"""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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCamelCase_							:							List[Any]				 =		{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''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__   (		UpperCAmelCase__							:      List[Any]		,      UpperCAmelCase__							:      Any		,      UpperCAmelCase__							:      Tuple		,      UpperCAmelCase__							:      Optional[Any]		,      UpperCAmelCase__							:      Tuple      )->						Optional[Any]:
						for attribute in key.split("""."""      ):
												UpperCAmelCase__       						=		getattr(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE      )

						if weight_type is not None:
												UpperCAmelCase__       						=		getattr(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE      ).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 	a__   (		UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      Optional[Any]		,      UpperCAmelCase__							:      Any      )->						Any:
						UpperCAmelCase__       						=		[]
						UpperCAmelCase__       						=		fairseq_model.state_dict()

						UpperCAmelCase__       						=		hf_model.sew_d.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(
																		    _SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      hf_model.config.feat_extract_norm == """group"""		,      )
																		UpperCAmelCase__       						=		True
												else:
																		for key, mapped_key in MAPPING.items():
																								UpperCAmelCase__       						=		"""sew_d.""" + 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(_SCREAMING_SNAKE_CASE      )[0].split("""."""      )[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				UpperCAmelCase__       						=		mapped_key.replace("""*"""		,      _SCREAMING_SNAKE_CASE      )
																														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(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE      )
																								continue
												if not is_used:
																		unused_weights.append(_SCREAMING_SNAKE_CASE      )

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


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      Union[str, Any]		,      UpperCAmelCase__							:      Tuple		,      UpperCAmelCase__							:      List[str]      )->						Optional[int]:
						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(_SCREAMING_SNAKE_CASE      )


def 	a__   (		UpperCAmelCase__							:      int		,      UpperCAmelCase__							:      Dict      )->						Optional[Any]:
						UpperCAmelCase__       						=		SEWDConfig()
						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(_SCREAMING_SNAKE_CASE      )
						UpperCAmelCase__       						=		fs_config.encoder_layers
						UpperCAmelCase__       						=		fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						UpperCAmelCase__       						=		fs_config.max_position_embeddings
						UpperCAmelCase__       						=		fs_config.position_buckets
						UpperCAmelCase__       						=		fs_config.share_att_key
						UpperCAmelCase__       						=		fs_config.relative_attention
						UpperCAmelCase__       						=		fs_config.position_biased_input
						UpperCAmelCase__       						=		tuple(fs_config.pos_att_type.split("""|"""      )      )
						UpperCAmelCase__       						=		fs_config.norm_rel_ebd

						# 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 	a__   (		UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      List[str]=None		,      UpperCAmelCase__							:      str=None		,      UpperCAmelCase__							:      Any=True      )->						Any:

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

						if config_path is not None:
												UpperCAmelCase__       						=		SEWDConfig.from_pretrained(_SCREAMING_SNAKE_CASE      )
						else:
												UpperCAmelCase__       						=		convert_config(model[0]		,      _SCREAMING_SNAKE_CASE      )
						UpperCAmelCase__       						=		model[0].eval()

						UpperCAmelCase__       						=		True if config.feat_extract_norm == """layer""" else False
						UpperCAmelCase__       						=		WavaVecaFeatureExtractor(
						    feature_size=1		,      sampling_rate=1_6_0_0_0		,      padding_value=0		,      do_normalize=_SCREAMING_SNAKE_CASE		,      return_attention_mask=_SCREAMING_SNAKE_CASE		,      )

						if is_finetuned:
												if dict_path:
																		UpperCAmelCase__       						=		Dictionary.load(_SCREAMING_SNAKE_CASE      )

																		# 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(_SCREAMING_SNAKE_CASE		,      """vocab.json"""      )
																		if not os.path.isdir(_SCREAMING_SNAKE_CASE      ):
																								logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_SCREAMING_SNAKE_CASE      )      )
																								return
																		os.makedirs(_SCREAMING_SNAKE_CASE		,      exist_ok=_SCREAMING_SNAKE_CASE      )
																		with open(_SCREAMING_SNAKE_CASE		,      """w"""		,      encoding="""utf-8"""      ) as vocab_handle:
																								json.dump(target_dict.indices		,      _SCREAMING_SNAKE_CASE      )
																		UpperCAmelCase__       						=		WavaVecaCTCTokenizer(
																		    _SCREAMING_SNAKE_CASE		,      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=_SCREAMING_SNAKE_CASE		,      )
																		UpperCAmelCase__       						=		WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE		,      tokenizer=_SCREAMING_SNAKE_CASE      )
																		processor.save_pretrained(_SCREAMING_SNAKE_CASE      )

												UpperCAmelCase__       						=		SEWDForCTC(_SCREAMING_SNAKE_CASE      )
						else:
												UpperCAmelCase__       						=		SEWDModel(_SCREAMING_SNAKE_CASE      )
												feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE      )

						recursively_load_weights(_SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE		,      _SCREAMING_SNAKE_CASE      )

						hf_model.save_pretrained(_SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
						UpperCamelCase_							:							Tuple				 =		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"""
						)
						UpperCamelCase_							:							Optional[Any]				 =		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"""



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


a  :						Dict					       =							logging.getLogger(__name__)




def       snake_case__		(   )    ->Any:
 UpperCAmelCase__ 						=				ArgumentParser()
 UpperCAmelCase__						,	UpperCAmelCase__ 						=				parser.parse_known_args()
 for arg in unknown:
  if arg.startswith(("""-""", """--""")			):
   parser.add_argument(arg.split("""="""			)[0]			)

 return parser.parse_args()




def       snake_case__		(   )    ->List[Any]:
 UpperCAmelCase__ 						=				parse_args()
 UpperCAmelCase__ 						=				8_8_8_8
 UpperCAmelCase__ 						=				int(os.environ["""SM_NUM_GPUS"""]			)
 UpperCAmelCase__ 						=				json.loads(os.environ["""SM_HOSTS"""]			)
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				os.environ["""SM_CURRENT_HOST"""]
 UpperCAmelCase__ 						=				hosts.index(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				"""INFO"""

 if num_nodes > 1:
  UpperCAmelCase__ 						=				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:
  UpperCAmelCase__ 						=				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(_SCREAMING_SNAKE_CASE       ,	shell=_SCREAMING_SNAKE_CASE			)
 except Exception as e:
  logger.info(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
 main()



