




'''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__:       Optional[Any]							     =      logging.get_logger(__name__)

UpperCamelCase__:       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 			snake_case_					( _lowerCAmelCase  :						Tuple       ,				_lowerCAmelCase  :						Tuple       ,				_lowerCAmelCase  :						Optional[Any]       ,				_lowerCAmelCase  :						int       ,				_lowerCAmelCase  :						Dict  )				->       Tuple:
							for attribute in key.split('''.'''  ):
														UpperCAmelCase			:	Dict										=		getattr(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_  )

							if weight_type is not None:
														UpperCAmelCase			:	Optional[int]										=		getattr(SCREAMING_SNAKE_CASE_       ,				SCREAMING_SNAKE_CASE_  ).shape
							else:
														UpperCAmelCase			:	List[Any]										=		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			:	Any										=		value
							elif weight_type == "weight_g":
														UpperCAmelCase			:	Tuple										=		value
							elif weight_type == "weight_v":
														UpperCAmelCase			:	List[Any]										=		value
							elif weight_type == "bias":
														UpperCAmelCase			:	str										=		value
							else:
														UpperCAmelCase			:	Tuple										=		value

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


def 			snake_case_					( _lowerCAmelCase  :						Optional[int]       ,				_lowerCAmelCase  :						List[str]       ,				_lowerCAmelCase  :						str  )				->       Any:
							UpperCAmelCase			:	Any										=		[]
							UpperCAmelCase			:	str										=		fairseq_model.state_dict()

							UpperCAmelCase			:	Optional[int]										=		hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														UpperCAmelCase			:	Any										=		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			:	List[Any]										=		True
														else:
																					for key, mapped_key in MAPPING.items():
																												UpperCAmelCase			:	Any										=		"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			:	Any										=		True
																																			if "*" in mapped_key:
																																										UpperCAmelCase			:	str										=		name.split(SCREAMING_SNAKE_CASE_  )[0].split('''.'''  )[-2]
																																										if not layer_index.isnumeric():
																																																	continue
																																										UpperCAmelCase			:	Tuple										=		mapped_key.replace('''*'''       ,				SCREAMING_SNAKE_CASE_  )
																																			if "weight_g" in name:
																																										UpperCAmelCase			:	List[Any]										=		"weight_g"
																																			elif "weight_v" in name:
																																										UpperCAmelCase			:	Tuple										=		"weight_v"
																																			elif "weight" in name:
																																										UpperCAmelCase			:	List[str]										=		"weight"
																																			elif "bias" in name:
																																										UpperCAmelCase			:	str										=		"bias"
																																			else:
																																										UpperCAmelCase			:	str										=		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 			snake_case_					( _lowerCAmelCase  :						Any       ,				_lowerCAmelCase  :						Dict       ,				_lowerCAmelCase  :						List[str]       ,				_lowerCAmelCase  :						Optional[Any]       ,				_lowerCAmelCase  :						Optional[int]  )				->       Optional[int]:
							UpperCAmelCase			:	Tuple										=		full_name.split('''conv_layers.'''  )[-1]
							UpperCAmelCase			:	str										=		name.split('''.'''  )
							UpperCAmelCase			:	Optional[Any]										=		int(items[0]  )
							UpperCAmelCase			:	List[Any]										=		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			:	str										=		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			:	Union[str, Any]										=		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			:	Optional[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."""
																					)
																					UpperCAmelCase			:	Any										=		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 			snake_case_					( _lowerCAmelCase  :						str       ,				_lowerCAmelCase  :						str  )				->       List[Any]:
							UpperCAmelCase			:	Tuple										=		SEWDConfig()
							if is_finetuned:
														UpperCAmelCase			:	Optional[Any]										=		model.wav_encoder.wav_model.cfg
							else:
														UpperCAmelCase			:	Optional[int]										=		model.cfg

							UpperCAmelCase			:	Tuple										=		fs_config.conv_bias
							UpperCAmelCase			:	Any										=		eval(fs_config.conv_feature_layers  )
							UpperCAmelCase			:	Tuple										=		[x[0] for x in conv_layers]
							UpperCAmelCase			:	int										=		[x[1] for x in conv_layers]
							UpperCAmelCase			:	int										=		[x[2] for x in conv_layers]
							UpperCAmelCase			:	int										=		"gelu"
							UpperCAmelCase			:	Union[str, Any]										=		"layer" if fs_config.extractor_mode == "layer_norm" else "group"
							UpperCAmelCase			:	Dict										=		0.0
							UpperCAmelCase			:	int										=		fs_config.activation_fn.name
							UpperCAmelCase			:	List[str]										=		fs_config.encoder_embed_dim
							UpperCAmelCase			:	Dict										=		0.0_2
							UpperCAmelCase			:	Dict										=		fs_config.encoder_ffn_embed_dim
							UpperCAmelCase			:	Tuple										=		1e-5
							UpperCAmelCase			:	str										=		fs_config.encoder_layerdrop
							UpperCAmelCase			:	Union[str, Any]										=		fs_config.encoder_attention_heads
							UpperCAmelCase			:	Optional[Any]										=		fs_config.conv_pos_groups
							UpperCAmelCase			:	Optional[Any]										=		fs_config.conv_pos
							UpperCAmelCase			:	Dict										=		len(SCREAMING_SNAKE_CASE_  )
							UpperCAmelCase			:	Any										=		fs_config.encoder_layers
							UpperCAmelCase			:	List[str]										=		fs_config.squeeze_factor
							# DeBERTa-specific parameters:
							UpperCAmelCase			:	int										=		fs_config.max_position_embeddings
							UpperCAmelCase			:	int										=		fs_config.position_buckets
							UpperCAmelCase			:	Optional[int]										=		fs_config.share_att_key
							UpperCAmelCase			:	List[str]										=		fs_config.relative_attention
							UpperCAmelCase			:	Optional[int]										=		fs_config.position_biased_input
							UpperCAmelCase			:	Dict										=		tuple(fs_config.pos_att_type.split('''|'''  )  )
							UpperCAmelCase			:	Tuple										=		fs_config.norm_rel_ebd

