



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


snake_case    :						Union[str, Any]  =						1_6
snake_case    :						str  =						3_2
def 	snake_case__       (					__lowercase							,     __lowercase = 1_6       )		->		List[str]:







						"""simple docstring"""

						A__	:							Any						    = AutoTokenizer.from_pretrained("bert-base-cased"       )
						A__	:							Dict						    = load_dataset("glue"							,     "mrpc"       )

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

						def collate_fn(__lowercase       ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												A__	:							Optional[int]						    = 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":
																		A__	:							Optional[Any]						    = 1_6
												elif accelerator.mixed_precision != "no":
																		A__	:							str						    = 8
												else:
																		A__	:							Any						    = None

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

						# Instantiate dataloaders.
						A__	:							str						    = DataLoader(
						    tokenized_datasets["train"]							,     shuffle=__lowercase							,     collate_fn=__lowercase							,     batch_size=__lowercase       )
						A__	:							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    :						int  =						mocked_dataloaders  # noqa: F811
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		List[str]:







						"""simple docstring"""

						if os.environ.get("TESTING_MOCKED_DATALOADERS"							,     __lowercase       ) == "1":
												A__	:							Optional[Any]						    = 2
						# New Code #
						A__	:							Optional[int]						    = int(args.gradient_accumulation_steps       )
						# Initialize accelerator
						A__	:							int						    = Accelerator(
						    cpu=args.cpu							,     mixed_precision=args.mixed_precision							,     gradient_accumulation_steps=__lowercase       )
						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
						A__	:							Tuple						    = config["lr"]
						A__	:							Dict						    = int(config["num_epochs"]       )
						A__	:							Optional[Any]						    = int(config["seed"]       )
						A__	:							Dict						    = int(config["batch_size"]       )

						A__	:							str						    = evaluate.load("glue"							,     "mrpc"       )

						set_seed(__lowercase       )
						A__    ,       A__	:							Union[str, Any]						    = get_dataloaders(__lowercase							,     __lowercase       )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						A__	:							Tuple						    = 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).
						A__	:							int						    = model.to(accelerator.device       )

						# Instantiate optimizer
						A__	:							Dict						    = AdamW(params=model.parameters()							,     lr=__lowercase       )

						# Instantiate scheduler
						A__	:							str						    = get_linear_schedule_with_warmup(
						    optimizer=__lowercase							,     num_warmup_steps=1_0_0							,     num_training_steps=(len(__lowercase       ) * 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.
						A__    ,       A__    ,       A__    ,       A__    ,       A__	:							Optional[int]						    = accelerator.prepare(
						    __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )

						# Now we train the model
						for epoch in range(__lowercase       ):
												model.train()
												for step, batch in enumerate(__lowercase       ):
																		# 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(__lowercase       ):
																								A__	:							List[str]						    = model(**__lowercase       )
																								A__	:							Any						    = output.loss
																								accelerator.backward(__lowercase       )
																								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`.
																		batch.to(accelerator.device       )
																		with torch.no_grad():
																								A__	:							str						    = model(**__lowercase       )
																		A__	:							Dict						    = outputs.logits.argmax(dim=-1       )
																		A__    ,       A__	:							Optional[Any]						    = accelerator.gather_for_metrics((predictions, batch["labels"])       )
																		metric.add_batch(
																		    predictions=__lowercase							,     references=__lowercase							,     )

												A__	:							Tuple						    = metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'epoch {epoch}:'							,     __lowercase       )
def 	snake_case__       (					)		->		Optional[int]:







						"""simple docstring"""

						A__	:							Optional[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."							,     )
						# New Code #
						parser.add_argument(
						    "--gradient_accumulation_steps"							,     type=__lowercase							,     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."       )
						A__	:							str						    = parser.parse_args()
						A__	:							Tuple						    = {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
						training_function(__lowercase							,     __lowercase       )


if __name__ == "__main__":
		main()




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class 							lowerCAmelCase__  (	unittest.TestCase					,							UpperCamelCase						):







					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[Any]						    = load_tool("text-classification")
											self.tool.setup()
											A__	:							Any						    = load_tool("text-classification"			,		remote=_A)







					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[Any]						    = self.tool("That's quite cool"			,		["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     List[str]):
											A__	:							Any						    = self.remote_tool("That's quite cool"			,		["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							List[str]						    = self.tool(text="That's quite cool"			,		labels=["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     Tuple):
											A__	:							Union[str, Any]						    = self.remote_tool(text="That's quite cool"			,		labels=["positive", "negative"])
											self.assertEqual(_A			,		"positive")