





'''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 the experiment tracking capability,
# 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
#
########################################################################

__lowerCAmelCase				:      Optional[Any]						      =16
__lowerCAmelCase				:      List[Any]						      =32





def 		UpperCamelCase							(				_lowerCamelCase   :   Accelerator		,			_lowerCamelCase   :   int = 16			):
    A__							=     AutoTokenizer.from_pretrained("bert-base-cased"			)
    A__							=     load_dataset("glue"		,			"mrpc"			)

    def tokenize_function(_lowerCamelCase   :   Optional[Any]			):
        # max_length=None => use the model max length (it's actually the default)
        A__							=     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():
        A__							=     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
    A__							=     tokenized_datasets.rename_column("label"		,			"labels"			)

    def collate_fn(_lowerCamelCase   :   Any			):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        A__							=     1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            A__							=     16
        elif accelerator.mixed_precision != "no":
            A__							=     8
        else:
            A__							=     None

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

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

  __lowerCAmelCase				:      List[str]						      =mocked_dataloaders  # noqa: F811





def 		UpperCamelCase							(				_lowerCamelCase   :   List[Any]		,			_lowerCamelCase   :   Tuple			):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"		,			_lowerCamelCase			) == "1":
        A__							=     2
    # Initialize Accelerator

    # New Code #
    # We pass in "all" to `log_with` to grab all available trackers in the environment
    # Note: If using a custom `Tracker` class, should be passed in here such as:
    # >>> log_with = ["all", MyCustomTrackerClassInstance()]
    if args.with_tracking:
        A__							=     Accelerator(
            cpu=args.cpu		,			mixed_precision=args.mixed_precision		,			log_with="all"		,			project_dir=args.project_dir			)
    else:
        A__							=     Accelerator(cpu=args.cpu		,			mixed_precision=args.mixed_precision			)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    A__							=     config["lr"]
    A__							=     int(config["num_epochs"]			)
    A__							=     int(config["seed"]			)
    A__							=     int(config["batch_size"]			)
    set_seed(_lowerCamelCase			)

    A__,      A__							=     get_dataloaders(_lowerCamelCase		,			_lowerCamelCase			)
    A__							=     evaluate.load("glue"		,			"mrpc"			)

    # If the batch size is too big we use gradient accumulation
    A__							=     1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        A__							=     batch_size // MAX_GPU_BATCH_SIZE
        A__							=     MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    A__							=     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).
    A__							=     model.to(accelerator.device			)

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

    # Instantiate scheduler
    A__							=     get_linear_schedule_with_warmup(
        optimizer=_lowerCamelCase		,			num_warmup_steps=1_00		,			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.
    A__,      A__,      A__,      A__,      A__							=     accelerator.prepare(
        _lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)

    # New Code #
    # We need to initialize the trackers we use. Overall configurations can also be stored
    if args.with_tracking:
        A__							=     os.path.split(_lowerCamelCase			)[-1].split("."			)[0]
        accelerator.init_trackers(_lowerCamelCase		,			_lowerCamelCase			)

    # Now we train the model
    for epoch in range(_lowerCamelCase			):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            A__							=     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			)
            A__							=     model(**_lowerCamelCase			)
            A__							=     outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            A__							=     loss / gradient_accumulation_steps
            accelerator.backward(_lowerCamelCase			)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

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

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

        # New Code #
        # To actually log, we call `Accelerator.log`
        # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": eval_metric["accuracy"],
                    "f1": eval_metric["f1"],
                    "train_loss": total_loss.item() / len(_lowerCamelCase			),
                    "epoch": epoch,
                }		,			step=_lowerCamelCase		,			)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
    if args.with_tracking:
        accelerator.end_training()





def 		UpperCamelCase							(				):
    A__							=     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."			)
    parser.add_argument(
        "--with_tracking"		,			action="store_true"		,			help="Whether to load in all available experiment trackers from the environment and use them for logging."		,			)
    parser.add_argument(
        "--project_dir"		,			type=_lowerCamelCase		,			default="logs"		,			help="Location on where to store experiment tracking logs` and relevent project information"		,			)
    A__							=     parser.parse_args()
    A__							=     {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(_lowerCamelCase		,			_lowerCamelCase			)


if __name__ == "__main__":
  main()










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      str						      ={
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""distilbert"""
 __lowercase  						=							{
     """hidden_size""": """dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
 }
 def __init__(							self :Tuple		,			lowercase_ :List[str]=3_05_22		,			lowercase_ :Tuple=5_12		,			lowercase_ :List[str]=False		,			lowercase_ :int=6		,			lowercase_ :Optional[int]=12		,			lowercase_ :Optional[int]=7_68		,			lowercase_ :str=4 * 7_68		,			lowercase_ :List[Any]=0.1		,			lowercase_ :str=0.1		,			lowercase_ :Optional[int]="gelu"		,			lowercase_ :Optional[Any]=0.0_2		,			lowercase_ :Optional[Any]=0.1		,			lowercase_ :List[Any]=0.2		,			lowercase_ :str=0		,			**lowercase_ :Union[str, Any]		,			)->				str:
     A__							=     vocab_size
     A__							=     max_position_embeddings
     A__							=     sinusoidal_pos_embds
     A__							=     n_layers
     A__							=     n_heads
     A__							=     dim
     A__							=     hidden_dim
     A__							=     dropout
     A__							=     attention_dropout
     A__							=     activation
     A__							=     initializer_range
     A__							=     qa_dropout
     A__							=     seq_classif_dropout
     super().__init__(**lowercase_		,			pad_token_id=lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         A__							=     {0: "batch", 1: "choice", 2: "sequence"}
     else:
         A__							=     {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
         ]				)



