




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()
lowercase_      :							Dict    							=					logging.get_logger(__name__)

lowercase_      :							Tuple    							=					{
    '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 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						for attribute in key.split("."		):
												_UpperCAmelCase						=	getattr(snake_case_      ,       snake_case_		)

						if weight_type is not None:
												_UpperCAmelCase						=	getattr(snake_case_      ,       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 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_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(
																		    snake_case_      ,       snake_case_      ,       snake_case_      ,       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(snake_case_		)[0].split("."		)[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				_UpperCAmelCase						=	mapped_key.replace("*"      ,       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(snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		)
																								continue
												if not is_used:
																		unused_weights.append(snake_case_		)

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_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(snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_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(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 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_=None      ,       snake_case_=None      ,       snake_case_=True		):

						'''simple docstring'''


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

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

						_UpperCAmelCase						=	True if config.feat_extract_norm == "layer" else False
						_UpperCAmelCase						=	WavaVecaFeatureExtractor(
						    feature_size=1      ,       sampling_rate=1_6000      ,       padding_value=0      ,       do_normalize=snake_case_      ,       return_attention_mask=snake_case_      ,       )

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

												_UpperCAmelCase						=	SEWDForCTC(snake_case_		)
						else:
												_UpperCAmelCase						=	SEWDModel(snake_case_		)
												feature_extractor.save_pretrained(snake_case_		)

						recursively_load_weights(snake_case_      ,       snake_case_      ,       snake_case_		)

						hf_model.save_pretrained(snake_case_		)


if __name__ == "__main__":
				lowercase_      :							Optional[int]    							=					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'
				)
				lowercase_      :							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
				)






def      __UpperCAmelCase    (  snake_case_							:		str				):



	'''simple docstring'''



	UpperCAmelCase:					Tuple 						=     str(snake_case_				).strip()
	if not bin_string:
		raise ValueError("Empty string was passed to the function"				)
	UpperCAmelCase:					List[Any] 						=     bin_string[0] == "-"
	if is_negative:
		UpperCAmelCase:					Optional[int] 						=     bin_string[1:]
	if not all(char in "01" for char in bin_string				):
		raise ValueError("Non-binary value was passed to the function"				)
	UpperCAmelCase:					List[str] 						=     0
	for char in bin_string:
		UpperCAmelCase:					Union[str, Any] 						=     2 * decimal_number + int(snake_case_				)
	return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()
