


"""simple docstring"""







import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a__       :							Any        =  1_6
a__       :							List[Any]        =  3_2




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_ = 16     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       AutoTokenizer.from_pretrained("bert-base-cased"     )
					__SCREAMING_SNAKE_CASE		     =       load_dataset("glue"						,		"mrpc"     )

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

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										__SCREAMING_SNAKE_CASE		     =       datasets.map(
										    __SCREAMING_SNAKE_CASE						,		batched=__SCREAMING_SNAKE_CASE						,		remove_columns=["idx", "sentence1", "sentence2"]						,		)

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					__SCREAMING_SNAKE_CASE		     =       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.
										__SCREAMING_SNAKE_CASE		     =       128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															__SCREAMING_SNAKE_CASE		     =       16
										elif accelerator.mixed_precision != "no":
															__SCREAMING_SNAKE_CASE		     =       8
										else:
															__SCREAMING_SNAKE_CASE		     =       None

										return tokenizer.pad(
										    __SCREAMING_SNAKE_CASE						,		padding="longest"						,		max_length=__SCREAMING_SNAKE_CASE						,		pad_to_multiple_of=__SCREAMING_SNAKE_CASE						,		return_tensors="pt"						,		)

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

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		a__       :							Any        =  mocked_dataloaders  # noqa: F811




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					if os.environ.get("TESTING_MOCKED_DATALOADERS"						,		__SCREAMING_SNAKE_CASE     ) == "1":
										__SCREAMING_SNAKE_CASE		     =       2
					# New Code #
					__SCREAMING_SNAKE_CASE		     =       int(args.gradient_accumulation_steps     )
					# Initialize accelerator
					__SCREAMING_SNAKE_CASE		     =       Accelerator(
					    cpu=args.cpu						,		mixed_precision=args.mixed_precision						,		gradient_accumulation_steps=__SCREAMING_SNAKE_CASE     )
					if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
										raise NotImplementedError(
										    "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"     )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__SCREAMING_SNAKE_CASE		     =       config["lr"]
					__SCREAMING_SNAKE_CASE		     =       int(config["num_epochs"]     )
					__SCREAMING_SNAKE_CASE		     =       int(config["seed"]     )
					__SCREAMING_SNAKE_CASE		     =       int(config["batch_size"]     )

					__SCREAMING_SNAKE_CASE		     =       evaluate.load("glue"						,		"mrpc"     )

					set_seed(__SCREAMING_SNAKE_CASE     )
					__SCREAMING_SNAKE_CASE		     =       get_dataloaders(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE     )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__SCREAMING_SNAKE_CASE		     =       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"						,		return_dict=__SCREAMING_SNAKE_CASE     )

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					__SCREAMING_SNAKE_CASE		     =       model.to(accelerator.device     )

					# Instantiate optimizer
					__SCREAMING_SNAKE_CASE		     =       AdamW(params=model.parameters()						,		lr=__SCREAMING_SNAKE_CASE     )

					# Instantiate scheduler
					__SCREAMING_SNAKE_CASE		     =       get_linear_schedule_with_warmup(
					    optimizer=__SCREAMING_SNAKE_CASE						,		num_warmup_steps=100						,		num_training_steps=(len(__SCREAMING_SNAKE_CASE     ) * num_epochs)						,		)

					# 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.
					__SCREAMING_SNAKE_CASE		     =       accelerator.prepare(
					    __SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE     )

					# Now we train the model
					for epoch in range(__SCREAMING_SNAKE_CASE     ):
										model.train()
										for step, batch in enumerate(__SCREAMING_SNAKE_CASE     ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device     )
															# New code #
															# We use the new `accumulate` context manager to perform gradient accumulation
															# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
															with accelerator.accumulate(__SCREAMING_SNAKE_CASE     ):
																				__SCREAMING_SNAKE_CASE		     =       model(**__SCREAMING_SNAKE_CASE     )
																				__SCREAMING_SNAKE_CASE		     =       output.loss
																				accelerator.backward(__SCREAMING_SNAKE_CASE     )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

										model.eval()
										for step, batch in enumerate(__SCREAMING_SNAKE_CASE     ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device     )
															with torch.no_grad():
																				__SCREAMING_SNAKE_CASE		     =       model(**__SCREAMING_SNAKE_CASE     )
															__SCREAMING_SNAKE_CASE		     =       outputs.logits.argmax(dim=-1     )
															__SCREAMING_SNAKE_CASE		     =       accelerator.gather_for_metrics((predictions, batch["labels"])     )
															metric.add_batch(
															    predictions=__SCREAMING_SNAKE_CASE						,		references=__SCREAMING_SNAKE_CASE						,		)

										__SCREAMING_SNAKE_CASE		     =       metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f"""epoch {epoch}:"""						,		__SCREAMING_SNAKE_CASE     )




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       argparse.ArgumentParser(description="Simple example of training script."     )
					parser.add_argument(
					    "--mixed_precision"						,		type=__SCREAMING_SNAKE_CASE						,		default=__SCREAMING_SNAKE_CASE						,		choices=["no", "fp16", "bf16", "fp8"]						,		help="Whether to use mixed precision. Choose"
					    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
					    "and an Nvidia Ampere GPU."						,		)
					# New Code #
					parser.add_argument(
					    "--gradient_accumulation_steps"						,		type=__SCREAMING_SNAKE_CASE						,		default=1						,		help="The number of minibatches to be ran before gradients are accumulated."						,		)
					parser.add_argument("--cpu"						,		action="store_true"						,		help="If passed, will train on the CPU."     )
					__SCREAMING_SNAKE_CASE		     =       parser.parse_args()
					__SCREAMING_SNAKE_CASE		     =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
		main()







"""simple docstring"""







import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
a__       :							Union[str, Any]        =  logging.get_logger('''transformers.models.encodec''')

a__       :							Optional[Any]        =  {
    '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
    '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
    '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
    '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
a__       :							int        =  {
    '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
    '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
    '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
    '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
    '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
    '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
    '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
    '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
    '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
    '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
    '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
    '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
    '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
    '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
    '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
    '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
    '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
    '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
    '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
a__       :							List[Any]        =  {
    '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
    '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
    '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
    '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
    '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
    '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
    '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
    '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
    '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
    '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
    '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
    '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
    '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
    '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
    '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
    '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
    '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
    '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
a__       :							List[Any]        =  {
    '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
    '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
    '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
    '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
    '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
    '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
    '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
    '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
    '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
    '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
    '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
    '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
    '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
    '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
    '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
    '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
    '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
    '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
    '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
a__       :							Union[str, Any]        =  {
    '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
    '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
    '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
    '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
    '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
    '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
    '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
    '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
    '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
    '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
    '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
    '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
    '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
    '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
    '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
    '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
    '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
    '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
a__       :							List[Any]        =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a__       :							Any        =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a__       :							List[Any]        =  []
a__       :							str        =  []




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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

					if weight_type is not None:
										__SCREAMING_SNAKE_CASE		     =       getattr(lowerCAmelCase_						,		lowerCAmelCase_     ).shape
					else:
										__SCREAMING_SNAKE_CASE		     =       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":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_g":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_v":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "running_mean":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "running_var":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "num_batches_tracked":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_ih_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_hh_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_ih_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_hh_l0":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_ih_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "weight_hh_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_ih_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					elif weight_type == "bias_hh_l1":
										__SCREAMING_SNAKE_CASE		     =       value
					else:
										__SCREAMING_SNAKE_CASE		     =       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'''


					for key in ignore_keys:
										if key.endswith(".*"     ):
															if name.startswith(key[:-1]     ):
																				return True
										elif ".*." in key:
															__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       key.split(".*."     )
															if prefix in name and suffix in name:
																				return True
										elif key in name:
															return True
					return False




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       []

					if model_name == "encodec_24khz" or "encodec_32khz":
										__SCREAMING_SNAKE_CASE		     =       MAPPING_24K
					elif model_name == "encodec_48khz":
										__SCREAMING_SNAKE_CASE		     =       MAPPING_48K
					else:
										raise ValueError(f"""Unsupported model: {model_name}"""     )

					for name, value in orig_dict.items():
										if should_ignore(lowerCAmelCase_						,		lowerCAmelCase_     ):
															logger.info(f"""{name} was ignored"""     )
															continue

										__SCREAMING_SNAKE_CASE		     =       False
										for key, mapped_key in MAPPING.items():
															if "*" in key:
																				__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       key.split(".*."     )
																				if prefix in name and suffix in name:
																									__SCREAMING_SNAKE_CASE		     =       suffix

															if key in name:
																				# HACK otherwise .embed gets initialized with .embed_avg too
																				if key.endswith("embed"     ) and name.endswith("embed_avg"     ):
																									continue

																				__SCREAMING_SNAKE_CASE		     =       True
																				if "*" in mapped_key:
																									__SCREAMING_SNAKE_CASE		     =       name.split(lowerCAmelCase_     )[0].split("."     )[-2]
																									__SCREAMING_SNAKE_CASE		     =       mapped_key.replace("*"						,		lowerCAmelCase_     )
																				if "weight_g" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_g"
																				elif "weight_v" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_v"
																				elif "weight_ih_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_ih_l0"
																				elif "weight_hh_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_hh_l0"
																				elif "bias_ih_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_ih_l0"
																				elif "bias_hh_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_hh_l0"
																				elif "weight_ih_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_ih_l1"
																				elif "weight_hh_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_hh_l1"
																				elif "bias_ih_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_ih_l1"
																				elif "bias_hh_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_hh_l1"
																				elif "bias" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias"
																				elif "weight" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight"
																				elif "running_mean" in name:
																									__SCREAMING_SNAKE_CASE		     =       "running_mean"
																				elif "running_var" in name:
																									__SCREAMING_SNAKE_CASE		     =       "running_var"
																				elif "num_batches_tracked" in name:
																									__SCREAMING_SNAKE_CASE		     =       "num_batches_tracked"
																				else:
																									__SCREAMING_SNAKE_CASE		     =       None
																				set_recursively(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
															continue
										if not is_used:
															unused_weights.append(lowerCAmelCase_     )

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




@torch.no_grad()
def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=None						,		lowerCAmelCase_=None						,		):




					'''simple docstring'''


					if config_path is not None:
										__SCREAMING_SNAKE_CASE		     =       EncodecConfig.from_pretrained(lowerCAmelCase_     )
					else:
										__SCREAMING_SNAKE_CASE		     =       EncodecConfig()

					if model_name == "encodec_24khz":
										pass  # config is already correct
					elif model_name == "encodec_32khz":
										__SCREAMING_SNAKE_CASE		     =       [8, 5, 4, 4]
										__SCREAMING_SNAKE_CASE		     =       [2.2]
										__SCREAMING_SNAKE_CASE		     =       64
										__SCREAMING_SNAKE_CASE		     =       3_2000
										__SCREAMING_SNAKE_CASE		     =       2048
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       False
					elif model_name == "encodec_48khz":
										__SCREAMING_SNAKE_CASE		     =       [8, 5, 4, 2]
										__SCREAMING_SNAKE_CASE		     =       [3.0, 6.0, 12.0, 24.0]
										__SCREAMING_SNAKE_CASE		     =       4_8000
										__SCREAMING_SNAKE_CASE		     =       2
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       "time_group_norm"
										__SCREAMING_SNAKE_CASE		     =       True
										__SCREAMING_SNAKE_CASE		     =       1.0
										__SCREAMING_SNAKE_CASE		     =       0.01
					else:
										raise ValueError(f"""Unknown model name: {model_name}"""     )

					__SCREAMING_SNAKE_CASE		     =       EncodecModel(lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       EncodecFeatureExtractor(
					    feature_size=config.audio_channels						,		sampling_rate=config.sampling_rate						,		chunk_length_s=config.chunk_length_s						,		overlap=config.overlap						,		)
					feature_extractor.save_pretrained(lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       torch.load(lowerCAmelCase_     )
					if "best_state" in original_checkpoint:
										# we might have a training state saved, in which case discard the yaml results and just retain the weights
										__SCREAMING_SNAKE_CASE		     =       original_checkpoint["best_state"]
					recursively_load_weights(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
					model.save_pretrained(lowerCAmelCase_     )

					if repo_id:
										print("Pushing to the hub..."     )
										feature_extractor.push_to_hub(lowerCAmelCase_     )
										model.push_to_hub(lowerCAmelCase_     )


if __name__ == "__main__":
		a__       :							List[str]        =  argparse.ArgumentParser()
		parser.add_argument(
		    '''--model''',
		    default='''encodec_24khz''',
		    type=str,
		    help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
		)
		parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
		)

		a__       :							Optional[int]        =  parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)
