import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
A     :  Any					     =       logging.get_logger(__name__)


A     :  Union[str, Any]					     =       "https://openaipublic.azureedge.net/jukebox/models/"
A     :  Any					     =       {
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}


def      a__ (		__UpperCamelCase				):
      if key.endswith(".model.1.bias"				) and len(key.split("."				)				) > 1_0:
            SCREAMING_SNAKE_CASE_          =  key.replace(".model.1.bias"     ,      ".conv1d_1.bias"				)
      elif key.endswith(".model.1.weight"				) and len(key.split("."				)				) > 1_0:
            SCREAMING_SNAKE_CASE_          =  key.replace(".model.1.weight"     ,      ".conv1d_1.weight"				)
      elif key.endswith(".model.3.bias"				) and len(key.split("."				)				) > 1_0:
            SCREAMING_SNAKE_CASE_          =  key.replace(".model.3.bias"     ,      ".conv1d_2.bias"				)
      elif key.endswith(".model.3.weight"				) and len(key.split("."				)				) > 1_0:
            SCREAMING_SNAKE_CASE_          =  key.replace(".model.3.weight"     ,      ".conv1d_2.weight"				)

      if "conditioner_blocks.0." in key:
            SCREAMING_SNAKE_CASE_          =  key.replace("conditioner_blocks.0"     ,      "conditioner_blocks"				)

      if "prime_prior" in key:
            SCREAMING_SNAKE_CASE_          =  key.replace("prime_prior"     ,      "encoder"				)

      if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
            SCREAMING_SNAKE_CASE_          =  key.replace(".emb."     ,      "."				)

      if key.endswith("k"				):  # replace vqvae.X.k with vqvae.X.codebook
            return key.replace(".k"     ,      ".codebook"				)
      if "y_emb." in key:
            return key.replace("y_emb."     ,      "metadata_embedding."				)

      if "x_emb.emb." in key:
            SCREAMING_SNAKE_CASE_          =  key.replace("0.x_emb.emb"     ,      "embed_tokens"				)

      if "prime_state_ln" in key:
            return key.replace("prime_state_ln"     ,      "encoder.final_layer_norm"				)
      if ".ln" in key:
            return key.replace(".ln"     ,      ".layer_norm"				)
      if "_ln" in key:
            return key.replace("_ln"     ,      "_layer_norm"				)

      if "prime_state_proj" in key:
            return key.replace("prime_state_proj"     ,      "encoder.proj_in"				)
      if "prime_x_out" in key:
            return key.replace("prime_x_out"     ,      "encoder.lm_head"				)
      if "prior.x_out" in key:
            return key.replace("x_out"     ,      "fc_proj_out"				)
      if "x_emb" in key:
            return key.replace("x_emb"     ,      "embed_tokens"				)

      return key


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  {}
      import re

      SCREAMING_SNAKE_CASE_          =  re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"				)
      SCREAMING_SNAKE_CASE_          =  re.compile(
          r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"				)
      SCREAMING_SNAKE_CASE_          =  re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"				)

      SCREAMING_SNAKE_CASE_          =  re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"				)
      SCREAMING_SNAKE_CASE_          =  re.compile(
          r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"				)
      SCREAMING_SNAKE_CASE_          =  re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"				)

      SCREAMING_SNAKE_CASE_          =  re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"				)
      SCREAMING_SNAKE_CASE_          =  re.compile(
          r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"				)
      SCREAMING_SNAKE_CASE_          =  re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"				)

      for original_key, value in state_dict.items():
            # rename vqvae.encoder keys
            if re_encoder_block_conv_in.fullmatch(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  re_encoder_block_conv_in.match(__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  regex_match.groups()
                  SCREAMING_SNAKE_CASE_          =  int(groups[2]				) * 2 + int(groups[3]				)
                  SCREAMING_SNAKE_CASE_          =  F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
                  SCREAMING_SNAKE_CASE_          =  re_encoder_block_conv_in.sub(__UpperCamelCase     ,      __UpperCamelCase				)

            elif re_encoder_block_resnet.fullmatch(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  re_encoder_block_resnet.match(__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  regex_match.groups()
                  SCREAMING_SNAKE_CASE_          =  int(groups[2]				) * 2 + int(groups[3]				)
                  SCREAMING_SNAKE_CASE_          =  {"1": 1, "3": 2}[groups[-2]]
                  SCREAMING_SNAKE_CASE_          =  F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
                  SCREAMING_SNAKE_CASE_          =  F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
                  SCREAMING_SNAKE_CASE_          =  prefix + resnet_block
                  SCREAMING_SNAKE_CASE_          =  re_encoder_block_resnet.sub(__UpperCamelCase     ,      __UpperCamelCase				)

            elif re_encoder_block_proj_out.fullmatch(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  re_encoder_block_proj_out.match(__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  regex_match.groups()
                  SCREAMING_SNAKE_CASE_          =  F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
                  SCREAMING_SNAKE_CASE_          =  re_encoder_block_proj_out.sub(__UpperCamelCase     ,      __UpperCamelCase				)

            # rename vqvae.decoder keys
            elif re_decoder_block_conv_out.fullmatch(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  re_decoder_block_conv_out.match(__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  regex_match.groups()
                  SCREAMING_SNAKE_CASE_          =  int(groups[2]				) * 2 + int(groups[3]				) - 2
                  SCREAMING_SNAKE_CASE_          =  F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
                  SCREAMING_SNAKE_CASE_          =  re_decoder_block_conv_out.sub(__UpperCamelCase     ,      __UpperCamelCase				)

            elif re_decoder_block_resnet.fullmatch(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  re_decoder_block_resnet.match(__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  regex_match.groups()
                  SCREAMING_SNAKE_CASE_          =  int(groups[2]				) * 2 + int(groups[3]				) - 2
                  SCREAMING_SNAKE_CASE_          =  {"1": 1, "3": 2}[groups[-2]]
                  SCREAMING_SNAKE_CASE_          =  F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
                  SCREAMING_SNAKE_CASE_          =  F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
                  SCREAMING_SNAKE_CASE_          =  prefix + resnet_block
                  SCREAMING_SNAKE_CASE_          =  re_decoder_block_resnet.sub(__UpperCamelCase     ,      __UpperCamelCase				)

            elif re_decoder_block_proj_in.fullmatch(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  re_decoder_block_proj_in.match(__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  regex_match.groups()
                  SCREAMING_SNAKE_CASE_          =  F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
                  SCREAMING_SNAKE_CASE_          =  re_decoder_block_proj_in.sub(__UpperCamelCase     ,      __UpperCamelCase				)

            # rename prior cond.model to upsampler.upsample_block and resnet
            elif re_prior_cond_conv_out.fullmatch(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  re_prior_cond_conv_out.match(__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  regex_match.groups()
                  SCREAMING_SNAKE_CASE_          =  int(groups[1]				) * 2 + int(groups[2]				) - 2
                  SCREAMING_SNAKE_CASE_          =  F'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
                  SCREAMING_SNAKE_CASE_          =  re_prior_cond_conv_out.sub(__UpperCamelCase     ,      __UpperCamelCase				)

            elif re_prior_cond_resnet.fullmatch(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  re_prior_cond_resnet.match(__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  regex_match.groups()
                  SCREAMING_SNAKE_CASE_          =  int(groups[1]				) * 2 + int(groups[2]				) - 2
                  SCREAMING_SNAKE_CASE_          =  {"1": 1, "3": 2}[groups[-2]]
                  SCREAMING_SNAKE_CASE_          =  F'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
                  SCREAMING_SNAKE_CASE_          =  F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
                  SCREAMING_SNAKE_CASE_          =  prefix + resnet_block
                  SCREAMING_SNAKE_CASE_          =  re_prior_cond_resnet.sub(__UpperCamelCase     ,      __UpperCamelCase				)

            elif re_prior_cond_proj_in.fullmatch(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  re_prior_cond_proj_in.match(__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  regex_match.groups()
                  SCREAMING_SNAKE_CASE_          =  F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
                  SCREAMING_SNAKE_CASE_          =  re_prior_cond_proj_in.sub(__UpperCamelCase     ,      __UpperCamelCase				)

            # keep original key
            else:
                  SCREAMING_SNAKE_CASE_          =  original_key

            SCREAMING_SNAKE_CASE_          =  replace_key(__UpperCamelCase				)

            if F'''{key_prefix}.{key}''' not in model_state_dict or key is None:
                  print(F'''failed converting {original_key} to {key}, does not match'''				)

            # handle missmatched shape
            elif value.shape != model_state_dict[F'''{key_prefix}.{key}'''].shape:
                  SCREAMING_SNAKE_CASE_          =  model_state_dict[F'''{key_prefix}.{key}''']
                  print(F'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''				)
                  SCREAMING_SNAKE_CASE_          =  original_key

            SCREAMING_SNAKE_CASE_          =  original_key
            SCREAMING_SNAKE_CASE_          =  value

      return new_dict


@torch.no_grad()
def      a__ (		__UpperCamelCase=None     ,      __UpperCamelCase=None				):
      for file in MODEL_MAPPING[model_name]:
            if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split("/"				)[-1]}'''				):
                  SCREAMING_SNAKE_CASE_          =  requests.get(F'''{PREFIX}{file}'''     ,      allow_redirects=__UpperCamelCase				)
                  os.makedirs(F'''{pytorch_dump_folder_path}/'''     ,      exist_ok=__UpperCamelCase				)
                  open(F'''{pytorch_dump_folder_path}/{file.split("/"				)[-1]}'''     ,      "wb"				).write(r.content				)

      SCREAMING_SNAKE_CASE_          =  MODEL_MAPPING[model_name.split("/"				)[-1]]

