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()
UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Optional[int]:




       '''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

       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":
              _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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Optional[int]:




       '''simple docstring'''






       _A=	[]
       _A=	fairseq_model.state_dict()

       _A=	hf_model.hubert.feature_extractor if is_finetuned else hf_model.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=	'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):
                                   _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 "weight" in name:
                                          _A=	'weight'
                                   elif "bias" in name:
                                          _A=	'bias'
                                   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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Optional[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:
                     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."
                     )
                     _A=	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."
                     )
                     _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:
                     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."
                     )
                     _A=	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."
                     )
                     _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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_=None       ,					lowerCAmelCase_=None       ,					lowerCAmelCase_=True   )		->						Optional[int]:




       '''simple docstring'''






       if config_path is not None:
              _A=	HubertConfig.from_pretrained(lowerCAmelCase_   )
       else:
              _A=	HubertConfig()

       if is_finetuned:
              if dict_path:
                     _A=	Dictionary.load(lowerCAmelCase_   )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     _A=	target_dict.pad_index
                     _A=	target_dict.bos_index
                     _A=	target_dict.eos_index
                     _A=	len(target_dict.symbols   )
                     _A=	os.path.join(lowerCAmelCase_       ,					'vocab.json'   )
                     if not os.path.isdir(lowerCAmelCase_   ):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase_   )   )
                            return
                     os.makedirs(lowerCAmelCase_       ,					exist_ok=lowerCAmelCase_   )
                     with open(lowerCAmelCase_       ,					'w'       ,					encoding='utf-8'   ) as vocab_handle:
                            json.dump(target_dict.indices       ,					lowerCAmelCase_   )
                     _A=	WavaVecaCTCTokenizer(
                         lowerCAmelCase_       ,					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=lowerCAmelCase_       ,					)
                     _A=	True if config.feat_extract_norm == 'layer' else False
                     _A=	WavaVecaFeatureExtractor(
                         feature_size=1       ,					sampling_rate=1_60_00       ,					padding_value=0       ,					do_normalize=lowerCAmelCase_       ,					return_attention_mask=lowerCAmelCase_       ,					)
                     _A=	WavaVecaProcessor(feature_extractor=lowerCAmelCase_       ,					tokenizer=lowerCAmelCase_   )
                     processor.save_pretrained(lowerCAmelCase_   )

              _A=	HubertForCTC(lowerCAmelCase_   )
       else:
              _A=	HubertModel(lowerCAmelCase_   )

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

       _A=	model[0].eval()

       recursively_load_weights(lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )

       hf_wavavec.save_pretrained(lowerCAmelCase_   )


if __name__ == "__main__":
      UpperCAmelCase_								=							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'''
      )
      UpperCAmelCase_								=							parser.parse_args()
      convert_hubert_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.nn.Linear(10				,      10       )
            _A=	torch.optim.SGD(model.parameters()				,      0.1       )
            _A=	Accelerator()
            _A=	accelerator.prepare(lowerCAmelCase__       )
            try:
                   pickle.loads(pickle.dumps(lowerCAmelCase__       )       )
            except Exception as e:
                   self.fail(f"Accelerated optimizer pickling failed with {e}"       )
            AcceleratorState._reset_state()