

'''simple docstring'''

from __future__ import annotations

import unittest
from collections.abc import Iterator







class lowerCamelCase   :

				def __init__(       self,						lowercase_,						lowercase_ )    ->							None:
					snake_case							=					label
					snake_case							=					parent
					snake_case							=					None
					snake_case							=					None







class lowerCamelCase   :

				def __init__(       self )    ->							None:
					snake_case							=					None

				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					None

				def 					_lowerCamelCase    (       self )    ->							bool:
					return self.root is None

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							None:
					snake_case							=					self._put(self.root,						lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_ = None )    ->							Node:
					if node is None:
						snake_case							=					Node(lowercase_,						lowercase_ )
					else:
						if label < node.label:
							snake_case							=					self._put(node.left,						lowercase_,						lowercase_ )
						elif label > node.label:
							snake_case							=					self._put(node.right,						lowercase_,						lowercase_ )
						else:
							snake_case							=					F'''Node with label {label} already exists'''
							raise Exception(lowercase_ )

					return node

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							Node:
					return self._search(self.root,						lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							Node:
					if node is None:
						snake_case							=					F'''Node with label {label} does not exist'''
						raise Exception(lowercase_ )
					else:
						if label < node.label:
							snake_case							=					self._search(node.left,						lowercase_ )
						elif label > node.label:
							snake_case							=					self._search(node.right,						lowercase_ )

					return node

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							None:
					snake_case							=					self.search(lowercase_ )
					if node.right and node.left:
						snake_case							=					self._get_lowest_node(node.right )
						snake_case							=					node.left
						snake_case							=					node.right
						snake_case							=					lowest_node
						if node.right:
							snake_case							=					lowest_node
						self._reassign_nodes(lowercase_,						lowercase_ )
					elif not node.right and node.left:
						self._reassign_nodes(lowercase_,						node.left )
					elif node.right and not node.left:
						self._reassign_nodes(lowercase_,						node.right )
					else:
						self._reassign_nodes(lowercase_,						lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							None:
					if new_children:
						snake_case							=					node.parent

					if node.parent:
						if node.parent.right == node:
							snake_case							=					new_children
						else:
							snake_case							=					new_children
					else:
						snake_case							=					new_children

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							Node:
					if node.left:
						snake_case							=					self._get_lowest_node(node.left )
					else:
						snake_case							=					node
						self._reassign_nodes(lowercase_,						node.right )

					return lowest_node

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							bool:
					try:
						self.search(lowercase_ )
						return True
					except Exception:
						return False

				def 					_lowerCamelCase    (       self )    ->							int:
					if self.root is None:
						raise Exception('Binary search tree is empty' )

					snake_case							=					self.root
					while node.right is not None:
						snake_case							=					node.right

					return node.label

				def 					_lowerCamelCase    (       self )    ->							int:
					if self.root is None:
						raise Exception('Binary search tree is empty' )

					snake_case							=					self.root
					while node.left is not None:
						snake_case							=					node.left

					return node.label

				def 					_lowerCamelCase    (       self )    ->							Iterator[Node]:
					return self._inorder_traversal(self.root )

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							Iterator[Node]:
					if node is not None:
						yield from self._inorder_traversal(node.left )
						yield node
						yield from self._inorder_traversal(node.right )

				def 					_lowerCamelCase    (       self )    ->							Iterator[Node]:
					return self._preorder_traversal(self.root )






				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							Iterator[Node]:
					if node is not None:
						yield node
						yield from self._preorder_traversal(node.left )
						yield from self._preorder_traversal(node.right )







class lowerCamelCase   (    unittest.TestCase				):

