



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__A       =						logging.get_logger(__name__)

__A       =						{
    '''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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}



def 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : List[str]    )				->			List[str]:






						'''simple docstring'''



						for attribute in key.split("""."""    ):
												UpperCAmelCase_=       getattr(lowerCAmelCase_      ,lowerCAmelCase_    )

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

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



def 	__a					(   lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Optional[Any]    )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       fairseq_model.state_dict()

						UpperCAmelCase_=       hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												UpperCAmelCase_=       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,hf_model.config.feat_extract_norm == """group"""      ,)
																		UpperCAmelCase_=       True
												else:
																		for key, mapped_key in MAPPING.items():
																								UpperCAmelCase_=       """hubert.""" + 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] and not is_finetuned):
																														UpperCAmelCase_=       True
																														if "*" in mapped_key:
																																				UpperCAmelCase_=       name.split(lowerCAmelCase_    )[0].split("""."""    )[-2]
																																				UpperCAmelCase_=       mapped_key.replace("""*"""      ,lowerCAmelCase_    )
																														if "weight_g" in name:
																																				UpperCAmelCase_=       """weight_g"""
																														elif "weight_v" in name:
																																				UpperCAmelCase_=       """weight_v"""
																														elif "weight" in name:
																																				UpperCAmelCase_=       """weight"""
																														elif "bias" in name:
																																				UpperCAmelCase_=       """bias"""
																														else:
																																				UpperCAmelCase_=       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 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : Dict    )				->			List[Any]:






						'''simple docstring'''



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



@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : List[Any]=None      ,lowerCAmelCase_       : Tuple=None      ,lowerCAmelCase_       : int=True    )				->			Tuple:






						'''simple docstring'''



						if config_path is not None:
												UpperCAmelCase_=       HubertConfig.from_pretrained(lowerCAmelCase_    )
						else:
												UpperCAmelCase_=       HubertConfig()

						if is_finetuned:
												if dict_path:
																		UpperCAmelCase_=       Dictionary.load(lowerCAmelCase_    )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		UpperCAmelCase_=       target_dict.pad_index
																		UpperCAmelCase_=       target_dict.bos_index
																		UpperCAmelCase_=       target_dict.eos_index
																		UpperCAmelCase_=       len(target_dict.symbols    )
																		UpperCAmelCase_=       os.path.join(lowerCAmelCase_      ,"""vocab.json"""    )
																		if not os.path.isdir(lowerCAmelCase_    ):
																								logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCAmelCase_    )    )
																								return
																		os.makedirs(lowerCAmelCase_      ,exist_ok=lowerCAmelCase_    )
																		with open(lowerCAmelCase_      ,"""w"""      ,encoding="""utf-8"""    ) as vocab_handle:
																								json.dump(target_dict.indices      ,lowerCAmelCase_    )
																		UpperCAmelCase_=       WavaVecaCTCTokenizer(
																		    lowerCAmelCase_      ,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=lowerCAmelCase_      ,)
																		UpperCAmelCase_=       True if config.feat_extract_norm == """layer""" else False
																		UpperCAmelCase_=       WavaVecaFeatureExtractor(
																		    feature_size=1      ,sampling_rate=1_60_00      ,padding_value=0      ,do_normalize=lowerCAmelCase_      ,return_attention_mask=lowerCAmelCase_      ,)
																		UpperCAmelCase_=       WavaVecaProcessor(feature_extractor=lowerCAmelCase_      ,tokenizer=lowerCAmelCase_    )
																		processor.save_pretrained(lowerCAmelCase_    )

												UpperCAmelCase_=       HubertForCTC(lowerCAmelCase_    )
						else:
												UpperCAmelCase_=       HubertModel(lowerCAmelCase_    )

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

						UpperCAmelCase_=       model[0].eval()

						recursively_load_weights(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						hf_wavavec.save_pretrained(lowerCAmelCase_    )


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(
	    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
	)
	__A       =						parser.parse_args()
	convert_hubert_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)









