



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


logging.set_verbosity_info()
__snake_case						:					Any	       =      logging.get_logger(__name__)

__snake_case						:					Optional[Any]	       =      {
    '''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 	a_						(      __a		,				__a		,				__a		,				__a		,				__a   ):
     for attribute in key.split('''.'''   ):
          A__      						= getattr(__a		,				__a   )

     if weight_type is not None:
          A__      						= getattr(__a		,				__a   ).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 	a_						(      __a		,				__a		,				__a   ):
     A__      						= []
     A__      						= fairseq_model.state_dict()

     A__      						= hf_model.sew.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(
                   __a		,				__a		,				__a		,				__a		,				hf_model.config.feat_extract_norm == '''group'''		,				)
               A__      						= True
          else:
               for key, mapped_key in MAPPING.items():
                    A__      						= '''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]:
                         A__      						= True
                         if "*" in mapped_key:
                              A__      						= name.split(__a   )[0].split('''.'''   )[-2]
                              A__      						= mapped_key.replace('''*'''		,				__a   )
                         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(__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   ):
     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(__a   )



def 	a_						(      __a		,				__a   ):
     A__      						= SEWConfig()
     if is_finetuned:
          A__      						= model.wav_encoder.wav_model.cfg
     else:
          A__      						= model.cfg

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

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

     A__      						= '''Wav2Vec2FeatureExtractor'''
     A__      						= '''Wav2Vec2CTCTokenizer'''

     return config



@torch.no_grad()
def 	a_						(      __a		,				__a		,				__a=None		,				__a=None		,				__a=True   ):

     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]   )

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

     A__      						= True if config.feat_extract_norm == '''layer''' else False
     A__      						= WavaVecaFeatureExtractor(
         feature_size=1		,				sampling_rate=1_6000		,				padding_value=0		,				do_normalize=__a		,				return_attention_mask=__a		,				)

     if is_finetuned:
          if dict_path:
               A__      						= Dictionary.load(__a   )

               # 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.pad_index
               A__      						= target_dict.bos_index
               A__      						= target_dict.eos_index
               A__      						= len(target_dict.symbols   )
               A__      						= 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   )
               A__      						= 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		,				)
               A__      						= WavaVecaProcessor(feature_extractor=__a		,				tokenizer=__a   )
               processor.save_pretrained(__a   )

          A__      						= SEWForCTC(__a   )
     else:
          A__      						= SEWModel(__a   )
          feature_extractor.save_pretrained(__a   )

     recursively_load_weights(__a		,				__a		,				__a   )

     hf_model.save_pretrained(__a   )


if __name__ == "__main__":
 __snake_case						:					List[Any]	       =      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						:					int	       =      parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )










SCREAMING_SNAKE_CASE_   :      dict[str, float]			=   {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 100_0000,
    "gigajoule": 10_0000_0000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 360_0000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 418_6800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1055.0_5585,
    "footpound": 1.35_5818,
}



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    float:
		if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
				__lowercase											=     (
				    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
				    F"Valid values are: {', '.join(snake_case							)}"
				)
				raise ValueError(snake_case							)
		return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




