
'''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


_lowercase          =						16
_lowercase          =						32


def A (__lowerCamelCase :Accelerator					,      __lowerCamelCase :int = 16					,      __lowerCamelCase :str = "bert-base-cased"						):
						_lowerCAmelCase    		=					AutoTokenizer.from_pretrained(__lowerCamelCase						)
						_lowerCAmelCase    		=					load_dataset("""glue"""					,      """mrpc"""						)

						def tokenize_function(__lowerCamelCase :int						):
												# 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 :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=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 A (__lowerCamelCase :Any					,      __lowerCamelCase :Optional[int]					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :Union[str, Any]						):
						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 A (__lowerCamelCase :Tuple					,      __lowerCamelCase :Optional[Any]						):
						# Initialize accelerator
						_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 A ():
						_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 json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

_lowercase          =						{
    """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""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
_lowercase          =						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]


def A (__lowerCamelCase :Optional[int]						):
						_lowerCAmelCase    		=					{}
						with open(__lowerCamelCase					,      """r"""						) as file:
												for line_number, line in enumerate(__lowerCamelCase						):
																		_lowerCAmelCase    		=					line.strip()
																		if line:
																								_lowerCAmelCase    		=					line.split()
																								_lowerCAmelCase    		=					line_number
																								_lowerCAmelCase    		=					words[0]
																								_lowerCAmelCase    		=					value
						return result


def A (__lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Any					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :List[Any]					,      __lowerCamelCase :List[str]						):
						for attribute in key.split("""."""						):
												_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      __lowerCamelCase						)

						_lowerCAmelCase    		=					None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(__lowerCamelCase						):
																		_lowerCAmelCase    		=					PARAM_MAPPING[full_name.split("""."""						)[-1]]
																		_lowerCAmelCase    		=					"""param"""

						if weight_type is not None and weight_type != "param":
												_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      __lowerCamelCase						).shape
						elif weight_type is not None and weight_type == "param":
												_lowerCAmelCase    		=					hf_pointer
												for attribute in hf_param_name.split("""."""						):
																		_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      __lowerCamelCase						)
												_lowerCAmelCase    		=					shape_pointer.shape

												# let's reduce dimension
												_lowerCAmelCase    		=					value[0]
						else:
												_lowerCAmelCase    		=					hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    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
						elif weight_type == "param":
												for attribute in hf_param_name.split("""."""						):
																		_lowerCAmelCase    		=					getattr(__lowerCamelCase					,      __lowerCamelCase						)
												_lowerCAmelCase    		=					value
						else:
												_lowerCAmelCase    		=					value

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


def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :Dict					,      __lowerCamelCase :List[Any]					,      __lowerCamelCase :int						):
						_lowerCAmelCase    		=					None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(__lowerCamelCase						):
																		_lowerCAmelCase    		=					PARAM_MAPPING[full_name.split("""."""						)[-1]]
																		_lowerCAmelCase    		=					"""param"""

						if weight_type is not None and weight_type != "param":
												_lowerCAmelCase    		=					""".""".join([key, weight_type]						)
						elif weight_type is not None and weight_type == "param":
												_lowerCAmelCase    		=					""".""".join([key, hf_param_name]						)
						else:
												_lowerCAmelCase    		=					key

						_lowerCAmelCase    		=					value if """lm_head""" in full_key else value[0]


_lowercase          =						{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}


def A (__lowerCamelCase :Any					,      __lowerCamelCase :int					,      __lowerCamelCase :List[str]=None					,      __lowerCamelCase :List[Any]=None						):
						_lowerCAmelCase    		=					False
						for key, mapped_key in MAPPING.items():
												_lowerCAmelCase    		=					"""wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
												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:
																								# TODO: don't match quantizer.weight_proj
																								_lowerCAmelCase    		=					"""weight"""
																		else:
																								_lowerCAmelCase    		=					None
																		if hf_dict is not None:
																								rename_dict(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		else:
																								set_recursively(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		return is_used
						return is_used


def A (__lowerCamelCase :Any					,      __lowerCamelCase :Dict					,      __lowerCamelCase :Dict						):
						_lowerCAmelCase    		=					[]
						_lowerCAmelCase    		=					fairseq_model.state_dict()