							# take care of any params that are overridden by the Wav2VecCtc model
							if is_finetuned:
														UpperCAmelCase			:	int										=		model.cfg
														UpperCAmelCase			:	str										=		fs_config.final_dropout
														UpperCAmelCase			:	int										=		fs_config.layerdrop
							UpperCAmelCase			:	int										=		fs_config.activation_dropout
							UpperCAmelCase			:	int										=		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
							UpperCAmelCase			:	str										=		fs_config.attention_dropout
							UpperCAmelCase			:	Optional[int]										=		fs_config.dropout_input
							UpperCAmelCase			:	List[str]										=		fs_config.dropout
							UpperCAmelCase			:	int										=		fs_config.mask_channel_length
							UpperCAmelCase			:	Tuple										=		fs_config.mask_channel_prob
							UpperCAmelCase			:	int										=		fs_config.mask_length
							UpperCAmelCase			:	List[str]										=		fs_config.mask_prob

							UpperCAmelCase			:	Optional[Any]										=		"Wav2Vec2FeatureExtractor"
							UpperCAmelCase			:	Dict										=		"Wav2Vec2CTCTokenizer"

							return config


@torch.no_grad()
def 			snake_case_					( _lowerCAmelCase  :						Dict       ,				_lowerCAmelCase  :						List[str]       ,				_lowerCAmelCase  :						Optional[int]=None       ,				_lowerCAmelCase  :						str=None       ,				_lowerCAmelCase  :						int=True  )				->       Dict:

							if is_finetuned:
														UpperCAmelCase			:	Union[str, Any]										=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]       ,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''  )[:-1]  )}  )
							else:
														UpperCAmelCase			:	Optional[int]										=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  )

							if config_path is not None:
														UpperCAmelCase			:	Tuple										=		SEWDConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
							else:
														UpperCAmelCase			:	str										=		convert_config(model[0]       ,				SCREAMING_SNAKE_CASE_  )
							UpperCAmelCase			:	List[str]										=		model[0].eval()

							UpperCAmelCase			:	str										=		True if config.feat_extract_norm == "layer" else False
							UpperCAmelCase			:	List[str]										=		WavaVecaFeatureExtractor(
							    feature_size=1       ,				sampling_rate=16000       ,				padding_value=0       ,				do_normalize=SCREAMING_SNAKE_CASE_       ,				return_attention_mask=SCREAMING_SNAKE_CASE_       ,				)

							if is_finetuned:
														if dict_path:
																					UpperCAmelCase			:	str										=		Dictionary.load(SCREAMING_SNAKE_CASE_  )

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					UpperCAmelCase			:	str										=		target_dict.pad_index
																					UpperCAmelCase			:	Dict										=		target_dict.bos_index
																					UpperCAmelCase			:	Any										=		target_dict.pad_index
																					UpperCAmelCase			:	List[Any]										=		target_dict.bos_index
																					UpperCAmelCase			:	Optional[Any]										=		target_dict.eos_index
																					UpperCAmelCase			:	Tuple										=		len(target_dict.symbols  )
																					UpperCAmelCase			:	Optional[Any]										=		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			:	Dict										=		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			:	Tuple										=		WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_       ,				tokenizer=SCREAMING_SNAKE_CASE_  )
																					processor.save_pretrained(SCREAMING_SNAKE_CASE_  )

														UpperCAmelCase			:	List[Any]										=		SEWDForCTC(SCREAMING_SNAKE_CASE_  )
							else:
														UpperCAmelCase			:	Union[str, Any]										=		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__:       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"
						)
						UpperCamelCase__:       str							     =      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 re


def 			snake_case_					( _lowerCAmelCase  :						List[str]  )				->       Dict:
							def docstring_decorator(_lowerCAmelCase  :						List[str]  ):
														UpperCAmelCase			:	str										=		fn.__doc__
														UpperCAmelCase			:	List[str]										=		func_doc.split('''\n'''  )
														UpperCAmelCase			:	Optional[Any]										=		0
														while i < len(_lowerCAmelCase  ) and re.search(R'''^\s*Examples?:\s*$'''       ,				lines[i]  ) is None:
																					i += 1
														if i < len(_lowerCAmelCase  ):
																					UpperCAmelCase			:	Union[str, Any]										=		example_docstring
																					UpperCAmelCase			:	Tuple										=		'''\n'''.join(_lowerCAmelCase  )
														else:
																					raise ValueError(
																					    f"""The function {fn} should have an empty 'Examples:' in its docstring as placeholder, """
																					    f"""current docstring is:\n{func_doc}"""  )
														UpperCAmelCase			:	Any										=		func_doc
														return fn

							return docstring_decorator





