


'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}


def      a (     __a					,   __a					,   __a					,   __a					,   __a  )    ->      List[str]:



					'''simple docstring'''






					for attribute in key.split('''.'''  ):
										UpperCamelCase__  :Union[str, Any]				   =					getattr(__a					,   __a  )

					if weight_type is not None:
										UpperCamelCase__  :Optional[int]				   =					getattr(__a					,   __a  ).shape
					else:
										UpperCamelCase__  :Dict				   =					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":
										UpperCamelCase__  :Dict				   =					value
					elif weight_type == "weight_g":
										UpperCamelCase__  :int				   =					value
					elif weight_type == "weight_v":
										UpperCamelCase__  :Tuple				   =					value
					elif weight_type == "bias":
										UpperCamelCase__  :str				   =					value
					else:
										UpperCamelCase__  :Tuple				   =					value

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


def      a (     __a					,   __a					,   __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :Optional[int]				   =					[]
					UpperCamelCase__  :Tuple				   =					fairseq_model.state_dict()

					UpperCamelCase__  :List[str]				   =					hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										UpperCamelCase__  :Optional[int]				   =					False
										if "conv_layers" in name:
															load_conv_layer(
															    __a					,   __a					,   __a					,   __a					,   hf_model.config.feat_extract_norm == '''group'''					,   )
															UpperCamelCase__  :List[Any]				   =					True
										else:
															for key, mapped_key in MAPPING.items():
																				UpperCamelCase__  :Optional[Any]				   =					'''hubert.''' + 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] and not is_finetuned):
																									UpperCamelCase__  :Any				   =					True
																									if "*" in mapped_key:
																														UpperCamelCase__  :Union[str, Any]				   =					name.split(__a  )[0].split('''.'''  )[-2]
																														UpperCamelCase__  :int				   =					mapped_key.replace('''*'''					,   __a  )
																									if "weight_g" in name:
																														UpperCamelCase__  :int				   =					'''weight_g'''
																									elif "weight_v" in name:
																														UpperCamelCase__  :Optional[int]				   =					'''weight_v'''
																									elif "weight" in name:
																														UpperCamelCase__  :Dict				   =					'''weight'''
																									elif "bias" in name:
																														UpperCamelCase__  :Optional[int]				   =					'''bias'''
																									else:
																														UpperCamelCase__  :int				   =					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      a (     __a					,   __a					,   __a					,   __a					,   __a  )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :Union[str, Any]				   =					full_name.split('''conv_layers.'''  )[-1]
					UpperCamelCase__  :Tuple				   =					name.split('''.'''  )
					UpperCamelCase__  :Tuple				   =					int(items[0]  )
					UpperCamelCase__  :List[Any]				   =					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.'''
															)
															UpperCamelCase__  :Any				   =					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.'''
															)
															UpperCamelCase__  :int				   =					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."
															)
															UpperCamelCase__  :Union[str, Any]				   =					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.'''
															)
															UpperCamelCase__  :List[str]				   =					value
															logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
					else:
										unused_weights.append(__a  )


@torch.no_grad()
def      a (     __a					,   __a					,   __a=None					,   __a=None					,   __a=True  )    ->      Tuple:



					'''simple docstring'''






					if config_path is not None:
										UpperCamelCase__  :str				   =					HubertConfig.from_pretrained(__a  )
					else:
										UpperCamelCase__  :int				   =					HubertConfig()

					if is_finetuned:
										if dict_path:
															UpperCamelCase__  :Any				   =					Dictionary.load(__a  )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															UpperCamelCase__  :str				   =					target_dict.pad_index
															UpperCamelCase__  :List[Any]				   =					target_dict.bos_index
															UpperCamelCase__  :int				   =					target_dict.eos_index
															UpperCamelCase__  :Union[str, Any]				   =					len(target_dict.symbols  )
															UpperCamelCase__  :int				   =					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  )
															UpperCamelCase__  :List[str]				   =					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					,   )
															UpperCamelCase__  :Tuple				   =					True if config.feat_extract_norm == '''layer''' else False
															UpperCamelCase__  :Optional[Any]				   =					WavaVecaFeatureExtractor(
															    feature_size=1					,   sampling_rate=16000					,   padding_value=0					,   do_normalize=__a					,   return_attention_mask=__a					,   )
															UpperCamelCase__  :List[Any]				   =					WavaVecaProcessor(feature_extractor=__a					,   tokenizer=__a  )
															processor.save_pretrained(__a  )

										UpperCamelCase__  :int				   =					HubertForCTC(__a  )
					else:
										UpperCamelCase__  :Dict				   =					HubertModel(__a  )

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

					UpperCamelCase__  :Optional[int]				   =					model[0].eval()

					recursively_load_weights(__a					,   __a					,   __a  )

					hf_wavavec.save_pretrained(__a  )


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_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)



'''simple docstring'''
from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class   lowercase      :

		"""simple docstring"""

		_a       					=							42
		_a       					=							None
		_a       					=							None



__snake_case				 =	namedtuple('''CoinsDistribResult''', '''moves excess''')


def      a (     __a  )    ->      int:



					'''simple docstring'''






					if root is None:
										return 0

					# Validation
					def count_nodes(__a  ) -> int:
										if node is None:
															return 0

										return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

					def count_coins(__a  ) -> int:
										if node is None:
															return 0

										return count_coins(node.left  ) + count_coins(node.right  ) + node.data

					if count_nodes(__a  ) != count_coins(__a  ):
										raise ValueError('''The nodes number should be same as the number of coins'''  )

					# Main calculation
					def get_distrib(__a  ) -> CoinsDistribResult:

										if node is None:
															return CoinsDistribResult(0					,   1  )

										UpperCamelCase__   ,							UpperCamelCase__  :Optional[Any]				   =					get_distrib(node.left  )
										UpperCamelCase__   ,							UpperCamelCase__  :Union[str, Any]				   =					get_distrib(node.right  )

										UpperCamelCase__  :Tuple				   =					1 - left_distrib_excess
										UpperCamelCase__  :Optional[int]				   =					1 - right_distrib_excess

										UpperCamelCase__  :int				   =					(
										    left_distrib_moves
										    + right_distrib_moves
										    + abs(__a  )
										    + abs(__a  )
										)
										UpperCamelCase__  :Optional[Any]				   =					node.data - coins_to_left - coins_to_right

										return CoinsDistribResult(__a					,   __a  )

					return get_distrib(__a  )[0]


if __name__ == "__main__":
		import doctest

		doctest.testmod()