




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

__a  :		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_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Tuple:
							for attribute in key.split("."    ):
														lowercase__  :	Dict        =							getattr(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

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

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""    )
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Any:
							lowercase__  :	Any        =							[]
							lowercase__  :	str        =							fairseq_model.state_dict()

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

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

							lowercase__  :	Tuple        =							fs_config.conv_bias
							lowercase__  :	Any        =							eval(fs_config.conv_feature_layers    )
							lowercase__  :	Tuple        =							[x[0] for x in conv_layers]
							lowercase__  :	int        =							[x[1] for x in conv_layers]
							lowercase__  :	int        =							[x[2] for x in conv_layers]
							lowercase__  :	int        =							"gelu"
							lowercase__  :	Union[str, Any]        =							"layer" if fs_config.extractor_mode == "layer_norm" else "group"
							lowercase__  :	Dict        =							0.0
							lowercase__  :	int        =							fs_config.activation_fn.name
							lowercase__  :	List[str]        =							fs_config.encoder_embed_dim
							lowercase__  :	Dict        =							0.02
							lowercase__  :	Dict        =							fs_config.encoder_ffn_embed_dim
							lowercase__  :	Tuple        =							1E-5
							lowercase__  :	str        =							fs_config.encoder_layerdrop
							lowercase__  :	Union[str, Any]        =							fs_config.encoder_attention_heads
							lowercase__  :	Optional[Any]        =							fs_config.conv_pos_groups
							lowercase__  :	Optional[Any]        =							fs_config.conv_pos
							lowercase__  :	Dict        =							len(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Any        =							fs_config.encoder_layers
							lowercase__  :	List[str]        =							fs_config.squeeze_factor
							# DeBERTa-specific parameters:
							lowercase__  :	int        =							fs_config.max_position_embeddings
							lowercase__  :	int        =							fs_config.position_buckets
							lowercase__  :	Optional[int]        =							fs_config.share_att_key
							lowercase__  :	List[str]        =							fs_config.relative_attention
							lowercase__  :	Optional[int]        =							fs_config.position_biased_input
							lowercase__  :	Dict        =							tuple(fs_config.pos_att_type.split("|"    )    )
							lowercase__  :	Tuple        =							fs_config.norm_rel_ebd

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

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

							return config
@torch.no_grad()
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=None    ,SCREAMING_SNAKE_CASE_=None    ,SCREAMING_SNAKE_CASE_=True    )      ->			Dict:

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

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

							lowercase__  :	str        =							True if config.feat_extract_norm == "layer" else False
							lowercase__  :	List[str]        =							WavaVecaFeatureExtractor(
							    feature_size=1    ,sampling_rate=1_60_00    ,padding_value=0    ,do_normalize=SCREAMING_SNAKE_CASE_    ,return_attention_mask=SCREAMING_SNAKE_CASE_    ,)

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

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

														lowercase__  :	List[Any]        =							SEWDForCTC(SCREAMING_SNAKE_CASE_    )
							else:
														lowercase__  :	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__":
						__a  :		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'''
						)
						__a  :		str		     =   parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)





import numpy as np
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			int:
							lowercase__  :	str        =							n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							lowercase__  :	Union[str, Any]        =							np.floor(SCREAMING_SNAKE_CASE_    )
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							lowercase__  :	Tuple        =							int(n_draws - floored.sum()    )
							if need_to_add > 0:
														lowercase__  :	Union[str, Any]        =							continuous - floored
														lowercase__  :	Optional[Any]        =							np.sort(np.unique(SCREAMING_SNAKE_CASE_    )    )[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					((lowercase__) ,       )  :	Optional[Any]        =							np.where(remainder == value    )
																					# if we need_to_add less than what's in inds
																					# we draw randomly from them.
																					# if we need to add more, we add them all and
																					# go to the next value
																					lowercase__  :	Dict        =							min(len(SCREAMING_SNAKE_CASE_    )    ,SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Any        =							rng.choice(SCREAMING_SNAKE_CASE_    ,size=SCREAMING_SNAKE_CASE_    ,replace=SCREAMING_SNAKE_CASE_    )
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa    )




def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=10    )      ->			List[str]:
							lowercase__ ,       lowercase__  :	int        =							np.unique(SCREAMING_SNAKE_CASE_    ,return_inverse=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	int        =							classes.shape[0]
							lowercase__  :	Union[str, Any]        =							np.bincount(SCREAMING_SNAKE_CASE_    )
							if np.min(SCREAMING_SNAKE_CASE_    ) < 2:
														raise ValueError("Minimum class count error"    )
							if n_train < n_classes:
														raise ValueError(
														    "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)    )
							if n_test < n_classes:
														raise ValueError(
														    "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)    )
							lowercase__  :	List[Any]        =							np.split(np.argsort(SCREAMING_SNAKE_CASE_    ,kind="mergesort"    )    ,np.cumsum(SCREAMING_SNAKE_CASE_    )[:-1]    )
							for _ in range(SCREAMING_SNAKE_CASE_    ):
														lowercase__  :	str        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
														lowercase__  :	int        =							class_counts - n_i
														lowercase__  :	int        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

														lowercase__  :	Tuple        =							[]
														lowercase__  :	int        =							[]

														for i in range(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Union[str, Any]        =							rng.permutation(class_counts[i]    )
																					lowercase__  :	List[Any]        =							class_indices[i].take(SCREAMING_SNAKE_CASE_    ,mode="clip"    )
																					train.extend(perm_indices_class_i[: n_i[i]]    )
																					test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]    )
														lowercase__  :	Tuple        =							rng.permutation(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Dict        =							rng.permutation(SCREAMING_SNAKE_CASE_    )

														yield train, test