




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__		    =    logging.get_logger(__name__)

a__		    =    {
    '''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 							A__   (snake_case      :		List[Any]		,  snake_case      :		Any		,  snake_case      :		Any		,  snake_case      :		List[Any]		,  snake_case      :		Union[str, Any]							)    ->	Dict:
					for attribute in key.split("""."""							):
										__UpperCamelCase				:  Any								=			getattr(snake_case		,  snake_case							)

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

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





def 							A__   (snake_case      :		Dict		,  snake_case      :		str		,  snake_case      :		Dict							)    ->	str:
					__UpperCamelCase				:  str								=			[]
					__UpperCamelCase				:  Dict								=			fairseq_model.state_dict()

					__UpperCamelCase				:  List[Any]								=			hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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

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

					__UpperCamelCase				:  Dict								=			"""Wav2Vec2FeatureExtractor"""
					__UpperCamelCase				:  str								=			"""Wav2Vec2CTCTokenizer"""

					return config





@torch.no_grad()
def 							A__   (snake_case      :		int		,  snake_case      :		Tuple		,  snake_case      :		Dict=None		,  snake_case      :		Union[str, Any]=None		,  snake_case      :		Tuple=True							)    ->	str:

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

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

					__UpperCamelCase				:  List[Any]								=			True if config.feat_extract_norm == """layer""" else False
					__UpperCamelCase				:  Optional[int]								=			WavaVecaFeatureExtractor(
					    feature_size=1		,  sampling_rate=1_60_00		,  padding_value=0		,  do_normalize=snake_case		,  return_attention_mask=snake_case		,  )

					if is_finetuned:
										if dict_path:
															__UpperCamelCase				:  int								=			Dictionary.load(snake_case							)

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

										__UpperCamelCase				:  str								=			SEWDForCTC(snake_case							)
					else:
										__UpperCamelCase				:  Any								=			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__":
	a__		    =    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__		    =    parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)












a__		    =    {
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.00_0984_2073,
    "short-ton": 0.00_1102_3122,
    "pound": 2.20_4624_4202,
    "stone": 0.15_7473_1728,
    "ounce": 35.2_7399_0723,
    "carrat": 5000,
    "atomic-mass-unit": 6.0_2213_6652e26,
}

a__		    =    {
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.0_4608,
    "short-ton": 907.184,
    "pound": 0.45_3592,
    "stone": 6.3_5029,
    "ounce": 0.028_3495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.6_6054_0199e-27,
}





def 							A__   (snake_case      :		str		,  snake_case      :		str		,  snake_case      :		float							)    ->	float:
					if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
										__UpperCamelCase				:  Any								=			(
										    F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
										    F'''Supported values are: {", ".join(snake_case							)}'''
										)
										raise ValueError(snake_case							)
					return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()






