
"""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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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__      :	Dict          =    16
A__      :	Tuple          =    32



def 							a__ (				lowerCAmelCase	:       Accelerator       ,    lowerCAmelCase	:       int = 16		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						AutoTokenizer.from_pretrained("bert-base-cased"		)
     UpperCAmelCase__							: Optional[int] 							=						load_dataset("glue"       ,    "mrpc"		)

     def tokenize_function(lowerCAmelCase	:       Any		):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase__							: List[Any] 							=						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
     # starting with the main process first:
     with accelerator.main_process_first():
          UpperCAmelCase__							: List[Any] 							=						datasets.map(
              lowerCamelCase_       ,    batched=lowerCamelCase_       ,    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
     UpperCAmelCase__							: Union[str, Any] 							=						tokenized_datasets.rename_column("label"       ,    "labels"		)

     def collate_fn(lowerCAmelCase	:       Dict		):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          UpperCAmelCase__							: Optional[Any] 							=						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":
               UpperCAmelCase__							: List[Any] 							=						16
          elif accelerator.mixed_precision != "no":
               UpperCAmelCase__							: Tuple 							=						8
          else:
               UpperCAmelCase__							: Any 							=						None

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

     # Instantiate dataloaders.
     UpperCAmelCase__							: List[Any] 							=						DataLoader(
         tokenized_datasets["train"]       ,    shuffle=lowerCamelCase_       ,    collate_fn=lowerCamelCase_       ,    batch_size=lowerCamelCase_		)
     UpperCAmelCase__							: Dict 							=						DataLoader(
         tokenized_datasets["validation"]       ,    shuffle=lowerCamelCase_       ,    collate_fn=lowerCamelCase_       ,    batch_size=lowerCamelCase_		)

     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__      :	int          =    mocked_dataloaders  # noqa: F811



def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''
     # For testing only
     if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,    lowerCamelCase_		) == "1":
          UpperCAmelCase__							: List[str] 							=						2
     # Initialize accelerator
     UpperCAmelCase__							: Any 							=						Accelerator(cpu=args.cpu       ,    mixed_precision=args.mixed_precision		)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCAmelCase__							: Union[str, Any] 							=						config["lr"]
     UpperCAmelCase__							: Optional[int] 							=						int(config["num_epochs"]		)
     UpperCAmelCase__							: Optional[int] 							=						int(config["seed"]		)
     UpperCAmelCase__							: Optional[Any] 							=						int(config["batch_size"]		)

     UpperCAmelCase__							: Any 							=						evaluate.load("glue"       ,    "mrpc"		)

     # If the batch size is too big we use gradient accumulation
     UpperCAmelCase__							: Optional[Any] 							=						1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          UpperCAmelCase__							: Union[str, Any] 							=						batch_size // MAX_GPU_BATCH_SIZE
          UpperCAmelCase__							: Dict 							=						MAX_GPU_BATCH_SIZE

     set_seed(lowerCamelCase_		)
     UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						get_dataloaders(lowerCamelCase_       ,    lowerCamelCase_		)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCAmelCase__							: List[str] 							=						AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,    return_dict=lowerCamelCase_		)

     # 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).
     UpperCAmelCase__							: Any 							=						model.to(accelerator.device		)

     # Instantiate optimizer
     UpperCAmelCase__							: Tuple 							=						AdamW(params=model.parameters()       ,    lr=lowerCamelCase_		)

     # Instantiate scheduler
     UpperCAmelCase__							: List[Any] 							=						get_linear_schedule_with_warmup(
         optimizer=lowerCamelCase_       ,    num_warmup_steps=100       ,    num_training_steps=(len(lowerCamelCase_		) * num_epochs) // gradient_accumulation_steps       ,    )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						accelerator.prepare(
         lowerCamelCase_       ,    lowerCamelCase_       ,    lowerCamelCase_       ,    lowerCamelCase_       ,    lowerCamelCase_		)

     # Now we train the model
     for epoch in range(lowerCamelCase_		):
          model.train()
          for step, batch in enumerate(lowerCamelCase_		):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device		)
               UpperCAmelCase__							: Any 							=						model(**lowerCamelCase_		)
               UpperCAmelCase__							: Any 							=						outputs.loss
               UpperCAmelCase__							: Optional[Any] 							=						loss / gradient_accumulation_steps
               accelerator.backward(lowerCamelCase_		)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          UpperCAmelCase__							: Optional[Any] 							=						0
          for step, batch in enumerate(lowerCamelCase_		):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device		)
               with torch.no_grad():
                    UpperCAmelCase__							: Optional[Any] 							=						model(**lowerCamelCase_		)
               UpperCAmelCase__							: Union[str, Any] 							=						outputs.logits.argmax(dim=-1		)
               UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						accelerator.gather((predictions, batch["labels"])		)
               # New Code #
               # First we check if it's a distributed system
               if accelerator.use_distributed:
                    # Then see if we're on the last batch of our eval dataloader
                    if step == len(lowerCamelCase_		) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         UpperCAmelCase__							: int 							=						predictions[: len(eval_dataloader.dataset		) - samples_seen]
                         UpperCAmelCase__							: int 							=						references[: len(eval_dataloader.dataset		) - samples_seen]
                    else:
                         # Otherwise we add the number of samples seen
                         samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
               metric.add_batch(
                   predictions=lowerCamelCase_       ,    references=lowerCamelCase_       ,    )

          UpperCAmelCase__							: int 							=						metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F"epoch {epoch}:"       ,    lowerCamelCase_		)



def 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						argparse.ArgumentParser(description="Simple example of training script."		)
     parser.add_argument(
         "--mixed_precision"       ,    type=lowerCamelCase_       ,    default=lowerCamelCase_       ,    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."       ,    )
     parser.add_argument("--cpu"       ,    action="store_true"       ,    help="If passed, will train on the CPU."		)
     UpperCAmelCase__							: Any 							=						parser.parse_args()
     UpperCAmelCase__							: str 							=						{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(lowerCamelCase_       ,    lowerCamelCase_		)


if __name__ == "__main__":
    main()







"""simple docstring"""




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     # Construct model
     if gpta_config_file == "":
          UpperCAmelCase__							: Optional[int] 							=						GPTaConfig()
     else:
          UpperCAmelCase__							: Dict 							=						GPTaConfig.from_json_file(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						GPTaModel(lowerCAmelCase		)

     # Load weights from numpy
     load_tf_weights_in_gpta(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     # Save pytorch-model
     UpperCAmelCase__							: Optional[int] 							=						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     UpperCAmelCase__							: Any 							=						pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(F"Save PyTorch model to {pytorch_weights_dump_path}"		)
     torch.save(model.state_dict()       ,    lowerCAmelCase		)
     print(F"Save configuration file to {pytorch_config_dump_path}"		)
     with open(lowerCAmelCase       ,    "w"       ,    encoding="utf-8"		) as f:
          f.write(config.to_json_string()		)


if __name__ == "__main__":
    A__      :	Optional[int]          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--gpt2_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
            """This specifies the model architecture."""
        ),
    )
    A__      :	Optional[Any]          =    parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



