


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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case    :   str	    =					logging.get_logger(__name__)

snake_case    :   List[Any]	    =					{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "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     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[Any]			,  UpperCAmelCase_						:		List[Any]			,  UpperCAmelCase_						:		int			,  UpperCAmelCase_						:		Tuple			,  UpperCAmelCase_						:		Any						):




						"""simple docstring"""





						for attribute in key.split('''.'''						):
												a						:List[Any]										=	getattr(UpperCAmelCase_			,  UpperCAmelCase_						)

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

						logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''						)
def     __lowerCamelCase						(			UpperCAmelCase_						:		int			,  UpperCAmelCase_						:		Optional[Any]			,  UpperCAmelCase_						:		Union[str, Any]						):




						"""simple docstring"""





						a						:Tuple										=	[]
						a						:Any										=	fairseq_model.state_dict()

						a						:List[Any]										=	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												a						:Dict										=	False
												if "conv_layers" in name:
																		load_conv_layer(
																		    UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_			,  hf_model.config.feat_extract_norm == '''group'''			,  )
																		a						:Optional[int]										=	True
												else:
																		for key, mapped_key in MAPPING.items():
																								a						:List[str]										=	'sew.' + 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]:
																														a						:str										=	True
																														if "*" in mapped_key:
																																				a						:Optional[int]										=	name.split(UpperCAmelCase_						)[0].split('''.'''						)[-2]
																																				a						:Optional[int]										=	mapped_key.replace('''*'''			,  UpperCAmelCase_						)
																														if "weight_g" in name:
																																				a						:int										=	'weight_g'
																														elif "weight_v" in name:
																																				a						:Any										=	'weight_v'
																														elif "weight" in name:
																																				a						:Union[str, Any]										=	'weight'
																														elif "bias" in name:
																																				a						:List[Any]										=	'bias'
																														else:
																																				a						: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     __lowerCamelCase						(			UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		int			,  UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Union[str, Any]			,  UpperCAmelCase_						:		Dict						):




						"""simple docstring"""





						a						:str										=	full_name.split('''conv_layers.'''						)[-1]
						a						:int										=	name.split('''.'''						)
						a						:int										=	int(items[0]						)
						a						: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.'''
																		)
																		a						:List[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.'''
																		)
																		a						: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."
																		)
																		a						:Optional[int]										=	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.'''
																		)
																		a						:Any										=	value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
						else:
												unused_weights.append(UpperCAmelCase_						)
def     __lowerCamelCase						(			UpperCAmelCase_						:		Any			,  UpperCAmelCase_						:		Dict						):




						"""simple docstring"""





						a						:Union[str, Any]										=	SEWConfig()
						if is_finetuned:
												a						:int										=	model.wav_encoder.wav_model.cfg
						else:
												a						:List[Any]										=	model.cfg

						a						:Optional[Any]										=	fs_config.conv_bias
						a						:Any										=	eval(fs_config.conv_feature_layers						)
						a						:str										=	[x[0] for x in conv_layers]
						a						:Dict										=	[x[1] for x in conv_layers]
						a						:List[Any]										=	[x[2] for x in conv_layers]
						a						:Any										=	'gelu'
						a						:int										=	'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
						a						:Any										=	0.0
						a						:Optional[Any]										=	fs_config.activation_fn.name
						a						:Any										=	fs_config.encoder_embed_dim
						a						:Tuple										=	0.02
						a						:int										=	fs_config.encoder_ffn_embed_dim
						a						:str										=	1E-5
						a						:Any										=	fs_config.encoder_layerdrop
						a						:int										=	fs_config.encoder_attention_heads
						a						:Optional[Any]										=	fs_config.conv_pos_groups
						a						:List[str]										=	fs_config.conv_pos
						a						:List[str]										=	len(UpperCAmelCase_						)
						a						:int										=	fs_config.encoder_layers
						a						:int										=	fs_config.squeeze_factor

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

						a						:Tuple										=	'Wav2Vec2FeatureExtractor'
						a						:List[Any]										=	'Wav2Vec2CTCTokenizer'

						return config
@torch.no_grad()
def     __lowerCamelCase						(			UpperCAmelCase_						:		Dict			,  UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		Optional[Any]=None			,  UpperCAmelCase_						:		Tuple=None			,  UpperCAmelCase_						:		Dict=True						):




						"""simple docstring"""





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

						if config_path is not None:
												a						:List[str]										=	SEWConfig.from_pretrained(UpperCAmelCase_						)
						else:
												a						:Optional[Any]										=	convert_config(model[0]			,  UpperCAmelCase_						)
						a						:Dict										=	model[0].eval()

						a						:List[str]										=	True if config.feat_extract_norm == 'layer' else False
						a						:Tuple										=	WavaVecaFeatureExtractor(
						    feature_size=1			,  sampling_rate=1_6000			,  padding_value=0			,  do_normalize=UpperCAmelCase_			,  return_attention_mask=UpperCAmelCase_			,  )

						if is_finetuned:
												if dict_path:
																		a						:Optional[Any]										=	Dictionary.load(UpperCAmelCase_						)

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

												a						:int										=	SEWForCTC(UpperCAmelCase_						)
						else:
												a						:str										=	SEWModel(UpperCAmelCase_						)
												feature_extractor.save_pretrained(UpperCAmelCase_						)

						recursively_load_weights(UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_						)

						hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
					snake_case    :   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'''
					)
					snake_case    :   List[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     __lowerCamelCase						(			UpperCAmelCase_						:		int = 100						):




      """simple docstring"""





      a						:List[Any]										=	0
      a						:List[Any]										=	0
      for i in range(1			,  n + 1						):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
     print(F"""{solution() = }""")





