





import json
import logging
import os
import subprocess
from argparse import ArgumentParser


_SCREAMING_SNAKE_CASE   						=		logging.getLogger(__name__)


def 				SCREAMING_SNAKE_CASE__    (     ):
	snake_case_		:      Optional[Any]           =						ArgumentParser()
	snake_case_    ,snake_case_		:      List[str]           =						parser.parse_known_args()
	for arg in unknown:
		if arg.startswith(('-', '--') ):
			parser.add_argument(arg.split('=' )[0] )

	return parser.parse_args()


def 				SCREAMING_SNAKE_CASE__    (     ):
	snake_case_		:      Dict           =						parse_args()
	snake_case_		:      Optional[int]           =						88_88
	snake_case_		:      str           =						int(os.environ['SM_NUM_GPUS'] )
	snake_case_		:      str           =						json.loads(os.environ['SM_HOSTS'] )
	snake_case_		:      Tuple           =						len(__a )
	snake_case_		:      Union[str, Any]           =						os.environ['SM_CURRENT_HOST']
	snake_case_		:      Dict           =						hosts.index(__a )
	snake_case_		:      int           =						'INFO'

	if num_nodes > 1:
		snake_case_		:      Dict           =						f"""python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()] )}"""
	else:
		snake_case_		:      Tuple           =						f"""python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()] )}"""
	try:
		subprocess.run(__a ,			shell=__a )
	except Exception as e:
		logger.info(__a )


if __name__ == "__main__":
						main()









import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

a 			=							{
    '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',
    'mask_emb': 'masked_spec_embed',
}
def    a__    (	A		,					A		,					A		,					A		,					A       ):
   for attribute in key.split("""."""       ):
      lowercase__    :						Dict       =			getattr(A		,					A       )

   if weight_type is not None:
      lowercase__    :						List[Any]       =			getattr(A		,					A       ).shape
   else:
      lowercase__    :						Tuple       =			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":
      lowercase__    :						Dict       =			value
   elif weight_type == "weight_g":
      lowercase__    :						Any       =			value
   elif weight_type == "weight_v":
      lowercase__    :						Dict       =			value
   elif weight_type == "bias":
      lowercase__    :						Any       =			value
   else:
      lowercase__    :						Tuple       =			value

   logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )
def    a__    (	A		,					A       ):
   lowercase__    :						str       =			[]
   lowercase__    :						int       =			fairseq_model.state_dict()

   lowercase__    :						Tuple       =			hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      lowercase__    :						Optional[int]       =			False
      if "conv_layers" in name:
         load_conv_layer(
             A		,					A		,					A		,					A		,					hf_model.config.feat_extract_norm == """group"""		,					)
         lowercase__    :						Optional[int]       =			True
      else:
         for key, mapped_key in MAPPING.items():
            lowercase__    :						Dict       =			mapped_key

            if key in name:
               lowercase__    :						int       =			True
               if "*" in mapped_key:
                  lowercase__    :						Any       =			name.split(A       )[0].split("""."""       )[-2]
                  lowercase__    :						int       =			mapped_key.replace("""*"""		,					A       )
               if "weight_g" in name:
                  lowercase__    :						Dict       =			"""weight_g"""
               elif "weight_v" in name:
                  lowercase__    :						Dict       =			"""weight_v"""
               elif "weight" in name:
                  lowercase__    :						Union[str, Any]       =			"""weight"""
               elif "bias" in name:
                  lowercase__    :						Optional[int]       =			"""bias"""
               else:
                  lowercase__    :						List[str]       =			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       ):
   lowercase__    :						Optional[int]       =			full_name.split("""conv_layers."""       )[-1]
   lowercase__    :						int       =			name.split("""."""       )
   lowercase__    :						Optional[Any]       =			int(items[0]       )
   lowercase__    :						Optional[int]       =			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.'''
         )
         lowercase__    :						int       =			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.'''
         )
         lowercase__    :						str       =			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."
         )
         lowercase__    :						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.'''
         )
         lowercase__    :						int       =			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       ):
   lowercase__    :						Optional[Any]       =			HubertConfig()
   lowercase__    :						List[str]       =			model.config

   lowercase__    :						int       =			fs_config.activation_dropout
   lowercase__    :						Tuple       =			False
   lowercase__    :						Optional[Any]       =			fs_config.attention_dropout
   lowercase__    :						str       =			False
   lowercase__    :						List[Any]       =			eval(fs_config.extractor_conv_feature_layers       )
   lowercase__    :						Union[str, Any]       =			[x[0] for x in conv_layers]
   lowercase__    :						str       =			[x[1] for x in conv_layers]
   lowercase__    :						str       =			[x[2] for x in conv_layers]
   lowercase__    :						Any       =			"""gelu"""
   lowercase__    :						Optional[int]       =			"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   lowercase__    :						Optional[int]       =			False
   lowercase__    :						Tuple       =			0.0
   lowercase__    :						Optional[Any]       =			0.0
   lowercase__    :						Optional[Any]       =			fs_config.activation_fn
   lowercase__    :						Optional[Any]       =			fs_config.dropout
   lowercase__    :						Any       =			fs_config.encoder_embed_dim
   lowercase__    :						List[Any]       =			0.0_2
   lowercase__    :						List[Any]       =			fs_config.encoder_ffn_embed_dim
   lowercase__    :						List[Any]       =			1e-5
   lowercase__    :						Dict       =			0.0
   lowercase__    :						List[Any]       =			fs_config.encoder_attention_heads
   lowercase__    :						List[str]       =			fs_config.conv_pos_groups
   lowercase__    :						List[Any]       =			fs_config.conv_pos
   lowercase__    :						Union[str, Any]       =			len(A       )
   lowercase__    :						List[str]       =			fs_config.encoder_layers

   return config
@torch.no_grad()
def    a__    (	A		,					A=None       ):
   lowercase__    :						List[str]       =			distilhubert().model.model

   if config_path is not None:
      lowercase__    :						List[str]       =			HubertConfig.from_pretrained(A       )
   else:
      lowercase__    :						List[str]       =			convert_config(A       )
   lowercase__    :						List[Any]       =			model.eval()

   lowercase__    :						Optional[Any]       =			WavaVecaFeatureExtractor(
       feature_size=1		,					sampling_rate=16_000		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
   lowercase__    :						Union[str, Any]       =			HubertModel(A       )

   recursively_load_weights(A		,					A       )

   feature_extractor.save_pretrained(A       )
   hf_model.save_pretrained(A       )


if __name__ == "__main__":
 a 			=							argparse.ArgumentParser()
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 a 			=							parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


