

"""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()
lowerCamelCase__				:  Tuple													=  logging.get_logger(__name__)

lowerCamelCase__				:  int													=  {
    '''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       UpperCamelCase					(			_lowerCAmelCase     :						Optional[Any],  _lowerCAmelCase     :						Tuple,  _lowerCAmelCase     :						Union[str, Any],  _lowerCAmelCase     :						Tuple,  _lowerCAmelCase     :						Dict )				->			List[Any]:
	for attribute in key.split(""".""" ):
		_UpperCAmelCase    :							Union[str, Any]									=					getattr(UpperCamelCase__,  UpperCamelCase__ )

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

	logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def       UpperCamelCase					(			_lowerCAmelCase     :						Any,  _lowerCAmelCase     :						Any,  _lowerCAmelCase     :						Any )				->			List[str]:
	_UpperCAmelCase    :							Optional[Any]									=					[]
	_UpperCAmelCase    :							Optional[int]									=					fairseq_model.state_dict()

	_UpperCAmelCase    :							Any									=					hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_UpperCAmelCase    :							Optional[int]									=					False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__,  hf_model.config.feat_extract_norm == """group""",  )
			_UpperCAmelCase    :							Optional[Any]									=					True
		else:
			for key, mapped_key in MAPPING.items():
				_UpperCAmelCase    :							str									=					"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    :							Dict									=					True
					if "*" in mapped_key:
						_UpperCAmelCase    :							Tuple									=					name.split(UpperCamelCase__ )[0].split(""".""" )[-2]
						if not layer_index.isnumeric():
							continue
						_UpperCAmelCase    :							List[Any]									=					mapped_key.replace("""*""",  UpperCamelCase__ )
					if "weight_g" in name:
						_UpperCAmelCase    :							Dict									=					"weight_g"
					elif "weight_v" in name:
						_UpperCAmelCase    :							Dict									=					"weight_v"
					elif "weight" in name:
						_UpperCAmelCase    :							Optional[int]									=					"weight"
					elif "bias" in name:
						_UpperCAmelCase    :							Any									=					"bias"
					else:
						_UpperCAmelCase    :							List[str]									=					None
					set_recursively(UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__ )
				continue
		if not is_used:
			unused_weights.append(UpperCamelCase__ )

	logger.warning(f'''Unused weights: {unused_weights}''' )
def       UpperCamelCase					(			_lowerCAmelCase     :						List[str],  _lowerCAmelCase     :						str,  _lowerCAmelCase     :						List[Any],  _lowerCAmelCase     :						Optional[int],  _lowerCAmelCase     :						List[str] )				->			Dict:
	_UpperCAmelCase    :							Any									=					full_name.split("""conv_layers.""" )[-1]
	_UpperCAmelCase    :							Union[str, Any]									=					name.split(""".""" )
	_UpperCAmelCase    :							Union[str, Any]									=					int(items[0] )
	_UpperCAmelCase    :							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.'''
			)
			_UpperCAmelCase    :							Optional[Any]									=					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    :							Optional[int]									=					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    :							Union[str, 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    :							int									=					value
			logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
	else:
		unused_weights.append(UpperCamelCase__ )
def       UpperCamelCase					(			_lowerCAmelCase     :						Tuple,  _lowerCAmelCase     :						Any )				->			Dict:
	_UpperCAmelCase    :							Any									=					SEWDConfig()
	if is_finetuned:
		_UpperCAmelCase    :							List[str]									=					model.wav_encoder.wav_model.cfg
	else:
		_UpperCAmelCase    :							Tuple									=					model.cfg

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

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

	_UpperCAmelCase    :							Dict									=					"Wav2Vec2FeatureExtractor"
	_UpperCAmelCase    :							List[str]									=					"Wav2Vec2CTCTokenizer"

	return config
@torch.no_grad()
def       UpperCamelCase					(			_lowerCAmelCase     :						Any,  _lowerCAmelCase     :						List[Any],  _lowerCAmelCase     :						Tuple=None,  _lowerCAmelCase     :						Union[str, Any]=None,  _lowerCAmelCase     :						List[Any]=True )				->			Union[str, Any]:

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

	if config_path is not None:
		_UpperCAmelCase    :							Dict									=					SEWDConfig.from_pretrained(UpperCamelCase__ )
	else:
		_UpperCAmelCase    :							Optional[int]									=					convert_config(model[0],  UpperCamelCase__ )
	_UpperCAmelCase    :							Any									=					model[0].eval()

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

	if is_finetuned:
		if dict_path:
			_UpperCAmelCase    :							Tuple									=					Dictionary.load(UpperCamelCase__ )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			_UpperCAmelCase    :							List[str]									=					target_dict.pad_index
			_UpperCAmelCase    :							Dict									=					target_dict.bos_index
			_UpperCAmelCase    :							Union[str, Any]									=					target_dict.pad_index
			_UpperCAmelCase    :							str									=					target_dict.bos_index
			_UpperCAmelCase    :							Any									=					target_dict.eos_index
			_UpperCAmelCase    :							Any									=					len(target_dict.symbols )
			_UpperCAmelCase    :							Tuple									=					os.path.join(UpperCamelCase__,  """vocab.json""" )
			if not os.path.isdir(UpperCamelCase__ ):
				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(UpperCamelCase__ ) )
				return
			os.makedirs(UpperCamelCase__,  exist_ok=UpperCamelCase__ )
			with open(UpperCamelCase__,  """w""",  encoding="""utf-8""" ) as vocab_handle:
				json.dump(target_dict.indices,  UpperCamelCase__ )
			_UpperCAmelCase    :							int									=					WavaVecaCTCTokenizer(
			    UpperCamelCase__,  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=UpperCamelCase__,  )
			_UpperCAmelCase    :							List[Any]									=					WavaVecaProcessor(feature_extractor=UpperCamelCase__,  tokenizer=UpperCamelCase__ )
			processor.save_pretrained(UpperCamelCase__ )

		_UpperCAmelCase    :							Dict									=					SEWDForCTC(UpperCamelCase__ )
	else:
		_UpperCAmelCase    :							Any									=					SEWDModel(UpperCamelCase__ )
		feature_extractor.save_pretrained(UpperCamelCase__ )

	recursively_load_weights(UpperCamelCase__,  UpperCamelCase__,  UpperCamelCase__ )

	hf_model.save_pretrained(UpperCamelCase__ )


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






def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int = 1000 )				->			str:
	if (not isinstance(_lowerCAmelCase,  _lowerCAmelCase )) or (digit_position <= 0):
		raise ValueError("""Digit position must be a positive integer""" )
	elif (not isinstance(_lowerCAmelCase,  _lowerCAmelCase )) or (precision < 0):
		raise ValueError("""Precision must be a nonnegative integer""" )

	# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
	# accurate
	_UpperCAmelCase    :							List[Any]									=					(
	    4 * _subsum(_lowerCAmelCase,  1,  _lowerCAmelCase )
	    - 2 * _subsum(_lowerCAmelCase,  4,  _lowerCAmelCase )
	    - _subsum(_lowerCAmelCase,  5,  _lowerCAmelCase )
	    - _subsum(_lowerCAmelCase,  6,  _lowerCAmelCase )
	)

	# return the first hex digit of the fractional part of the result
	return hex(int((sum_result % 1) * 16 ) )[2:]
def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						int )				->			float:
	_UpperCAmelCase    :							Optional[int]									=					0.0
	for sum_index in range(digit_pos_to_extract + precision ):
		_UpperCAmelCase    :							Optional[Any]									=					8 * sum_index + denominator_addend
		if sum_index < digit_pos_to_extract:
			# if the exponential term is an integer and we mod it by the denominator
			# before dividing, only the integer part of the sum will change;
			# the fractional part will not
			_UpperCAmelCase    :							Optional[Any]									=					pow(
			    16,  digit_pos_to_extract - 1 - sum_index,  _lowerCAmelCase )
		else:
			_UpperCAmelCase    :							int									=					pow(16,  digit_pos_to_extract - 1 - sum_index )
		total += exponential_term / denominator
	return total


if __name__ == "__main__":
						import doctest

						doctest.testmod()





