




'''simple docstring'''







import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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							:						List[Any]							     =  32






def 		_lowercase		(     lowercase_						,							lowercase_ = 16  ):



		'''simple docstring'''







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

		def tokenize_function(lowercase_  ):
				# max_length=None => use the model max length (it's actually the default)
				_snake_case     	=	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     	=	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     	=	tokenized_datasets.rename_column("label"						,							"labels"  )

		def collate_fn(lowercase_  ):
				# 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(
				    lowercase_						,							padding="longest"						,							pad_to_multiple_of=lowercase_						,							return_tensors="pt"						,							)

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

						_lowerCamelCase							:						Tuple							     =  mocked_dataloaders  # noqa: F811






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		if os.environ.get("TESTING_MOCKED_DATALOADERS"						,							lowercase_  ) == "1":
				_snake_case     	=	2
		# Initialize accelerator
		_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"]  )

		_snake_case     	=	evaluate.load("glue"						,							"mrpc"  )

		# New Code #
		# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
		# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
		# half each time. From this, we can calculate the number of gradient accumulation steps needed
		# and modify the Accelerator object as a result
		@find_executable_batch_size(starting_batch_size=int(lowercase_  )  )
		def inner_training_loop(lowercase_  ):
				# Since we need to modify the outside accelerator object, we need to bring it
				# to the local scope
				nonlocal accelerator

				# We can calculate the number of gradient accumulation steps based on the current
				# batch size vs the starting batch size
				_snake_case     	=	observed_batch_size // batch_size

				# And then set it in the Accelerator directly:
				_snake_case     	=	num_gradient_accumulation_steps

				# Next we need to free all of the stored model references in the Accelerator each time
				accelerator.free_memory()

				# And set the seed so our results are reproducable each reset
				set_seed(lowercase_  )

				# 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=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     	=	model.to(accelerator.device  )

				# Instantiate optimizer
				_snake_case     	=	AdamW(params=model.parameters()						,							lr=lowercase_  )
				_snake_case     	=	get_dataloaders(lowercase_						,							lowercase_  )

				# Instantiate scheduler
				_snake_case     	=	get_linear_schedule_with_warmup(
				    optimizer=lowercase_						,							num_warmup_steps=100						,							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.
				_snake_case     	=	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_  ):
								# And perform gradient accumulation
								with accelerator.accumulate(lowercase_  ):
										# We could avoid this line since we set the accelerator with `device_placement=True`.
										batch.to(accelerator.device  )
										_snake_case     	=	model(**lowercase_  )
										_snake_case     	=	outputs.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():
										_snake_case     	=	model(**lowercase_  )
								_snake_case     	=	outputs.logits.argmax(dim=-1  )
								_snake_case     	=	accelerator.gather_for_metrics((predictions, batch["labels"])  )
								metric.add_batch(
								    predictions=lowercase_						,							references=lowercase_						,							)

						_snake_case     	=	metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''						,							lowercase_  )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
		inner_training_loop()






def 		_lowercase		(     ):



		'''simple docstring'''







		_snake_case     	=	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."  )
		_snake_case     	=	parser.parse_args()
		# New Code #
		# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
		_snake_case     	=	{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
		training_function(lowercase_						,							lowercase_  )


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[int]							     =  logging.get_logger(__name__)

_lowerCamelCase							:						int							     =  {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			"""flaubert"""
					lowercase__   =			{
					    """hidden_size""": """emb_dim""",
					    """num_attention_heads""": """n_heads""",
					    """num_hidden_layers""": """n_layers""",
					    """n_words""": """vocab_size""",  # For backward compatibility
					}






					def __init__(   self,  __UpperCamelCase=False,  __UpperCamelCase=0.0,  __UpperCamelCase=30_145,  __UpperCamelCase=2_048,  __UpperCamelCase=12,  __UpperCamelCase=16,  __UpperCamelCase=0.1,  __UpperCamelCase=0.1,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=1,  __UpperCamelCase=True,  __UpperCamelCase=512,  __UpperCamelCase=2_048**-0.5,  __UpperCamelCase=1E-12,  __UpperCamelCase=0.02,  __UpperCamelCase=0,  __UpperCamelCase=1,  __UpperCamelCase=2,  __UpperCamelCase=3,  __UpperCamelCase=5,  __UpperCamelCase=True,  __UpperCamelCase="first",  __UpperCamelCase=True,  __UpperCamelCase=None,  __UpperCamelCase=True,  __UpperCamelCase=0.1,  __UpperCamelCase=5,  __UpperCamelCase=5,  __UpperCamelCase=0,  __UpperCamelCase=0,  __UpperCamelCase=2,  __UpperCamelCase=0,  **__UpperCamelCase,  ):
							_snake_case     	=	pre_norm
							_snake_case     	=	layerdrop
							_snake_case     	=	vocab_size
							_snake_case     	=	emb_dim
							_snake_case     	=	n_layers
							_snake_case     	=	n_heads
							_snake_case     	=	dropout
							_snake_case     	=	attention_dropout
							_snake_case     	=	gelu_activation
							_snake_case     	=	sinusoidal_embeddings
							_snake_case     	=	causal
							_snake_case     	=	asm
							_snake_case     	=	n_langs
							_snake_case     	=	use_lang_emb
							_snake_case     	=	layer_norm_eps
							_snake_case     	=	bos_index
							_snake_case     	=	eos_index
							_snake_case     	=	pad_index
							_snake_case     	=	unk_index
							_snake_case     	=	mask_index
							_snake_case     	=	is_encoder
							_snake_case     	=	max_position_embeddings
							_snake_case     	=	embed_init_std
							_snake_case     	=	init_std
							_snake_case     	=	summary_type
							_snake_case     	=	summary_use_proj
							_snake_case     	=	summary_activation
							_snake_case     	=	summary_proj_to_labels
							_snake_case     	=	summary_first_dropout
							_snake_case     	=	start_n_top
							_snake_case     	=	end_n_top
							_snake_case     	=	mask_token_id
							_snake_case     	=	lang_id

							if "n_words" in kwargs:
									_snake_case     	=	kwargs["n_words"]

							super().__init__(pad_token_id=__UpperCamelCase,  bos_token_id=__UpperCamelCase,  **__UpperCamelCase			)





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							if self.task == "multiple-choice":
									_snake_case     	=	{0: "batch", 1: "choice", 2: "sequence"}
							else:
									_snake_case     	=	{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]			)





