



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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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_			:     Optional[Any]  	=  16
snake_case_			:     Union[str, Any]  	=  32






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_ = 1_6							):
						_UpperCamelCase :		Dict			    =       AutoTokenizer.from_pretrained('bert-base-cased'							)
						_UpperCamelCase :		Union[str, Any]			    =       load_dataset('glue'  ,  'mrpc'							)

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

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

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

						# Instantiate dataloaders.
						_UpperCamelCase :		str			    =       DataLoader(
						    tokenized_datasets['train']  ,  shuffle=UpperCAmelCase_  ,  collate_fn=UpperCAmelCase_  ,  batch_size=UpperCAmelCase_							)
						_UpperCamelCase :		Any			    =       DataLoader(
						    tokenized_datasets['validation']  ,  shuffle=UpperCAmelCase_  ,  collate_fn=UpperCAmelCase_  ,  batch_size=UpperCAmelCase_							)

						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_			:     Any  	=  mocked_dataloaders  # noqa: F811






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						# For testing only
						if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,  UpperCAmelCase_							) == "1":
												_UpperCamelCase :		Tuple			    =       2
						# Initialize accelerator
						_UpperCamelCase :		Dict			    =       Accelerator(cpu=args.cpu  ,  mixed_precision=args.mixed_precision							)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_UpperCamelCase :		Optional[int]			    =       config['lr']
						_UpperCamelCase :		Dict			    =       int(config['num_epochs']							)
						_UpperCamelCase :		List[Any]			    =       int(config['seed']							)
						_UpperCamelCase :		Optional[int]			    =       int(config['batch_size']							)

						_UpperCamelCase :		Union[str, Any]			    =       evaluate.load('glue'  ,  'mrpc'							)

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

						set_seed(UpperCAmelCase_							)
						_UpperCamelCase :		Optional[Any]			    =       get_dataloaders(UpperCAmelCase_  ,  UpperCAmelCase_							)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_UpperCamelCase :		Union[str, Any]			    =       AutoModelForSequenceClassification.from_pretrained('bert-base-cased'  ,  return_dict=UpperCAmelCase_							)

						# 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).
						_UpperCamelCase :		List[str]			    =       model.to(accelerator.device							)

						# Instantiate optimizer
						_UpperCamelCase :		List[str]			    =       AdamW(params=model.parameters()  ,  lr=UpperCAmelCase_							)

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

						# Now we train the model
						for epoch in range(UpperCAmelCase_							):
												model.train()
												for step, batch in enumerate(UpperCAmelCase_							):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device							)
																		_UpperCamelCase :		List[str]			    =       model(**UpperCAmelCase_							)
																		_UpperCamelCase :		Optional[Any]			    =       outputs.loss
																		_UpperCamelCase :		Optional[int]			    =       loss / gradient_accumulation_steps
																		accelerator.backward(UpperCAmelCase_							)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												_UpperCamelCase :		List[Any]			    =       0
												for step, batch in enumerate(UpperCAmelCase_							):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device							)
																		with torch.no_grad():
																								_UpperCamelCase :		List[Any]			    =       model(**UpperCAmelCase_							)
																		_UpperCamelCase :		Optional[Any]			    =       outputs.logits.argmax(dim=-1							)
																		_UpperCamelCase :		int			    =       accelerator.gather((predictions, batch['labels'])							)
																		# New Code #
																		# First we check if it's a distributed system
																		if accelerator.use_distributed:
																								# Then see if we're on the last batch of our eval dataloader
																								if step == len(UpperCAmelCase_							) - 1:
																														# Last batch needs to be truncated on distributed systems as it contains additional samples
																														_UpperCamelCase :		List[Any]			    =       predictions[: len(eval_dataloader.dataset							) - samples_seen]
																														_UpperCamelCase :		Dict			    =       references[: len(eval_dataloader.dataset							) - samples_seen]
																								else:
																														# Otherwise we add the number of samples seen
																														samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																		metric.add_batch(
																		    predictions=UpperCAmelCase_  ,  references=UpperCAmelCase_  ,  )

												_UpperCamelCase :		List[str]			    =       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'epoch {epoch}:'  ,  UpperCAmelCase_							)






def A__   (					):
						_UpperCamelCase :		str			    =       argparse.ArgumentParser(description='Simple example of training script.'							)
						parser.add_argument(
						    '--mixed_precision'  ,  type=UpperCAmelCase_  ,  default=UpperCAmelCase_  ,  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.'							)
						_UpperCamelCase :		List[str]			    =       parser.parse_args()
						_UpperCamelCase :		List[Any]			    =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
						training_function(UpperCAmelCase_  ,  UpperCAmelCase_							)


if __name__ == "__main__":
					main()