				@staticmethod
				def 					_lowerCamelCase    (       )    ->							BinarySearchTree:
					snake_case							=					BinarySearchTree()
					t.put(8 )
					t.put(3 )
					t.put(6 )
					t.put(1 )
					t.put(10 )
					t.put(14 )
					t.put(13 )
					t.put(4 )
					t.put(7 )
					t.put(5 )

					return t

				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					BinarySearchTree()
					assert t.is_empty()

					t.put(8 )
					assert t.root is not None
					assert t.root.parent is None
					assert t.root.label == 8

					t.put(10 )
					assert t.root.right is not None
					assert t.root.right.parent == t.root
					assert t.root.right.label == 10

					t.put(3 )
					assert t.root.left is not None
					assert t.root.left.parent == t.root
					assert t.root.left.label == 3

					t.put(6 )
					assert t.root.left.right is not None
					assert t.root.left.right.parent == t.root.left
					assert t.root.left.right.label == 6

					t.put(1 )
					assert t.root.left.left is not None
					assert t.root.left.left.parent == t.root.left
					assert t.root.left.left.label == 1

					with self.assertRaises(lowercase_ ):  # noqa: B017
						t.put(1 )

				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					self._get_binary_search_tree()

					snake_case							=					t.search(6 )
					assert node.label == 6

					snake_case							=					t.search(13 )
					assert node.label == 13

					with self.assertRaises(lowercase_ ):  # noqa: B017
						t.search(2 )

				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					self._get_binary_search_tree()

					t.remove(13 )
					assert t.root is not None
					assert t.root.right is not None
					assert t.root.right.right is not None
					assert t.root.right.right.right is None
					assert t.root.right.right.left is None

					t.remove(7 )
					assert t.root.left is not None
					assert t.root.left.right is not None
					assert t.root.left.right.left is not None
					assert t.root.left.right.right is None
					assert t.root.left.right.left.label == 4

					t.remove(6 )
					assert t.root.left.left is not None
					assert t.root.left.right.right is not None
					assert t.root.left.left.label == 1
					assert t.root.left.right.label == 4
					assert t.root.left.right.right.label == 5
					assert t.root.left.right.left is None
					assert t.root.left.left.parent == t.root.left
					assert t.root.left.right.parent == t.root.left

					t.remove(3 )
					assert t.root is not None
					assert t.root.left.label == 4
					assert t.root.left.right.label == 5
					assert t.root.left.left.label == 1
					assert t.root.left.parent == t.root
					assert t.root.left.left.parent == t.root.left
					assert t.root.left.right.parent == t.root.left

					t.remove(4 )
					assert t.root.left is not None
					assert t.root.left.left is not None
					assert t.root.left.label == 5
					assert t.root.left.right is None
					assert t.root.left.left.label == 1
					assert t.root.left.parent == t.root
					assert t.root.left.left.parent == t.root.left

				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					self._get_binary_search_tree()

					t.remove(3 )
					assert t.root is not None
					assert t.root.left is not None
					assert t.root.left.left is not None
					assert t.root.left.right is not None
					assert t.root.left.right.left is not None
					assert t.root.left.right.right is not None
					assert t.root.left.label == 4
					assert t.root.left.right.label == 6
					assert t.root.left.left.label == 1
					assert t.root.left.right.right.label == 7
					assert t.root.left.right.left.label == 5
					assert t.root.left.parent == t.root
					assert t.root.left.right.parent == t.root.left
					assert t.root.left.left.parent == t.root.left
					assert t.root.left.right.left.parent == t.root.left.right

				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					self._get_binary_search_tree()
					t.empty()
					assert t.root is None

				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					self._get_binary_search_tree()
					assert not t.is_empty()

					t.empty()
					assert t.is_empty()

				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					self._get_binary_search_tree()

					assert t.exists(6 )
					assert not t.exists(-1 )

				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					self._get_binary_search_tree()

					assert t.get_max_label() == 14

					t.empty()
					with self.assertRaises(lowercase_ ):  # noqa: B017
						t.get_max_label()

				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					self._get_binary_search_tree()

					assert t.get_min_label() == 1

					t.empty()
					with self.assertRaises(lowercase_ ):  # noqa: B017
						t.get_min_label()