						_lowerCAmelCase    		=					hf_model.wavaveca.feature_extractor

						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
												else:
																		_lowerCAmelCase    		=					load_wavaveca_layer(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
												if not is_used:
																		unused_weights.append(__lowerCamelCase						)

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


def A (__lowerCamelCase :Tuple					,      __lowerCamelCase :Optional[int]					,      __lowerCamelCase :Any					,      __lowerCamelCase :List[Any]					,      __lowerCamelCase :List[Any]						):
						_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:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    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:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    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:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    f'{full_name} has size {value.shape}, but'
																								    f' {feature_extractor.conv_layers[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:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    f'{full_name} has size {value.shape}, but'
																								    f' {feature_extractor.conv_layers[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						)


@torch.no_grad()
def A (__lowerCamelCase :List[str]					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :List[Any]=None					,      __lowerCamelCase :Union[str, Any]=None					,      __lowerCamelCase :str=True					,      __lowerCamelCase :str=False						):
						if config_path is not None:
												_lowerCAmelCase    		=					WavaVecaConfig.from_pretrained(__lowerCamelCase						)
						else:
												_lowerCAmelCase    		=					WavaVecaConfig()

						if is_seq_class:
												_lowerCAmelCase    		=					read_txt_into_dict(__lowerCamelCase						)
												_lowerCAmelCase    		=					idalabel
												_lowerCAmelCase    		=					WavaVecaForSequenceClassification(__lowerCamelCase						)
												_lowerCAmelCase    		=					WavaVecaFeatureExtractor(
												    feature_size=1					,      sampling_rate=16000					,      padding_value=0					,      do_normalize=__lowerCamelCase					,      return_attention_mask=__lowerCamelCase					,      )
												feature_extractor.save_pretrained(__lowerCamelCase						)

						elif is_finetuned:
												if dict_path:
																		_lowerCAmelCase    		=					Dictionary.load(__lowerCamelCase						)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		_lowerCAmelCase    		=					target_dict.pad_index
																		_lowerCAmelCase    		=					target_dict.bos_index
																		_lowerCAmelCase    		=					target_dict.eos_index
																		_lowerCAmelCase    		=					len(target_dict.symbols						)
																		_lowerCAmelCase    		=					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						)
																		_lowerCAmelCase    		=					target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		_lowerCAmelCase    		=					0
																		_lowerCAmelCase    		=					1
																		with open(__lowerCamelCase					,      """w"""					,      encoding="""utf-8"""						) as vocab_handle:
																								json.dump(__lowerCamelCase					,      __lowerCamelCase						)
																		_lowerCAmelCase    		=					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					,      )
																		_lowerCAmelCase    		=					True if config.feat_extract_norm == """layer""" else False
																		_lowerCAmelCase    		=					WavaVecaFeatureExtractor(
																		    feature_size=1					,      sampling_rate=16000					,      padding_value=0					,      do_normalize=__lowerCamelCase					,      return_attention_mask=__lowerCamelCase					,      )
																		_lowerCAmelCase    		=					WavaVecaProcessor(feature_extractor=__lowerCamelCase					,      tokenizer=__lowerCamelCase						)
																		processor.save_pretrained(__lowerCamelCase						)

												_lowerCAmelCase    		=					WavaVecaForCTC(__lowerCamelCase						)
						else:
												_lowerCAmelCase    		=					WavaVecaForPreTraining(__lowerCamelCase						)

						if is_finetuned or is_seq_class:
												_lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase    		=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]					,      arg_overrides={"""data""": """/""".join(dict_path.split("""/"""						)[:-1]						)}						)
						else:
												_lowerCAmelCase    		=					argparse.Namespace(task="""audio_pretraining"""						)
												_lowerCAmelCase    		=					fairseq.tasks.setup_task(__lowerCamelCase						)

												_lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase    		=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,      task=__lowerCamelCase						)

						_lowerCAmelCase    		=					model[0].eval()

						recursively_load_weights(__lowerCamelCase					,      __lowerCamelCase					,      not is_finetuned						)

						hf_wavavec.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
						_lowercase          =						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"""
						)
						parser.add_argument(
						    """--is_seq_class""",
						    action="""store_true""",
						    help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
						)
						_lowercase          =						parser.parse_args()

						_lowercase          =						not args.not_finetuned and not args.is_seq_class
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.dict_path,
						    is_finetuned,
						    args.is_seq_class,
						)
