
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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_snake_case      = {
    '''post_extract_proj''': '''feature_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.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					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      Optional[Any]:
     for attribute in key.split('.'			):
          UpperCamelCase						    =					getattr(_lowercase						,     _lowercase			)

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

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

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase			)       ->      List[str]:
     UpperCamelCase						    =					[]
     UpperCamelCase						    =					fairseq_model.state_dict()

     UpperCamelCase						    =					hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

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

     logger.warning(F'Unused weights: {unused_weights}'			)

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      List[Any]:
     UpperCamelCase						    =					full_name.split('conv_layers.'			)[-1]
     UpperCamelCase						    =					name.split('.'			)
     UpperCamelCase						    =					int(items[0]			)
     UpperCamelCase						    =					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						    =					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						    =					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						    =					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						    =					value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
     else:
          unused_weights.append(_lowercase			)

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Union[str, Any]:
     UpperCamelCase						    =					SEWConfig()
     if is_finetuned:
          UpperCamelCase						    =					model.wav_encoder.wav_model.cfg
     else:
          UpperCamelCase						    =					model.cfg

     UpperCamelCase						    =					fs_config.conv_bias
     UpperCamelCase						    =					eval(fs_config.conv_feature_layers			)
     UpperCamelCase						    =					[x[0] for x in conv_layers]
     UpperCamelCase						    =					[x[1] for x in conv_layers]
     UpperCamelCase						    =					[x[2] for x in conv_layers]
     UpperCamelCase						    =					"""gelu"""
     UpperCamelCase						    =					"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
     UpperCamelCase						    =					0.0
     UpperCamelCase						    =					fs_config.activation_fn.name
     UpperCamelCase						    =					fs_config.encoder_embed_dim
     UpperCamelCase						    =					0.02
     UpperCamelCase						    =					fs_config.encoder_ffn_embed_dim
     UpperCamelCase						    =					1e-5
     UpperCamelCase						    =					fs_config.encoder_layerdrop
     UpperCamelCase						    =					fs_config.encoder_attention_heads
     UpperCamelCase						    =					fs_config.conv_pos_groups
     UpperCamelCase						    =					fs_config.conv_pos
     UpperCamelCase						    =					len(_lowercase			)
     UpperCamelCase						    =					fs_config.encoder_layers
     UpperCamelCase						    =					fs_config.squeeze_factor

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

     UpperCamelCase						    =					"""Wav2Vec2FeatureExtractor"""
     UpperCamelCase						    =					"""Wav2Vec2CTCTokenizer"""

     return config

@torch.no_grad()
def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase=None						,     _lowercase=None						,     _lowercase=True			)       ->      Optional[Any]:

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

     if config_path is not None:
          UpperCamelCase						    =					SEWConfig.from_pretrained(_lowercase			)
     else:
          UpperCamelCase						    =					convert_config(model[0]						,     _lowercase			)
     UpperCamelCase						    =					model[0].eval()

     UpperCamelCase						    =					True if config.feat_extract_norm == """layer""" else False
     UpperCamelCase						    =					WavaVecaFeatureExtractor(
         feature_size=1						,     sampling_rate=16000						,     padding_value=0						,     do_normalize=_lowercase						,     return_attention_mask=_lowercase						,     )

     if is_finetuned:
          if dict_path:
               UpperCamelCase						    =					Dictionary.load(_lowercase			)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               UpperCamelCase						    =					target_dict.pad_index
               UpperCamelCase						    =					target_dict.bos_index
               UpperCamelCase						    =					target_dict.pad_index
               UpperCamelCase						    =					target_dict.bos_index
               UpperCamelCase						    =					target_dict.eos_index
               UpperCamelCase						    =					len(target_dict.symbols			)
               UpperCamelCase						    =					os.path.join(_lowercase						,     'vocab.json'			)
               if not os.path.isdir(_lowercase			):
                    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_lowercase			)			)
                    return
               os.makedirs(_lowercase						,     exist_ok=_lowercase			)
               with open(_lowercase						,     'w'						,     encoding='utf-8'			) as vocab_handle:
                    json.dump(target_dict.indices						,     _lowercase			)
               UpperCamelCase						    =					WavaVecaCTCTokenizer(
                   _lowercase						,     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=_lowercase						,     )
               UpperCamelCase						    =					WavaVecaProcessor(feature_extractor=_lowercase						,     tokenizer=_lowercase			)
               processor.save_pretrained(_lowercase			)

          UpperCamelCase						    =					SEWForCTC(_lowercase			)
     else:
          UpperCamelCase						    =					SEWModel(_lowercase			)
          feature_extractor.save_pretrained(_lowercase			)

     recursively_load_weights(_lowercase						,     _lowercase						,     _lowercase			)

     hf_model.save_pretrained(_lowercase			)


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





"""simple docstring"""
def 					a__						(						)	-> Union[str, Any]:
      UpperCAmelCase__			:							Dict							=    []
      UpperCAmelCase__			:							Tuple							=    1

      while len(lowerCAmelCase    ) < 1E6:
            constant.append(str(lowerCAmelCase    )    )
            i += 1

      UpperCAmelCase__			:							Any							=    """""".join(lowerCAmelCase    )

      return (
          int(constant[0]    )
          * int(constant[9]    )
          * int(constant[99]    )
          * int(constant[9_99]    )
          * int(constant[99_99]    )
          * int(constant[9_99_99]    )
          * int(constant[99_99_99]    )
      )


if __name__ == "__main__":
    print(solution())

