




import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

UpperCAmelCase_		:					int          =  {
    "post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
UpperCAmelCase_		:					Union[str, Any]          =  [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				for attribute in key.split(""".""" ):
								__magic_name__						:						Tuple										=getattr(lowerCamelCase						,  lowerCamelCase )

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

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




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Optional[Any]										=[]
				__magic_name__						:						Tuple										=fairseq_model.state_dict()

				__magic_name__						:						Union[str, Any]										=hf_model.feature_extractor

				# if encoder has different dim to decoder -> use proj_weight
				__magic_name__						:						List[Any]										=None

				for name, value in fairseq_dict.items():
								__magic_name__						:						Dict										=False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  hf_model.config.feat_extract_norm == """group"""						,  )
												__magic_name__						:						str										=True
								elif name.split(""".""" )[0] == "proj":
												__magic_name__						:						List[str]										=fairseq_model.proj
												__magic_name__						:						Tuple										=True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
																				__magic_name__						:						int										=True
																				if "*" in mapped_key:
																								__magic_name__						:						List[str]										=name.split(lowerCamelCase )[0].split(""".""" )[-2]
																								__magic_name__						:						str										=mapped_key.replace("""*"""						,  lowerCamelCase )
																				if "weight_g" in name:
																								__magic_name__						:						Optional[Any]										="""weight_g"""
																				elif "weight_v" in name:
																								__magic_name__						:						Optional[Any]										="""weight_v"""
																				elif "bias" in name:
																								__magic_name__						:						Tuple										="""bias"""
																				elif "weight" in name:
																								__magic_name__						:						int										="""weight"""
																				else:
																								__magic_name__						:						Union[str, Any]										=None
																				set_recursively(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
																continue
								if not is_used:
												unused_weights.append(lowerCamelCase )

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

				return proj_weight




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Union[str, Any]										=full_name.split("""conv_layers.""" )[-1]
				__magic_name__						:						Tuple										=name.split(""".""" )
				__magic_name__						:						int										=int(items[0] )
				__magic_name__						:						Union[str, 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."
												)
												__magic_name__						:						Tuple										=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."
												)
												__magic_name__						:						List[str]										=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."
												)
												__magic_name__						:						List[str]										=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."
												)
												__magic_name__						:						List[Any]										=value
												logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
				else:
								unused_weights.append(lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__					,			__magic_name__						:						Tuple										=emb.weight.shape
				__magic_name__						:						Dict										=nn.Linear(lowerCamelCase						,  lowerCamelCase						,  bias=lowerCamelCase )
				__magic_name__						:						int										=emb.weight.data
				return lin_layer




def 		lowerCAmelCase_ (						lowerCamelCase ):
				with open(lowerCamelCase						,  """r"""						,  encoding="""utf-8""" ) as f:
								__magic_name__						:						Optional[Any]										=f.readlines()
								__magic_name__						:						Tuple										=[line.split(""" """ )[0] for line in lines]

				__magic_name__						:						List[Any]										=len(lowerCamelCase )

				__magic_name__						:						List[str]										={
				    """<s>""": 0,
				    """<pad>""": 1,
				    """</s>""": 2,
				    """<unk>""": 3,
				}

				vocab_dict.update(dict(zip(lowerCamelCase						,  range(4						,  num_words + 4 ) ) ) )
				return vocab_dict




@torch.no_grad()
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  ):
				__magic_name__						:						Tuple										=WavaVecaConfig.from_pretrained(lowerCamelCase )
				__magic_name__						:						List[Any]										=SpeechaTextaConfig.from_pretrained(
				    lowerCamelCase						,  vocab_size=lowerCamelCase						,  decoder_layers=lowerCamelCase						,  do_stable_layer_norm=lowerCamelCase )

				__magic_name__						:						List[str]										=WavaVecaFeatureExtractor(
				    feature_size=1						,  sampling_rate=16000						,  padding_value=0						,  do_normalize=lowerCamelCase						,  return_attention_mask=lowerCamelCase						,  )

				__magic_name__					,			__magic_name__					,			__magic_name__						:						List[Any]										=fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]						,  arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
				__magic_name__						:						Dict										=model[0].eval()

				# set weights for wav2vec2 encoder
				__magic_name__						:						Optional[Any]										=WavaVecaModel(lowerCamelCase )
				__magic_name__						:						Tuple										=recursively_load_weights_wavaveca(model.encoder						,  lowerCamelCase )

				__magic_name__						:						Tuple										=SpeechaTextaForCausalLM(lowerCamelCase )
				__magic_name__					,			__magic_name__						:						Dict										=hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,  strict=lowerCamelCase )

				# set output linear layer
				unexpected_keys.remove("""embed_out""" )
				__magic_name__						:						List[str]										=nn.Parameter(model.decoder.embed_out.detach() )

				# layer norm is init to identity matrix so leaving it is fine
				logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}" )
				logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}" )

				__magic_name__						:						Optional[int]										=SpeechEncoderDecoderModel(encoder=lowerCamelCase						,  decoder=lowerCamelCase )
				__magic_name__						:						Union[str, Any]										=False

				# add projection layer
				__magic_name__						:						List[str]										=nn.Parameter(projection_layer.weight )
				__magic_name__						:						Dict										=nn.Parameter(projection_layer.bias )

				__magic_name__						:						str										=create_vocab_dict(lowerCamelCase )

				with open(os.path.join(lowerCamelCase						,  """vocab.json""" )						,  """w""" ) as fp:
								json.dump(lowerCamelCase						,  lowerCamelCase )

				__magic_name__						:						Any										=SpeechaTextaTokenizer(os.path.join(lowerCamelCase						,  """vocab.json""" ) )
				tokenizer.save_pretrained(lowerCamelCase )

				__magic_name__						:						str										=hf_wavavec.config.to_dict()
				__magic_name__						:						int										=tokenizer.pad_token_id
				__magic_name__						:						Optional[Any]										=tokenizer.bos_token_id
				__magic_name__						:						Optional[Any]										=tokenizer.eos_token_id
				__magic_name__						:						Tuple										="""speech_to_text_2"""
				__magic_name__						:						Union[str, Any]										="""wav2vec2"""

				__magic_name__						:						Union[str, Any]										=SpeechEncoderDecoderConfig.from_dict(lowerCamelCase )

				hf_wavavec.save_pretrained(lowerCamelCase )
				feature_extractor.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
							UpperCAmelCase_		:					Dict          =  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(
							    "--encoder_config_path",
							    default="facebook/wav2vec2-large-lv60",
							    type=str,
							    help="Path to hf encoder wav2vec2 checkpoint config",
							)
							parser.add_argument(
							    "--decoder_config_path",
							    default="facebook/s2t-small-mustc-en-fr-st",
							    type=str,
							    help="Path to hf decoder s2t checkpoint config",
							)
							parser.add_argument("--vocab_size", default=10224, type=int, help="Vocab size of decoder")
							parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

							UpperCAmelCase_		:					Any          =  parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    vocab_size=args.vocab_size,
							    num_decoder_layers=args.num_decoder_layers,
							)










from pathlib import Path

import fire
from tqdm import tqdm




def 		lowerCAmelCase_ (						lowerCamelCase="ro"						,  lowerCamelCase="en"						,  lowerCamelCase="wmt16"						,  lowerCamelCase=None ):
				try:
								import datasets
				except (ModuleNotFoundError, ImportError):
								raise ImportError("""run pip install datasets""" )
				__magic_name__						:						Dict										=F"{src_lang}-{tgt_lang}"
				print(F"Converting {dataset}-{pair}" )
				__magic_name__						:						Dict										=datasets.load_dataset(lowerCamelCase						,  lowerCamelCase )
				if save_dir is None:
								__magic_name__						:						Optional[int]										=F"{dataset}-{pair}"
				__magic_name__						:						int										=Path(lowerCamelCase )
				save_dir.mkdir(exist_ok=lowerCamelCase )

				for split in ds.keys():
								print(F"Splitting {split} with {ds[split].num_rows} records" )

								# to save to val.source, val.target like summary datasets
								__magic_name__						:						Dict										="""val""" if split == """validation""" else split
								__magic_name__						:						List[Any]										=save_dir.joinpath(F"{fn}.source" )
								__magic_name__						:						Optional[int]										=save_dir.joinpath(F"{fn}.target" )
								__magic_name__						:						Optional[Any]										=src_path.open("""w+""" )
								__magic_name__						:						List[Any]										=tgt_path.open("""w+""" )

								# reader is the bottleneck so writing one record at a time doesn't slow things down
								for x in tqdm(ds[split] ):
												__magic_name__						:						str										=x["""translation"""]
												src_fp.write(ex[src_lang] + """\n""" )
												tgt_fp.write(ex[tgt_lang] + """\n""" )

				print(F"Saved {dataset} dataset to {save_dir}" )


if __name__ == "__main__":
							fire.Fire(download_wmt_dataset)




