




import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule




class       _A				(		__lowercase     ):
		__a					    =       42
		__a					    =       jnp.floataa
		__a					    =       True






		def 				UpperCAmelCase (    self			):
								super().setup()
								_UpperCAmelCase      =			nn.Dense(5						,		dtype=self.dtype			)






		def __call__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			super().__call__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.cls(outputs[2]			)
								return outputs[:2] + (cls_out,)




class       _A				(		__lowercase     ):
		__a					    =       FlaxBigBirdForNaturalQuestionsModule




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						def cross_entropy(snake_case			,     snake_case			,     snake_case=None       ):
												_UpperCAmelCase      =			logits.shape[-1]
												_UpperCAmelCase      =			(labels[..., None] == jnp.arange(snake_case       )[None]).astype("""f4"""       )
												_UpperCAmelCase      =			jax.nn.log_softmax(snake_case			,     axis=-1       )
												_UpperCAmelCase      =			-jnp.sum(labels * logits			,     axis=-1       )
												if reduction is not None:
																		_UpperCAmelCase      =			reduction(snake_case       )
												return loss

						_UpperCAmelCase      =			partial(snake_case			,     reduction=jnp.mean       )
						_UpperCAmelCase      =			cross_entropy(snake_case			,     snake_case       )
						_UpperCAmelCase      =			cross_entropy(snake_case			,     snake_case       )
						_UpperCAmelCase      =			cross_entropy(snake_case			,     snake_case       )
						return (start_loss + end_loss + pooled_loss) / 3




@dataclass
class       _A				:
		__a					    =       "google/bigbird-roberta-base"
		__a					    =       30_00
		__a					    =       1_05_00

		__a					    =       1_28
		__a					    =       3

		__a					    =       1
		__a					    =       5

		# tx_args
		__a					    =       3e-5
		__a					    =       0.0
		__a					    =       2_00_00
		__a					    =       0.0095

		__a					    =       "bigbird-roberta-natural-questions"
		__a					    =       "training-expt"
		__a					    =       "data/nq-training.jsonl"
		__a					    =       "data/nq-validation.jsonl"






		def 				UpperCAmelCase (    self			):
								os.makedirs(self.base_dir						,		exist_ok=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			os.path.join(self.base_dir						,		self.save_dir			)
								_UpperCAmelCase      =			self.batch_size_per_device * jax.device_count()




@dataclass
class       _A				:
		__a					    =       42
		__a					    =       40_96  # no dynamic padding on TPUs






		def __call__(    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.collate_fn(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			jax.tree_util.tree_map(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								return batch






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.fetch_inputs(features["""input_ids"""]			)
								_UpperCAmelCase      =			{
								    """input_ids""": jnp.array(_SCREAMING_SNAKE_CASE						,		dtype=jnp.intaa			),
								    """attention_mask""": jnp.array(_SCREAMING_SNAKE_CASE						,		dtype=jnp.intaa			),
								    """start_labels""": jnp.array(features["""start_token"""]						,		dtype=jnp.intaa			),
								    """end_labels""": jnp.array(features["""end_token"""]						,		dtype=jnp.intaa			),
								    """pooled_labels""": jnp.array(features["""category"""]						,		dtype=jnp.intaa			),
								}
								return batch






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			[self._fetch_inputs(_SCREAMING_SNAKE_CASE			) for ids in input_ids]
								return zip(*_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			[1 for _ in range(len(_SCREAMING_SNAKE_CASE			)			)]
								while len(_SCREAMING_SNAKE_CASE			) < self.max_length:
														input_ids.append(self.pad_id			)
														attention_mask.append(0			)
								return input_ids, attention_mask




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case=None       )						->				Dict:
						if seed is not None:
												_UpperCAmelCase      =			dataset.shuffle(seed=snake_case       )
						for i in range(len(snake_case       ) // batch_size       ):
												_UpperCAmelCase      =			dataset[i * batch_size : (i + 1) * batch_size]
												yield dict(snake_case       )