      SCREAMING_SNAKE_CASE_          =  JukeboxConfig.from_pretrained(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  JukeboxModel(__UpperCamelCase				)

      SCREAMING_SNAKE_CASE_          =  []
      SCREAMING_SNAKE_CASE_          =  {}
      for i, dict_name in enumerate(__UpperCamelCase				):
            SCREAMING_SNAKE_CASE_          =  torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split("/"				)[-1]}'''				)["model"]

            SCREAMING_SNAKE_CASE_          =  {}
            for k in old_dic.keys():
                  if k.endswith(".b"				):
                        SCREAMING_SNAKE_CASE_          =  old_dic[k]
                  elif k.endswith(".w"				):
                        SCREAMING_SNAKE_CASE_          =  old_dic[k]
                  elif "level_2" not in dict_name and "cond.model." in k:
                        SCREAMING_SNAKE_CASE_          =  old_dic[k]
                  else:
                        SCREAMING_SNAKE_CASE_          =  old_dic[k]

            SCREAMING_SNAKE_CASE_          =  "vqvae" if i == 0 else F'''priors.{3 - i}'''
            SCREAMING_SNAKE_CASE_          =  fix_jukebox_keys(__UpperCamelCase     ,      model.state_dict()     ,      __UpperCamelCase     ,      __UpperCamelCase				)
            weight_dict.append(__UpperCamelCase				)

      SCREAMING_SNAKE_CASE_          =  weight_dict.pop(0				)
      model.vqvae.load_state_dict(__UpperCamelCase				)
      for i in range(len(__UpperCamelCase				)				):
            model.priors[i].load_state_dict(weight_dict[2 - i]				)

      Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
      with open(F'''{pytorch_dump_folder_path}/mapping.json'''     ,      "w"				) as txtfile:
            json.dump(__UpperCamelCase     ,      __UpperCamelCase				)

      print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
      model.save_pretrained(__UpperCamelCase				)

      return weight_dict


if __name__ == "__main__":
      A     :  str					     =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default="jukebox-5b-lyrics",
          type=str,
          help="Name of the model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default="jukebox-5b-lyrics-converted",
          type=str,
          help="Path to the output PyTorch model directory.",
      )
      A     :  Optional[int]					     =       parser.parse_args()
      convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



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     :  Optional[Any]					     =       16
A     :  Optional[Any]					     =       32


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase = 1_6				):
      SCREAMING_SNAKE_CASE_          =  AutoTokenizer.from_pretrained("bert-base-cased"				)
      SCREAMING_SNAKE_CASE_          =  load_dataset("glue"     ,      "mrpc"				)

      def tokenize_function(__UpperCamelCase				):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE_          =  tokenizer(examples["sentence1"]     ,      examples["sentence2"]     ,      truncation=__UpperCamelCase     ,      max_length=__UpperCamelCase				)
            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(
                __UpperCamelCase     ,      batched=__UpperCamelCase     ,      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(__UpperCamelCase				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE_          =  1_2_8 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_          =  1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE_          =  8
            else:
                  SCREAMING_SNAKE_CASE_          =  None

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

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

      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     :  Tuple					     =       mocked_dataloaders  # noqa: F811


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      # For testing only
      if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,      __UpperCamelCase				) == "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=__UpperCamelCase				)
      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(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  get_dataloaders(__UpperCamelCase     ,      __UpperCamelCase				)
      # 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=__UpperCamelCase				)

      # 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=__UpperCamelCase				)

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE_          =  get_linear_schedule_with_warmup(
          optimizer=__UpperCamelCase     ,      num_warmup_steps=1_0_0     ,      num_training_steps=(len(__UpperCamelCase				) * 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_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  accelerator.prepare(
          __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)

      # Now we train the model
      for epoch in range(__UpperCamelCase				):
            model.train()
            for step, batch in enumerate(__UpperCamelCase				):
                  # 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(__UpperCamelCase				):
                        SCREAMING_SNAKE_CASE_          =  model(**__UpperCamelCase				)
                        SCREAMING_SNAKE_CASE_          =  output.loss
                        accelerator.backward(__UpperCamelCase				)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(__UpperCamelCase				):
                  # 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(**__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  outputs.logits.argmax(dim=-1				)
                  SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  accelerator.gather_for_metrics((predictions, batch["labels"])				)
                  metric.add_batch(
                      predictions=__UpperCamelCase     ,      references=__UpperCamelCase     ,      )

            SCREAMING_SNAKE_CASE_          =  metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''     ,      __UpperCamelCase				)


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  argparse.ArgumentParser(description="Simple example of training script."				)
      parser.add_argument(
          "--mixed_precision"     ,      type=__UpperCamelCase     ,      default=__UpperCamelCase     ,      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=__UpperCamelCase     ,      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": 4_2, "batch_size": 1_6}
      training_function(__UpperCamelCase     ,      __UpperCamelCase				)


if __name__ == "__main__":
      main()