				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					self._get_binary_search_tree()

					snake_case							=					[i.label for i in t.inorder_traversal()]
					assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]






				def 					_lowerCamelCase    (       self )    ->							None:
					snake_case							=					self._get_binary_search_tree()

					snake_case							=					[i.label for i in t.preorder_traversal()]
					assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]

def      __magic_name__							(			)      ->		None:
	snake_case							=					BinarySearchTree()
	t.put(8				)
	t.put(3				)
	t.put(6				)
	t.put(1				)
	t.put(1_0				)
	t.put(1_4				)
	t.put(1_3				)
	t.put(4				)
	t.put(7				)
	t.put(5				)

	print(
	    '\n            8\n           / \\\n          3   10\n         / \\    \\\n        1   6    14\n           / \\   /\n          4   7 13\n           \\\n            5\n        '				)

	print('Label 6 exists:'       , t.exists(6				)				)
	print('Label 13 exists:'       , t.exists(1_3				)				)
	print('Label -1 exists:'       , t.exists(-1				)				)
	print('Label 12 exists:'       , t.exists(1_2				)				)

	# Prints all the elements of the list in inorder traversal
	snake_case							=					[i.label for i in t.inorder_traversal()]
	print('Inorder traversal:'       , A				)

	# Prints all the elements of the list in preorder traversal
	snake_case							=					[i.label for i in t.preorder_traversal()]
	print('Preorder traversal:'       , A				)

	print('Max. label:'       , t.get_max_label()				)
	print('Min. label:'       , t.get_min_label()				)

	# Delete elements
	print('\nDeleting elements 13, 10, 8, 3, 6, 14'				)
	print(
	    '\n          4\n         / \\\n        1   7\n             \\\n              5\n        '				)
	t.remove(1_3				)
	t.remove(1_0				)
	t.remove(8				)
	t.remove(3				)
	t.remove(6				)
	t.remove(1_4				)

	# Prints all the elements of the list in inorder traversal after delete
	snake_case							=					[i.label for i in t.inorder_traversal()]
	print('Inorder traversal after delete:'       , A				)

	# Prints all the elements of the list in preorder traversal after delete
	snake_case							=					[i.label for i in t.preorder_traversal()]
	print('Preorder traversal after delete:'       , A				)

	print('Max. label:'       , t.get_max_label()				)
	print('Min. label:'       , t.get_min_label()				)


if __name__ == "__main__":
						binary_search_tree_example()







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

lowerCAmelCase_      =   {
    "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   lowerCAmelCase(  a__    :  List[Any]      , a__    :  List[str]      , a__    :  Optional[int]      , a__    :  Optional[Any]      , a__    :  List[Any]      ):







       '''simple docstring'''






       for attribute in key.split("."      ):
              lowerCamelCase__				   =      getattr(a__      , a__      )

       if weight_type is not None:
              lowerCamelCase__				   =      getattr(a__      , a__      ).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   lowerCAmelCase(  a__    :  Any      , a__    :  List[str]      , a__    :  str      ):







       '''simple docstring'''






       lowerCamelCase__				   =      []
       lowerCamelCase__				   =      fairseq_model.state_dict()

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

       for name, value in fairseq_dict.items():
              lowerCamelCase__				   =      False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__      , a__      , a__      , a__      , hf_model.config.feat_extract_norm == "group"      , )
                     lowerCamelCase__				   =      True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowerCamelCase__				   =      "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]:
                                   lowerCamelCase__				   =      True
                                   if "*" in mapped_key:
                                          lowerCamelCase__				   =      name.split(a__      )[0].split("."      )[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          lowerCamelCase__				   =      mapped_key.replace("*"      , a__      )
                                   if "weight_g" in name:
                                          lowerCamelCase__				   =      "weight_g"
                                   elif "weight_v" in name:
                                          lowerCamelCase__				   =      "weight_v"
                                   elif "weight" in name:
                                          lowerCamelCase__				   =      "weight"
                                   elif "bias" in name:
                                          lowerCamelCase__				   =      "bias"
                                   else:
                                          lowerCamelCase__				   =      None
                                   set_recursively(a__      , a__      , a__      , a__      , a__      )
                            continue
              if not is_used:
                     unused_weights.append(a__      )

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







def   lowerCAmelCase(  a__    :  Optional[Any]      , a__    :  Dict      , a__    :  List[Any]      , a__    :  Union[str, Any]      , a__    :  List[str]      ):







       '''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(a__      )







def   lowerCAmelCase(  a__    :  Optional[int]      , a__    :  Tuple      ):