@partial(jax.pmap			,     axis_name="""batch"""       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     **snake_case       )						->				Any:
						def loss_fn(snake_case       ):
												_UpperCAmelCase      =			model_inputs.pop("""start_labels"""       )
												_UpperCAmelCase      =			model_inputs.pop("""end_labels"""       )
												_UpperCAmelCase      =			model_inputs.pop("""pooled_labels"""       )

												_UpperCAmelCase      =			state.apply_fn(**snake_case			,     params=snake_case			,     dropout_rng=snake_case			,     train=snake_case       )
												_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			outputs

												return state.loss_fn(
												    snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case			,     )

						_UpperCAmelCase    ,					_UpperCAmelCase      =			jax.random.split(snake_case       )
						_UpperCAmelCase      =			jax.value_and_grad(snake_case       )
						_UpperCAmelCase    ,					_UpperCAmelCase      =			grad_fn(state.params       )
						_UpperCAmelCase      =			jax.lax.pmean({"""loss""": loss}			,     axis_name="""batch"""       )
						_UpperCAmelCase      =			jax.lax.pmean(snake_case			,     """batch"""       )

						_UpperCAmelCase      =			state.apply_gradients(grads=snake_case       )
						return state, metrics, new_drp_rng




@partial(jax.pmap			,     axis_name="""batch"""       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     **snake_case       )						->				Tuple:
						_UpperCAmelCase      =			model_inputs.pop("""start_labels"""       )
						_UpperCAmelCase      =			model_inputs.pop("""end_labels"""       )
						_UpperCAmelCase      =			model_inputs.pop("""pooled_labels"""       )

						_UpperCAmelCase      =			state.apply_fn(**snake_case			,     params=state.params			,     train=snake_case       )
						_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			outputs

						_UpperCAmelCase      =			state.loss_fn(snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )
						_UpperCAmelCase      =			jax.lax.pmean({"""loss""": loss}			,     axis_name="""batch"""       )
						return metrics




class       _A				(		train_state.TrainState     ):
		__a					    =       struct.field(pytree_node=__lowercase     )




@dataclass
class       _A				:
		__a					    =       42
		__a					    =       42
		__a					    =       42
		__a					    =       42
		__a					    =       42
		__a					    =       42
		__a					    =       None






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								_UpperCAmelCase      =			model.params
								_UpperCAmelCase      =			TrainState.create(
								    apply_fn=model.__call__						,		params=_SCREAMING_SNAKE_CASE						,		tx=_SCREAMING_SNAKE_CASE						,		loss_fn=_SCREAMING_SNAKE_CASE						,		)
								if ckpt_dir is not None:
														_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			restore_checkpoint(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			{
														    """lr""": args.lr,
														    """init_lr""": args.init_lr,
														    """warmup_steps""": args.warmup_steps,
														    """num_train_steps""": num_train_steps,
														    """weight_decay""": args.weight_decay,
														}
														_UpperCAmelCase    ,					_UpperCAmelCase      =			build_tx(**_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			train_state.TrainState(
														    step=_SCREAMING_SNAKE_CASE						,		apply_fn=model.__call__						,		params=_SCREAMING_SNAKE_CASE						,		tx=_SCREAMING_SNAKE_CASE						,		opt_state=_SCREAMING_SNAKE_CASE						,		)
														_UpperCAmelCase      =			args
														_UpperCAmelCase      =			data_collator
														_UpperCAmelCase      =			lr
														_UpperCAmelCase      =			params
								_UpperCAmelCase      =			jax_utils.replicate(_SCREAMING_SNAKE_CASE			)
								return state






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.args
								_UpperCAmelCase      =			len(_SCREAMING_SNAKE_CASE			) // args.batch_size

								_UpperCAmelCase      =			jax.random.PRNGKey(0			)
								_UpperCAmelCase      =			jax.random.split(_SCREAMING_SNAKE_CASE						,		jax.device_count()			)
								for epoch in range(args.max_epochs			):
														_UpperCAmelCase      =			jnp.array(0						,		dtype=jnp.floataa			)
														_UpperCAmelCase      =			get_batched_dataset(_SCREAMING_SNAKE_CASE						,		args.batch_size						,		seed=_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			0
														for batch in tqdm(_SCREAMING_SNAKE_CASE						,		total=_SCREAMING_SNAKE_CASE						,		desc=F"Running EPOCH-{epoch}"			):
																				_UpperCAmelCase      =			self.data_collator(_SCREAMING_SNAKE_CASE			)
																				_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			self.train_step_fn(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
																				running_loss += jax_utils.unreplicate(metrics["""loss"""]			)
																				i += 1
																				if i % args.logging_steps == 0:
																										_UpperCAmelCase      =			jax_utils.unreplicate(state.step			)
																										_UpperCAmelCase      =			running_loss.item() / i
																										_UpperCAmelCase      =			self.scheduler_fn(state_step - 1			)

																										_UpperCAmelCase      =			self.evaluate(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
																										_UpperCAmelCase      =			{
																										    """step""": state_step.item(),
																										    """eval_loss""": eval_loss.item(),
																										    """tr_loss""": tr_loss,
																										    """lr""": lr.item(),
																										}
																										tqdm.write(str(_SCREAMING_SNAKE_CASE			)			)
																										self.logger.log(_SCREAMING_SNAKE_CASE						,		commit=_SCREAMING_SNAKE_CASE			)

																				if i % args.save_steps == 0:
																										self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}"						,		state=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			get_batched_dataset(_SCREAMING_SNAKE_CASE						,		self.args.batch_size			)
								_UpperCAmelCase      =			len(_SCREAMING_SNAKE_CASE			) // self.args.batch_size
								_UpperCAmelCase      =			jnp.array(0						,		dtype=jnp.floataa			)
								_UpperCAmelCase      =			0
								for batch in tqdm(_SCREAMING_SNAKE_CASE						,		total=_SCREAMING_SNAKE_CASE						,		desc="""Evaluating ... """			):
														_UpperCAmelCase      =			self.data_collator(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			self.val_step_fn(_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
														running_loss += jax_utils.unreplicate(metrics["""loss"""]			)
														i += 1
								return running_loss / i






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			jax_utils.unreplicate(_SCREAMING_SNAKE_CASE			)
								print(F"SAVING CHECKPOINT IN {save_dir}"						,		end=""" ... """			)
								self.model_save_fn(_SCREAMING_SNAKE_CASE						,		params=state.params			)
								with open(os.path.join(_SCREAMING_SNAKE_CASE						,		"""opt_state.msgpack"""			)						,		"""wb"""			) as f:
														f.write(to_bytes(state.opt_state			)			)
								joblib.dump(self.args						,		os.path.join(_SCREAMING_SNAKE_CASE						,		"""args.joblib"""			)			)
								joblib.dump(self.data_collator						,		os.path.join(_SCREAMING_SNAKE_CASE						,		"""data_collator.joblib"""			)			)
								with open(os.path.join(_SCREAMING_SNAKE_CASE						,		"""training_state.json"""			)						,		"""w"""			) as f:
														json.dump({"""step""": state.step.item()}						,		_SCREAMING_SNAKE_CASE			)
								print("""DONE"""			)




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Dict:
						print(f"RESTORING CHECKPOINT FROM {save_dir}"			,     end=""" ... """       )
						with open(os.path.join(snake_case			,     """flax_model.msgpack"""       )			,     """rb"""       ) as f:
												_UpperCAmelCase      =			from_bytes(state.params			,     f.read()       )

						with open(os.path.join(snake_case			,     """opt_state.msgpack"""       )			,     """rb"""       ) as f:
												_UpperCAmelCase      =			from_bytes(state.opt_state			,     f.read()       )

						_UpperCAmelCase      =			joblib.load(os.path.join(snake_case			,     """args.joblib"""       )       )
						_UpperCAmelCase      =			joblib.load(os.path.join(snake_case			,     """data_collator.joblib"""       )       )

						with open(os.path.join(snake_case			,     """training_state.json"""       )			,     """r"""       ) as f:
												_UpperCAmelCase      =			json.load(snake_case       )
						_UpperCAmelCase      =			training_state["""step"""]

						print("""DONE"""       )
						return params, opt_state, step, args, data_collator




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						_UpperCAmelCase      =			num_train_steps - warmup_steps
						_UpperCAmelCase      =			optax.linear_schedule(init_value=snake_case			,     end_value=snake_case			,     transition_steps=snake_case       )
						_UpperCAmelCase      =			optax.linear_schedule(init_value=snake_case			,     end_value=1E-7			,     transition_steps=snake_case       )
						_UpperCAmelCase      =			optax.join_schedules(schedules=[warmup_fn, decay_fn]			,     boundaries=[warmup_steps]       )
						return lr




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						def weight_decay_mask(snake_case       ):
												_UpperCAmelCase      =			traverse_util.flatten_dict(snake_case       )
												_UpperCAmelCase      =			{k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
												return traverse_util.unflatten_dict(snake_case       )

						_UpperCAmelCase      =			scheduler_fn(snake_case			,     snake_case			,     snake_case			,     snake_case       )

						_UpperCAmelCase      =			optax.adamw(learning_rate=snake_case			,     weight_decay=snake_case			,     mask=snake_case       )
						return tx, lr





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, 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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
a													=			[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				List[str]:
						for attribute in key.split("""."""       ):
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       )

						if weight_type is not None:
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       ).shape
						else:
												_UpperCAmelCase      =			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":
												_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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Optional[int]:
						_UpperCAmelCase      =			[]
						_UpperCAmelCase      =			fairseq_model.state_dict()

						_UpperCAmelCase      =			hf_model.unispeech_sat.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCAmelCase      =			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case			,     snake_case			,     snake_case			,     snake_case			,     hf_model.config.feat_extract_norm == """group"""			,     )
																		_UpperCAmelCase      =			True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCAmelCase      =			"""unispeech_sat.""" + 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]:
																														if "layer_norm_for_extract" in name and (".".join(name.split("""."""       )[:-1]       ) != key):
																																				# special case since naming is very similar
																																				continue
																														_UpperCAmelCase      =			True
																														if "*" in mapped_key:
																																				_UpperCAmelCase      =			name.split(snake_case       )[0].split("""."""       )[-2]
																																				_UpperCAmelCase      =			mapped_key.replace("""*"""			,     snake_case       )
																														if "weight_g" in name:
																																				_UpperCAmelCase      =			"""weight_g"""
																														elif "weight_v" in name:
																																				_UpperCAmelCase      =			"""weight_v"""
																														elif "bias" in name:
																																				_UpperCAmelCase      =			"""bias"""
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				_UpperCAmelCase      =			"""weight"""
																														else:
																																				_UpperCAmelCase      =			None
																														set_recursively(snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )
																								continue
												if not is_used:
																		unused_weights.append(snake_case       )

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				str:
						_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:
																		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."       )
																		_UpperCAmelCase      =			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."       )
																		_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:
																		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[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:
																		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[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(snake_case       )




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case=None			,     snake_case=None			,     snake_case=True       )						->				List[Any]:
						if config_path is not None:
												_UpperCAmelCase      =			UniSpeechSatConfig.from_pretrained(snake_case       )
						else:
												_UpperCAmelCase      =			UniSpeechSatConfig()

						_UpperCAmelCase      =			""""""

						if is_finetuned:
												_UpperCAmelCase      =			UniSpeechSatForCTC(snake_case       )
						else:
												_UpperCAmelCase      =			UniSpeechSatForPreTraining(snake_case       )

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

						recursively_load_weights(snake_case			,     snake_case       )

						hf_wavavec.save_pretrained(snake_case       )


if __name__ == "__main__":
				a													=			argparse.ArgumentParser()
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
				parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				parser.add_argument(
				    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
				)
				a													=			parser.parse_args()
				convert_unispeech_sat_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)