
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
#
########################################################################

_A						      =				16
_A						      =				32



def 		lowercase_    (     A__				,	A__ = 16	)							->							List[Any]:
    """simple docstring"""

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

    def tokenize_function(A__	):
        # max_length=None => use the model max length (it's actually the default)
        snake_case				  =      tokenizer(examples["sentence1"]				,	examples["sentence2"]				,	truncation=A__				,	max_length=A__	)
        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():
        snake_case				  =      datasets.map(
            A__				,	batched=A__				,	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
    snake_case				  =      tokenized_datasets.rename_column("label"				,	"labels"	)

    def collate_fn(A__	):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        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":
            snake_case				  =      16
        elif accelerator.mixed_precision != "no":
            snake_case				  =      8
        else:
            snake_case				  =      None

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

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

    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						      =				mocked_dataloaders  # noqa: F811



def 		lowercase_    (     A__				,	A__	)							->							Tuple:
    """simple docstring"""

    if os.environ.get("TESTING_MOCKED_DATALOADERS"				,	A__	) == "1":
        snake_case				  =      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:
        snake_case				  =      Accelerator(
            cpu=args.cpu				,	mixed_precision=args.mixed_precision				,	log_with="all"				,	project_dir=args.project_dir	)
    else:
        snake_case				  =      Accelerator(cpu=args.cpu				,	mixed_precision=args.mixed_precision	)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case				  =      config["lr"]
    snake_case				  =      int(config["num_epochs"]	)
    snake_case				  =      int(config["seed"]	)
    snake_case				  =      int(config["batch_size"]	)
    set_seed(A__	)

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

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

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

    # 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).
    snake_case				  =      model.to(accelerator.device	)

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

    # Instantiate scheduler
    snake_case				  =      get_linear_schedule_with_warmup(
        optimizer=A__				,	num_warmup_steps=100				,	num_training_steps=(len(A__	) * 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.
    snake_case				  =      accelerator.prepare(
        A__				,	A__				,	A__				,	A__				,	A__	)

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

    # Now we train the model
    for epoch in range(A__	):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            snake_case				  =      0
        for step, batch in enumerate(A__	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device	)
            snake_case				  =      model(**A__	)
            snake_case				  =      outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            snake_case				  =      loss / gradient_accumulation_steps
            accelerator.backward(A__	)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

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

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

        # 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(A__	),
                    "epoch": epoch,
                }				,	step=A__				,	)

        # 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 		lowercase_    (     )							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      argparse.ArgumentParser(description="Simple example of training script."	)
    parser.add_argument(
        "--mixed_precision"				,	type=A__				,	default=A__				,	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=A__				,	default="logs"				,	help="Location on where to store experiment tracking logs` and relevent project information"				,	)
    snake_case				  =      parser.parse_args()
    snake_case				  =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(A__				,	A__	)


if __name__ == "__main__":
   main()



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Dict     =	"canine"





     def __init__(self   :					List[Any]       ,					_A   :					Union[str, Any]=7_6_8       ,					_A   :					Any=1_2       ,					_A   :					List[Any]=1_2       ,					_A   :					List[Any]=3_0_7_2       ,					_A   :					Dict="gelu"       ,					_A   :					Optional[Any]=0.1       ,					_A   :					Tuple=0.1       ,					_A   :					str=1_6_3_8_4       ,					_A   :					Union[str, Any]=1_6       ,					_A   :					Any=0.02       ,					_A   :					List[str]=1E-12       ,					_A   :					Union[str, Any]=0       ,					_A   :					Dict=0Xe0_00       ,					_A   :					List[Any]=0Xe0_01       ,					_A   :					int=4       ,					_A   :					str=4       ,					_A   :					Optional[int]=8       ,					_A   :					Optional[Any]=1_6_3_8_4       ,					_A   :					Optional[Any]=1_2_8       ,					**_A   :					Union[str, Any]       ,					)		->		Union[str, Any]:
         super().__init__(pad_token_id=_A       ,					bos_token_id=_A       ,					eos_token_id=_A       ,					**_A     )

         snake_case				  =      max_position_embeddings
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_attention_heads
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_act
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      initializer_range
         snake_case				  =      type_vocab_size
         snake_case				  =      layer_norm_eps

         # Character config:
         snake_case				  =      downsampling_rate
         snake_case				  =      upsampling_kernel_size
         snake_case				  =      num_hash_functions
         snake_case				  =      num_hash_buckets
         snake_case				  =      local_transformer_stride