       '''simple docstring'''






       lowerCamelCase__				   =      SEWDConfig()
       if is_finetuned:
              lowerCamelCase__				   =      model.wav_encoder.wav_model.cfg
       else:
              lowerCamelCase__				   =      model.cfg

       lowerCamelCase__				   =      fs_config.conv_bias
       lowerCamelCase__				   =      eval(fs_config.conv_feature_layers      )
       lowerCamelCase__				   =      [x[0] for x in conv_layers]
       lowerCamelCase__				   =      [x[1] for x in conv_layers]
       lowerCamelCase__				   =      [x[2] for x in conv_layers]
       lowerCamelCase__				   =      "gelu"
       lowerCamelCase__				   =      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
       lowerCamelCase__				   =      0.0
       lowerCamelCase__				   =      fs_config.activation_fn.name
       lowerCamelCase__				   =      fs_config.encoder_embed_dim
       lowerCamelCase__				   =      0.0_2
       lowerCamelCase__				   =      fs_config.encoder_ffn_embed_dim
       lowerCamelCase__				   =      1E-5
       lowerCamelCase__				   =      fs_config.encoder_layerdrop
       lowerCamelCase__				   =      fs_config.encoder_attention_heads
       lowerCamelCase__				   =      fs_config.conv_pos_groups
       lowerCamelCase__				   =      fs_config.conv_pos
       lowerCamelCase__				   =      len(a__      )
       lowerCamelCase__				   =      fs_config.encoder_layers
       lowerCamelCase__				   =      fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       lowerCamelCase__				   =      fs_config.max_position_embeddings
       lowerCamelCase__				   =      fs_config.position_buckets
       lowerCamelCase__				   =      fs_config.share_att_key
       lowerCamelCase__				   =      fs_config.relative_attention
       lowerCamelCase__				   =      fs_config.position_biased_input
       lowerCamelCase__				   =      tuple(fs_config.pos_att_type.split("|"      )      )
       lowerCamelCase__				   =      fs_config.norm_rel_ebd

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

       lowerCamelCase__				   =      "Wav2Vec2FeatureExtractor"
       lowerCamelCase__				   =      "Wav2Vec2CTCTokenizer"

       return config







@torch.no_grad()
def   lowerCAmelCase(  a__    :  List[Any]      , a__    :  int      , a__    :  Any=None      , a__    :  Any=None      , a__    :  Any=True      ):







       '''simple docstring'''






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

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

       lowerCamelCase__				   =      True if config.feat_extract_norm == "layer" else False
       lowerCamelCase__				   =      WavaVecaFeatureExtractor(
           feature_size=1      , sampling_rate=1_6000      , padding_value=0      , do_normalize=a__      , return_attention_mask=a__      , )

       if is_finetuned:
              if dict_path:
                     lowerCamelCase__				   =      Dictionary.load(a__      )

                     # 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.pad_index
                     lowerCamelCase__				   =      target_dict.bos_index
                     lowerCamelCase__				   =      target_dict.eos_index
                     lowerCamelCase__				   =      len(target_dict.symbols      )
                     lowerCamelCase__				   =      os.path.join(a__      , "vocab.json"      )
                     if not os.path.isdir(a__      ):
                            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(a__      )      )
                            return
                     os.makedirs(a__      , exist_ok=a__      )
                     with open(a__      , "w"      , encoding="utf-8"      ) as vocab_handle:
                            json.dump(target_dict.indices      , a__      )
                     lowerCamelCase__				   =      WavaVecaCTCTokenizer(
                         a__      , 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=a__      , )
                     lowerCamelCase__				   =      WavaVecaProcessor(feature_extractor=a__      , tokenizer=a__      )
                     processor.save_pretrained(a__      )

              lowerCamelCase__				   =      SEWDForCTC(a__      )
       else:
              lowerCamelCase__				   =      SEWDModel(a__      )
              feature_extractor.save_pretrained(a__      )

       recursively_load_weights(a__      , a__      , a__      )

       hf_model.save_pretrained(a__      )


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(
        "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    lowerCAmelCase_      =   parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )

