


"""simple docstring"""



import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.roberta.modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
  )







class _lowerCAmelCase       (   unittest.TestCase			):







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__=13		,			UpperCamelCase__=7		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=99		,			UpperCamelCase__=32		,			UpperCamelCase__=5		,			UpperCamelCase__=4		,			UpperCamelCase__=37		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=16		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=4		,			)			->	Dict:

       '''simple docstring'''




       snake_case					:   Any         =					parent
       snake_case					:   int         =					batch_size
       snake_case					:   Optional[Any]         =					seq_length
       snake_case					:   Dict         =					is_training
       snake_case					:   str         =					use_attention_mask
       snake_case					:   List[Any]         =					use_token_type_ids
       snake_case					:   Tuple         =					use_labels
       snake_case					:   str         =					vocab_size
       snake_case					:   Optional[Any]         =					hidden_size
       snake_case					:   Optional[Any]         =					num_hidden_layers
       snake_case					:   Dict         =					num_attention_heads
       snake_case					:   int         =					intermediate_size
       snake_case					:   Union[str, Any]         =					hidden_act
       snake_case					:   Optional[Any]         =					hidden_dropout_prob
       snake_case					:   int         =					attention_probs_dropout_prob
       snake_case					:   Optional[int]         =					max_position_embeddings
       snake_case					:   Dict         =					type_vocab_size
       snake_case					:   str         =					type_sequence_label_size
       snake_case					:   List[Any]         =					initializer_range
       snake_case					:   Dict         =					num_choices







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size		)

       snake_case					:   Optional[int]         =					None
       if self.use_attention_mask:
           snake_case					:   Optional[Any]         =					random_attention_mask([self.batch_size, self.seq_length]		)

       snake_case					:   List[Any]         =					None
       if self.use_token_type_ids:
           snake_case					:   Dict         =					ids_tensor([self.batch_size, self.seq_length]		,			self.type_vocab_size		)

       snake_case					:   Union[str, Any]         =					RobertaConfig(
           vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			max_position_embeddings=self.max_position_embeddings		,			type_vocab_size=self.type_vocab_size		,			is_decoder=UpperCamelCase__		,			initializer_range=self.initializer_range		,			)

       return config, input_ids, token_type_ids, attention_mask







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.prepare_config_and_inputs()
       snake_case      ,snake_case      ,snake_case      ,snake_case					:   List[Any]         =					config_and_inputs
       snake_case					:   Tuple         =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.prepare_config_and_inputs()
       snake_case      ,snake_case      ,snake_case      ,snake_case					:   List[Any]         =					config_and_inputs

       snake_case					:   int         =					True
       snake_case					:   Optional[int]         =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
       snake_case					:   Optional[Any]         =					ids_tensor([self.batch_size, self.seq_length]		,			vocab_size=2		)

       return (
           config,
           input_ids,
           token_type_ids,
           encoder_hidden_states,
           encoder_attention_mask,
       )







@require_flax
class _lowerCAmelCase       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: int													=						True

   __UpperCAmelCase				: Optional[Any]													=						(
       (
           FlaxRobertaModel,
           FlaxRobertaForCausalLM,
           FlaxRobertaForMaskedLM,
           FlaxRobertaForSequenceClassification,
           FlaxRobertaForTokenClassification,
           FlaxRobertaForMultipleChoice,
           FlaxRobertaForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Dict         =					FlaxRobertaModelTester(self		)







   @slow
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       for model_class_name in self.all_model_classes:
           snake_case					:   Dict         =					model_class_name.from_pretrained("roberta-base"		,			from_pt=UpperCamelCase__		)
           snake_case					:   List[str]         =					model(np.ones((1, 1)		)		)
           self.assertIsNotNone(UpperCamelCase__		)









"""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
#
########################################################################

__snake_case         =					16
__snake_case         =					32







def 						__lowerCAmelCase		(		lowercase						:						Accelerator		,							lowercase						:						int = 16				)			->		Union[str, Any]:


    """simple docstring"""





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

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

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

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

    # Instantiate dataloaders.
    snake_case					:   Any         =					DataLoader(
        tokenized_datasets["train"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)
    snake_case					:   List[str]         =					DataLoader(
        tokenized_datasets["validation"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)

    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

  __snake_case         =					mocked_dataloaders  # noqa: F811







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						int				)			->		Tuple:


    """simple docstring"""





    if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							lowercase				) == "1":
        snake_case					:   Union[str, Any]         =					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					:   str         =					Accelerator(
            cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with="all"		,							project_dir=args.project_dir				)
    else:
        snake_case					:   Optional[int]         =					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					:   str         =					config["lr"]
    snake_case					:   Dict         =					int(config["num_epochs"]				)
    snake_case					:   int         =					int(config["seed"]				)
    snake_case					:   Tuple         =					int(config["batch_size"]				)
    set_seed(lowercase				)

    snake_case      ,snake_case					:   List[Any]         =					get_dataloaders(lowercase		,							lowercase				)
    snake_case					:   Optional[Any]         =					evaluate.load("glue"		,							"mrpc"				)

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

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

    # 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					:   str         =					model.to(accelerator.device				)

    # Instantiate optimizer
    snake_case					:   Tuple         =					AdamW(params=model.parameters()		,							lr=lowercase				)

    # Instantiate scheduler
    snake_case					:   Optional[Any]         =					get_linear_schedule_with_warmup(
        optimizer=lowercase		,							num_warmup_steps=100		,							num_training_steps=(len(lowercase				) * 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      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   Union[str, Any]         =					accelerator.prepare(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase				)

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

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

        model.eval()
        for step, batch in enumerate(lowercase				):
            # 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					:   Optional[int]         =					model(**lowercase				)
            snake_case					:   Tuple         =					outputs.logits.argmax(dim=-1				)
            snake_case      ,snake_case					:   List[str]         =					accelerator.gather_for_metrics((predictions, batch["labels"])				)
            metric.add_batch(
                predictions=lowercase		,							references=lowercase		,							)

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

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

    # 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 						__lowerCAmelCase		(		)			->		str:


    """simple docstring"""





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


if __name__ == "__main__":
  main()