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


__A       =						16
__A       =						32



def 	__a					(   lowerCAmelCase_       : Accelerator      ,lowerCAmelCase_       : int = 16      ,lowerCAmelCase_       : str = "bert-base-cased"    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       AutoTokenizer.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       load_dataset("""glue"""      ,"""mrpc"""    )

						def tokenize_function(lowerCAmelCase_       : List[Any]    ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase_=       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
						UpperCAmelCase_=       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
						UpperCAmelCase_=       tokenized_datasets.rename_column("""label"""      ,"""labels"""    )

						def collate_fn(lowerCAmelCase_       : List[Any]    ):
												# 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=1_28      ,return_tensors="""pt"""    )
												return tokenizer.pad(lowerCAmelCase_      ,padding="""longest"""      ,return_tensors="""pt"""    )

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

						return train_dataloader, eval_dataloader



def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : List[str]    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       Accelerator()

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

						set_seed(lowerCAmelCase_    )
						UpperCAmelCase_,       UpperCAmelCase_=       get_dataloaders(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

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

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

						if accelerator.state.deepspeed_plugin is not None:
												UpperCAmelCase_=       accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												UpperCAmelCase_=       1
						UpperCAmelCase_=       (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
						):
												UpperCAmelCase_=       get_linear_schedule_with_warmup(
												    optimizer=lowerCAmelCase_      ,num_warmup_steps=0      ,num_training_steps=lowerCAmelCase_      ,)
						else:
												UpperCAmelCase_=       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.
						UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       accelerator.prepare(
						    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						# We need to keep track of how many total steps we have iterated over
						UpperCAmelCase_=       0
						# We also need to keep track of the stating epoch so files are named properly
						UpperCAmelCase_=       0

						# Now we train the model
						UpperCAmelCase_=       evaluate.load("""glue"""      ,"""mrpc"""    )
						UpperCAmelCase_=       0
						UpperCAmelCase_=       {}
						for epoch in range(lowerCAmelCase_      ,lowerCAmelCase_    ):
												model.train()
												for step, batch in enumerate(lowerCAmelCase_    ):
																		UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		UpperCAmelCase_=       outputs.loss
																		UpperCAmelCase_=       loss / gradient_accumulation_steps
																		accelerator.backward(lowerCAmelCase_    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

												model.eval()
												UpperCAmelCase_=       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():
																								UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		UpperCAmelCase_=       outputs.logits.argmax(dim=-1    )
																		# It is slightly faster to call this once, than multiple times
																		UpperCAmelCase_,       UpperCAmelCase_=       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:
																														UpperCAmelCase_=       predictions[: len(eval_dataloader.dataset    ) - samples_seen]
																														UpperCAmelCase_=       references[: len(eval_dataloader.dataset    ) - samples_seen]
																								else:
																														samples_seen += references.shape[0]
																		metric.add_batch(
																		    predictions=lowerCAmelCase_      ,references=lowerCAmelCase_      ,)

												UpperCAmelCase_=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""      ,lowerCAmelCase_    )
												UpperCAmelCase_=       eval_metric["""accuracy"""]

												if best_performance < eval_metric["accuracy"]:
																		UpperCAmelCase_=       eval_metric["""accuracy"""]

						if args.performance_lower_bound is not None:
												assert (
												    args.performance_lower_bound <= best_performance
												), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
												with open(os.path.join(args.output_dir      ,"""all_results.json"""    )      ,"""w"""    ) as f:
																		json.dump(lowerCAmelCase_      ,lowerCAmelCase_    )



def 	__a					(   )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       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(
						    """--performance_lower_bound"""      ,type=lowerCAmelCase_      ,default=lowerCAmelCase_      ,help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""      ,)
						parser.add_argument(
						    """--num_epochs"""      ,type=lowerCAmelCase_      ,default=3      ,help="""Number of train epochs."""      ,)
						UpperCAmelCase_=       parser.parse_args()
						UpperCAmelCase_=       {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
						training_function(lowerCAmelCase_      ,lowerCAmelCase_    )


if __name__ == "__main__":
	main()




