


import argparse

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


lowerCamelCase						=		16
lowerCamelCase						=		32






def      lowerCamelCase_    (       _a	,   _a = 16       ):




		"""simple docstring"""






		lowerCAmelCase__   :			Any         =					AutoTokenizer.from_pretrained('''bert-base-cased'''       )
		lowerCAmelCase__   :			Optional[Any]         =					load_dataset('''glue'''	,   '''mrpc'''       )

		def tokenize_function(_a       ):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase__   :			Optional[Any]         =					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():
				lowerCAmelCase__   :			Optional[Any]         =					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
		lowerCAmelCase__   :			Optional[int]         =					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.
				lowerCAmelCase__   :			List[str]         =					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":
						lowerCAmelCase__   :			int         =					16
				elif accelerator.mixed_precision != "no":
						lowerCAmelCase__   :			Optional[Any]         =					8
				else:
						lowerCAmelCase__   :			Any         =					None

				return tokenizer.pad(
				    __lowerCamelCase	,   padding='''longest'''	,   max_length=__lowerCamelCase	,   pad_to_multiple_of=__lowerCamelCase	,   return_tensors='''pt'''	,   )

		# Instantiate dataloaders.
		lowerCAmelCase__   :			Optional[int]         =					DataLoader(
		    tokenized_datasets['''train''']	,   shuffle=__lowerCamelCase	,   collate_fn=__lowerCamelCase	,   batch_size=__lowerCamelCase	,   drop_last=__lowerCamelCase       )
		lowerCAmelCase__   :			int         =					DataLoader(
		    tokenized_datasets['''validation''']	,   shuffle=__lowerCamelCase	,   collate_fn=__lowerCamelCase	,   batch_size=__lowerCamelCase	,   drop_last=(accelerator.mixed_precision == '''fp8''')	,   )

		return train_dataloader, eval_dataloader






def      lowerCamelCase_    (       _a	,   _a       ):




		"""simple docstring"""






		lowerCAmelCase__   :			Any         =					Accelerator(cpu=args.cpu	,   mixed_precision=args.mixed_precision       )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase__   :			str         =					config["lr"]
		lowerCAmelCase__   :			str         =					int(config['''num_epochs''']       )
		lowerCAmelCase__   :			Dict         =					int(config['''seed''']       )
		lowerCAmelCase__   :			str         =					int(config['''batch_size''']       )

		lowerCAmelCase__   :			str         =					evaluate.load('''glue'''	,   '''mrpc'''       )

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

		set_seed(__lowerCamelCase       )
		lowerCAmelCase__   :			str         =					get_dataloaders(__lowerCamelCase	,   __lowerCamelCase       )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase__   :			int         =					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).
		lowerCAmelCase__   :			Dict         =					model.to(accelerator.device       )
		# Instantiate optimizer
		lowerCAmelCase__   :			str         =					AdamW(params=model.parameters()	,   lr=__lowerCamelCase       )

		# Instantiate scheduler
		lowerCAmelCase__   :			Any         =					get_linear_schedule_with_warmup(
		    optimizer=__lowerCamelCase	,   num_warmup_steps=100	,   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.

		lowerCAmelCase__   :			Union[str, Any]         =					accelerator.prepare(
		    __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase       )

		# Now we train the model
		for epoch in range(__lowerCamelCase       ):
				model.train()
				for step, batch in enumerate(__lowerCamelCase       ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device       )
						lowerCAmelCase__   :			Union[str, Any]         =					model(**__lowerCamelCase       )
						lowerCAmelCase__   :			Union[str, Any]         =					outputs.loss
						lowerCAmelCase__   :			int         =					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`.
						batch.to(accelerator.device       )
						with torch.no_grad():
								lowerCAmelCase__   :			Optional[int]         =					model(**__lowerCamelCase       )
						lowerCAmelCase__   :			str         =					outputs.logits.argmax(dim=-1       )
						lowerCAmelCase__   :			Any         =					accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
						metric.add_batch(
						    predictions=__lowerCamelCase	,   references=__lowerCamelCase	,   )

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






def      lowerCamelCase_    (       ):




		"""simple docstring"""






		lowerCAmelCase__   :			Optional[int]         =					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.'''       )
		lowerCAmelCase__   :			List[Any]         =					parser.parse_args()
		lowerCAmelCase__   :			Optional[int]         =					{"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


_a		       =							{
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		"""albert"""







  def __init__(		self				,   lowercase_=3_0000				,   lowercase_=128				,   lowercase_=4096				,   lowercase_=12				,   lowercase_=1				,   lowercase_=64				,   lowercase_=1_6384				,   lowercase_=1				,   lowercase_="gelu_new"				,   lowercase_=0				,   lowercase_=0				,   lowercase_=512				,   lowercase_=2				,   lowercase_=0.02				,   lowercase_=1E-1_2				,   lowercase_=0.1				,   lowercase_="absolute"				,   lowercase_=0				,   lowercase_=2				,   lowercase_=3				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(pad_token_id=lowercase_				,   bos_token_id=lowercase_				,   eos_token_id=lowercase_				,   **lowercase_  )

     UpperCAmelCase_					:					int			     =    vocab_size
     UpperCAmelCase_					:					Optional[int]			     =    embedding_size
     UpperCAmelCase_					:					List[str]			     =    hidden_size
     UpperCAmelCase_					:					Optional[int]			     =    num_hidden_layers
     UpperCAmelCase_					:					Union[str, Any]			     =    num_hidden_groups
     UpperCAmelCase_					:					Dict			     =    num_attention_heads
     UpperCAmelCase_					:					Any			     =    inner_group_num
     UpperCAmelCase_					:					Union[str, Any]			     =    hidden_act
     UpperCAmelCase_					:					Union[str, Any]			     =    intermediate_size
     UpperCAmelCase_					:					List[str]			     =    hidden_dropout_prob
     UpperCAmelCase_					:					Union[str, Any]			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					Optional[Any]			     =    max_position_embeddings
     UpperCAmelCase_					:					Any			     =    type_vocab_size
     UpperCAmelCase_					:					List[str]			     =    initializer_range
     UpperCAmelCase_					:					Optional[int]			     =    layer_norm_eps
     UpperCAmelCase_					:					List[Any]			     =    classifier_dropout_prob
     UpperCAmelCase_					:					Tuple			     =    position_embedding_type




class A_	(lowercase__      ):




  '''simple docstring'''







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if self.task == "multiple-choice":
        UpperCAmelCase_					:					int			     =    {0: "batch", 1: "choice", 2: "sequence"}
     else:
        UpperCAmelCase_					:					Optional[Any]			     =    {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("token_type_ids", dynamic_axis),
         ]  )


