCAmelCase		)
      else:
            _lowerCamelCase			    =				UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  _lowerCamelCase			    =				Dictionary.load_from_json(__UpperCAmelCase		)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  _lowerCamelCase			    =				target_dict.pad_index
                  _lowerCamelCase			    =				target_dict.bos_index
                  _lowerCamelCase			    =				target_dict.eos_index
                  _lowerCamelCase			    =				len(target_dict.symbols		)
                  _lowerCamelCase			    =				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		)
                  _lowerCamelCase			    =				target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  _lowerCamelCase			    =				42
                  _lowerCamelCase			    =				43
                  with open(__UpperCAmelCase ,						'''w''' ,						encoding='''utf-8'''		) as vocab_handle:
                        json.dump(__UpperCAmelCase ,						__UpperCAmelCase		)
                  _lowerCamelCase			    =				WavaVecaPhonemeCTCTokenizer(
                      __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 ,						)
                  _lowerCamelCase			    =				True if config.feat_extract_norm == '''layer''' else False
                  _lowerCamelCase			    =				WavaVecaFeatureExtractor(
                      feature_size=1 ,						sampling_rate=1_6000 ,						padding_value=0 ,						do_normalize=__UpperCAmelCase ,						return_attention_mask=__UpperCAmelCase ,						)
                  _lowerCamelCase			    =				WavaVecaProcessor(feature_extractor=__UpperCAmelCase ,						tokenizer=__UpperCAmelCase		)
                  processor.save_pretrained(__UpperCAmelCase		)

            _lowerCamelCase			    =				UniSpeechForCTC(__UpperCAmelCase		)
      else:
            _lowerCamelCase			    =				UniSpeechForPreTraining(__UpperCAmelCase		)

      if is_finetuned:
            _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path] ,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		), '''w2v_path''': checkpoint_path}		)
      else:
            _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

      _lowerCamelCase			    =				model[0].eval()

      recursively_load_weights(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)

      hf_unispeech.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__				= 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__				= parser.parse_args()
     convert_unispeech_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )