"""simple docstring"""




import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowerCAmelCase     :				Dict    				=    16
__lowerCAmelCase     :				str    				=    32







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__ = 16      ,	lowerCamelCase__ = "bert-base-cased"  ):





		"""simple docstring"""




		lowerCAmelCase__   = AutoTokenizer.from_pretrained(lowerCamelCase__  )
		lowerCAmelCase__   = load_dataset("""glue"""      ,	"""mrpc"""  )

		def tokenize_function(lowerCamelCase__  ):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase__   = tokenizer(examples["""sentence1"""]      ,	examples["""sentence2"""]      ,	truncation=lowerCamelCase__      ,	max_length=lowerCamelCase__  )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		lowerCAmelCase__   = datasets.map(
		    lowerCamelCase__      ,	batched=lowerCamelCase__      ,	remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,	load_from_cache_file=lowerCamelCase__  )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowerCAmelCase__   = tokenized_datasets.rename_column("""label"""      ,	"""labels"""  )

		def collate_fn(lowerCamelCase__  ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(lowerCamelCase__      ,	padding="""max_length"""      ,	max_length=128      ,	return_tensors="""pt"""  )
				return tokenizer.pad(lowerCamelCase__      ,	padding="""longest"""      ,	return_tensors="""pt"""  )

		# Instantiate dataloaders.
		lowerCAmelCase__   = DataLoader(
		    tokenized_datasets["""train"""]      ,	shuffle=lowerCamelCase__      ,	collate_fn=lowerCamelCase__      ,	batch_size=lowerCamelCase__  )
		lowerCAmelCase__   = DataLoader(
		    tokenized_datasets["""validation"""]      ,	shuffle=lowerCamelCase__      ,	collate_fn=lowerCamelCase__      ,	batch_size=lowerCamelCase__  )

		return train_dataloader, eval_dataloader







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		model.eval()
		lowerCAmelCase__   = 0
		for step, batch in enumerate(lowerCamelCase__  ):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device  )
				with torch.no_grad():
						lowerCAmelCase__   = model(**lowerCamelCase__  )
				lowerCAmelCase__   = outputs.logits.argmax(dim=-1  )
				# It is slightly faster to call this once, than multiple times
				lowerCAmelCase__ ,       lowerCAmelCase__   = accelerator.gather(
				    (predictions, batch["""labels"""])  )  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(lowerCamelCase__  ) - 1:
								lowerCAmelCase__   = predictions[: len(eval_dataloader.dataset  ) - samples_seen]
								lowerCAmelCase__   = references[: len(eval_dataloader.dataset  ) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=lowerCamelCase__      ,	references=lowerCamelCase__      ,	)

		lowerCAmelCase__   = metric.compute()
		return eval_metric["accuracy"]







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase__   = config["""lr"""]
		lowerCAmelCase__   = int(config["""num_epochs"""]  )
		lowerCAmelCase__   = int(config["""seed"""]  )
		lowerCAmelCase__   = int(config["""batch_size"""]  )
		lowerCAmelCase__   = args.model_name_or_path

		set_seed(lowerCamelCase__  )
		lowerCAmelCase__ ,       lowerCAmelCase__   = get_dataloaders(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase__   = AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__      ,	return_dict=lowerCamelCase__  )

		# Instantiate optimizer
		lowerCAmelCase__   = (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowerCAmelCase__   = optimizer_cls(params=model.parameters()      ,	lr=lowerCamelCase__  )

		if accelerator.state.deepspeed_plugin is not None:
				lowerCAmelCase__   = accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				lowerCAmelCase__   = 1
		lowerCAmelCase__   = (len(lowerCamelCase__  ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				lowerCAmelCase__   = get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase__      ,	num_warmup_steps=0      ,	num_training_steps=lowerCamelCase__      ,	)
		else:
				lowerCAmelCase__   = DummyScheduler(lowerCamelCase__      ,	total_num_steps=lowerCamelCase__      ,	warmup_num_steps=0  )

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = accelerator.prepare(
		    lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )

		# We need to keep track of how many total steps we have iterated over
		lowerCAmelCase__   = 0
		# We also need to keep track of the stating epoch so files are named properly
		lowerCAmelCase__   = 0
		lowerCAmelCase__   = evaluate.load("""glue"""      ,	"""mrpc"""  )
		lowerCAmelCase__   = num_epochs

		if args.partial_train_epoch is not None:
				lowerCAmelCase__   = args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint  )
				lowerCAmelCase__   = args.resume_from_checkpoint.split("""epoch_"""  )[1]
				lowerCAmelCase__   = """"""
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				lowerCAmelCase__   = int(lowerCamelCase__  ) + 1
				lowerCAmelCase__   = evaluation_loop(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )
				accelerator.print("""resumed checkpoint performance:"""      ,	lowerCamelCase__  )
				accelerator.print("""resumed checkpoint's scheduler's lr:"""      ,	lr_scheduler.get_lr()[0]  )
				accelerator.print("""resumed optimizers's lr:"""      ,	optimizer.param_groups[0]["""lr"""]  )
				with open(os.path.join(args.output_dir      ,	f"""state_{starting_epoch-1}.json"""  )      ,	"""r"""  ) as f:
						lowerCAmelCase__   = json.load(lowerCamelCase__  )
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		lowerCAmelCase__   = {}
		for epoch in range(lowerCamelCase__      ,	lowerCamelCase__  ):
				model.train()
				for step, batch in enumerate(lowerCamelCase__  ):
						lowerCAmelCase__   = model(**lowerCamelCase__  )
						lowerCAmelCase__   = outputs.loss
						lowerCAmelCase__   = loss / gradient_accumulation_steps
						accelerator.backward(lowerCamelCase__  )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				lowerCAmelCase__   = f"""epoch_{epoch}"""
				lowerCAmelCase__   = os.path.join(args.output_dir      ,	lowerCamelCase__  )
				accelerator.save_state(lowerCamelCase__  )
				lowerCAmelCase__   = evaluation_loop(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )
				lowerCAmelCase__   = accuracy
				lowerCAmelCase__   = lr_scheduler.get_lr()[0]
				lowerCAmelCase__   = optimizer.param_groups[0]["""lr"""]
				lowerCAmelCase__   = epoch
				lowerCAmelCase__   = overall_step
				accelerator.print(f"""epoch {epoch}:"""      ,	lowerCamelCase__  )

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir      ,	f"""state_{epoch}.json"""  )      ,	"""w"""  ) as f:
								json.dump(lowerCamelCase__      ,	lowerCamelCase__  )







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		lowerCAmelCase__   = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""  )
		parser.add_argument(
		    """--model_name_or_path"""      ,	type=lowerCamelCase__      ,	default="""bert-base-cased"""      ,	help="""Path to pretrained model or model identifier from huggingface.co/models."""      ,	required=lowerCamelCase__      ,	)
		parser.add_argument(
		    """--output_dir"""      ,	type=lowerCamelCase__      ,	default="""."""      ,	help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""      ,	)
		parser.add_argument(
		    """--resume_from_checkpoint"""      ,	type=lowerCamelCase__      ,	default=lowerCamelCase__      ,	help="""If the training should continue from a checkpoint folder."""      ,	)
		parser.add_argument(
		    """--partial_train_epoch"""      ,	type=lowerCamelCase__      ,	default=lowerCamelCase__      ,	help="""If passed, the training will stop after this number of epochs."""      ,	)
		parser.add_argument(
		    """--num_epochs"""      ,	type=lowerCamelCase__      ,	default=2      ,	help="""Number of train epochs."""      ,	)
		lowerCAmelCase__   = parser.parse_args()
		lowerCAmelCase__   = {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

		training_function(lowerCamelCase__      ,	lowerCamelCase__  )


if __name__ == "__main__":
			main()





"""simple docstring"""




import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__lowerCAmelCase     :				int    				=    logging.get_logger(__name__)

__lowerCAmelCase     :				Dict    				=    {
    "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",
}
__lowerCAmelCase     :				Optional[Any]    				=    [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		for attribute in key.split("""."""  ):
				lowerCAmelCase__   = getattr(lowerCamelCase__      ,	lowerCamelCase__  )

		if weight_type is not None:
				lowerCAmelCase__   = getattr(lowerCamelCase__      ,	lowerCamelCase__  ).shape
		else:
				lowerCAmelCase__   = 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":
				lowerCAmelCase__   = value
		elif weight_type == "weight_g":
				lowerCAmelCase__   = value
		elif weight_type == "weight_v":
				lowerCAmelCase__   = value
		elif weight_type == "bias":
				lowerCAmelCase__   = value
		else:
				lowerCAmelCase__   = value

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = []
		lowerCAmelCase__   = fairseq_model.state_dict()

		lowerCAmelCase__   = hf_model.feature_extractor
		lowerCAmelCase__   = hf_model.adapter

		for name, value in fairseq_dict.items():
				lowerCAmelCase__   = False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	hf_model.config.feat_extract_norm == """group"""      ,	)
						lowerCAmelCase__   = True
				elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]  ):
						load_adapter(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )
						lowerCAmelCase__   = True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split("""w2v_model."""  )[-1] == name.split("""."""  )[0]:
										lowerCAmelCase__   = True
										if "*" in mapped_key:
												lowerCAmelCase__   = name.split(lowerCamelCase__  )[0].split("""."""  )[-2]
												lowerCAmelCase__   = mapped_key.replace("""*"""      ,	lowerCamelCase__  )
										if "weight_g" in name:
												lowerCAmelCase__   = """weight_g"""
										elif "weight_v" in name:
												lowerCAmelCase__   = """weight_v"""
										elif "bias" in name:
												lowerCAmelCase__   = """bias"""
										elif "weight" in name:
												lowerCAmelCase__   = """weight"""
										else:
												lowerCAmelCase__   = None
										set_recursively(lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )
								continue
				if not is_used:
						unused_weights.append(lowerCamelCase__  )

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = full_name.split("""conv_layers."""  )[-1]
		lowerCAmelCase__   = name.split("""."""  )
		lowerCAmelCase__   = int(items[0]  )
		lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = 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."
						)
						lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
		else:
				unused_weights.append(lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = full_name.split("""adaptor."""  )[-1]
		lowerCAmelCase__   = name.split("""."""  )

		if items[1].isdigit():
				lowerCAmelCase__   = int(items[1]  )
		else:
				lowerCAmelCase__   = None

		if "adaptor" not in full_name:
				if "proj_ln" in full_name:
						# has to be layer norm
						if "bias" in name:
								assert (
								    value.shape == adapter.proj_layer_norm.bias.data.shape
								), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
								lowerCAmelCase__   = value
								logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""  )
						if "weight" in name:
								assert (
								    value.shape == adapter.proj_layer_norm.weight.data.shape
								), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
								lowerCAmelCase__   = value
				else:
						# has to be projection layer
						if "bias" in name:
								assert (
								    value.shape == adapter.proj.bias.data.shape
								), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
								lowerCAmelCase__   = value
								logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""  )
						if "weight" in name:
								assert (
								    value.shape == adapter.proj.weight.data.shape
								), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
								lowerCAmelCase__   = value
								logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""  )
		elif isinstance(lowerCamelCase__      ,	lowerCamelCase__  ):
				if "bias" in name:
						assert (
						    value.shape == adapter.layers[layer_id].conv.bias.data.shape
						), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
						lowerCAmelCase__   = value
						logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""  )
				elif "weight" in name:
						assert (
						    value.shape == adapter.layers[layer_id].conv.weight.data.shape
						), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
						lowerCAmelCase__   = value
						logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""  )
		else:
				unused_weights.append(lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__ ,       lowerCAmelCase__   = emb.weight.shape
		lowerCAmelCase__   = nn.Linear(lowerCamelCase__      ,	lowerCamelCase__      ,	bias=lowerCamelCase__  )
		lowerCAmelCase__   = emb.weight.data
		return lin_layer







@torch.no_grad()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	):





		"""simple docstring"""




		lowerCAmelCase__   = WavaVecaConfig.from_pretrained(
		    lowerCamelCase__      ,	add_adapter=lowerCamelCase__      ,	adapter_stride=lowerCamelCase__      ,	adapter_kernel_size=lowerCamelCase__      ,	use_auth_token=lowerCamelCase__      ,	output_hidden_size=lowerCamelCase__      ,	)
		lowerCAmelCase__   = MBartConfig.from_pretrained(lowerCamelCase__  )

		# load model
		lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]      ,	arg_overrides={
		        """config_yaml""": config_yaml_path,
		        """data""": """/""".join(dict_path.split("""/"""  )[:-1]  ),
		        """w2v_path""": checkpoint_path,
		        """load_pretrained_decoder_from""": None,
		    }      ,	)
		lowerCAmelCase__   = model[0].eval()

		# load feature extractor
		lowerCAmelCase__   = WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__      ,	use_auth_token=lowerCamelCase__  )

		# set weights for wav2vec2 encoder
		lowerCAmelCase__   = WavaVecaModel(lowerCamelCase__  )

		recursively_load_weights_wavaveca(model.encoder      ,	lowerCamelCase__  )

		# load decoder weights
		lowerCAmelCase__   = MBartForCausalLM(lowerCamelCase__  )
		lowerCAmelCase__ ,       lowerCAmelCase__   = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,	strict=lowerCamelCase__  )
		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}"""  )

		lowerCAmelCase__   = SpeechEncoderDecoderModel(encoder=lowerCamelCase__      ,	decoder=lowerCamelCase__  )
		lowerCAmelCase__   = False

		lowerCAmelCase__   = MBartaaTokenizer(lowerCamelCase__  )
		tokenizer.save_pretrained(lowerCamelCase__  )

		lowerCAmelCase__   = hf_wavavec.config.to_dict()
		lowerCAmelCase__   = tokenizer.pad_token_id
		lowerCAmelCase__   = tokenizer.bos_token_id
		lowerCAmelCase__   = tokenizer.eos_token_id
		lowerCAmelCase__   = """mbart50"""
		lowerCAmelCase__   = """wav2vec2"""

		lowerCAmelCase__   = tokenizer.eos_token_id
		lowerCAmelCase__   = 25_0004
		lowerCAmelCase__   = tokenizer.eos_token_id

		lowerCAmelCase__   = SpeechEncoderDecoderConfig.from_dict(lowerCamelCase__  )

		hf_wavavec.save_pretrained(lowerCamelCase__  )
		feature_extractor.save_pretrained(lowerCamelCase__  )


if __name__ == "__main__":
			__lowerCAmelCase     :				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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
			parser.add_argument(
			    "--encoder_config_path",
			    default="facebook/wav2vec2-xls-r-1b",
			    type=str,
			    help="Path to hf encoder wav2vec2 checkpoint config",
			)
			parser.add_argument(
			    "--decoder_config_path",
			    default="facebook/mbart-large-50-one-to-many-mmt",
			    type=str,
			    help="Path to hf decoder checkpoint config",
			)
			parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
			parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
			parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
			parser.add_argument("--encoder_output_dim", default=10_24, type=int, help="encoder output dim")
			parser.add_argument("--start_token_id", default=25_00_04, type=int, help="`decoder_start_token_id` of model config")

			__lowerCAmelCase     :				Union[str, Any]    				=    parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    args.config_yaml_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    add_adapter=args.add_adapter,
			    adapter_kernel_size=args.adapter_kernel_size,
			    adapter_stride=args.adapter_stride,
			    decoder_start_token_id=args.start_token_id,
			    encoder_output_dim=args.encoder_output_dim,
			)