'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


snake_case_			:     int  	=  logging.get_logger(__name__)

snake_case_			:     List[str]  	=  {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """gptj"""
	lowercase__      =      {
	    """max_position_embeddings""": """n_positions""",
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(			self :    Any			,lowerCamelCase__ :    Optional[Any]=50400			,lowerCamelCase__ :    Tuple=2048			,lowerCamelCase__ :    Tuple=4096			,lowerCamelCase__ :    int=28			,lowerCamelCase__ :    Optional[Any]=16			,lowerCamelCase__ :    Optional[Any]=64			,lowerCamelCase__ :    List[Any]=None			,lowerCamelCase__ :    List[Any]="gelu_new"			,lowerCamelCase__ :    Optional[Any]=0.0			,lowerCamelCase__ :    List[str]=0.0			,lowerCamelCase__ :    List[Any]=0.0			,lowerCamelCase__ :    Tuple=1E-5			,lowerCamelCase__ :    int=0.0_2			,lowerCamelCase__ :    Any=True			,lowerCamelCase__ :    str=50256			,lowerCamelCase__ :    Any=50256			,lowerCamelCase__ :    Tuple=False			,**lowerCamelCase__ :    Optional[Any]			,):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       vocab_size
							_UpperCamelCase :		Optional[Any]			    =       n_positions
							_UpperCamelCase :		Union[str, Any]			    =       n_embd
							_UpperCamelCase :		Any			    =       n_layer
							_UpperCamelCase :		Optional[int]			    =       n_head
							_UpperCamelCase :		List[str]			    =       n_inner
							_UpperCamelCase :		List[Any]			    =       rotary_dim
							_UpperCamelCase :		int			    =       activation_function
							_UpperCamelCase :		Dict			    =       resid_pdrop
							_UpperCamelCase :		Any			    =       embd_pdrop
							_UpperCamelCase :		Union[str, Any]			    =       attn_pdrop
							_UpperCamelCase :		Union[str, Any]			    =       layer_norm_epsilon
							_UpperCamelCase :		Optional[Any]			    =       initializer_range
							_UpperCamelCase :		str			    =       use_cache

							_UpperCamelCase :		Union[str, Any]			    =       bos_token_id
							_UpperCamelCase :		Any			    =       eos_token_id

							super().__init__(
							    bos_token_id=lowerCamelCase__			,eos_token_id=lowerCamelCase__			,tie_word_embeddings=lowerCamelCase__			,**lowerCamelCase__			)






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Tuple			,lowerCamelCase__ :    PretrainedConfig			,lowerCamelCase__ :    str = "default"			,lowerCamelCase__ :    List[PatchingSpec] = None			,lowerCamelCase__ :    bool = False			,):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			,task=lowerCamelCase__			,patching_specs=lowerCamelCase__			,use_past=lowerCamelCase__			)
							if not getattr(self._config			,'pad_token_id'			,lowerCamelCase__			):
													# TODO: how to do that better?
													_UpperCamelCase :		int			    =       0





	@property
	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}			)
							if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase__			,direction='inputs'			)
													_UpperCamelCase :		Tuple			    =       {0: 'batch', 1: 'past_sequence + sequence'}
							else:
													_UpperCamelCase :		Any			    =       {0: 'batch', 1: 'sequence'}

							return common_inputs





	@property
	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							return self._config.n_layer





	@property
	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							return self._config.n_head





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    PreTrainedTokenizer			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    int = -1			,lowerCamelCase__ :    bool = False			,lowerCamelCase__ :    Optional[TensorType] = None			,):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       super(lowerCamelCase__			,self			).generate_dummy_inputs(
							    lowerCamelCase__			,batch_size=lowerCamelCase__			,seq_length=lowerCamelCase__			,is_pair=lowerCamelCase__			,framework=lowerCamelCase__			)

							# We need to order the input in the way they appears in the forward()
							_UpperCamelCase :		Tuple			    =       OrderedDict({'input_ids': common_inputs['input_ids']}			)

							# Need to add the past_keys
							if self.use_past:
													if not is_torch_available():
																			raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
													else:
																			import torch

																			_UpperCamelCase    ,      _UpperCamelCase :		str			    =       common_inputs['input_ids'].shape
																			# Not using the same length for past_key_values
																			_UpperCamelCase :		Optional[int]			    =       seqlen + 2
																			_UpperCamelCase :		List[Any]			    =       (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			_UpperCamelCase :		Optional[Any]			    =       [
																			    (torch.zeros(lowerCamelCase__			), torch.zeros(lowerCamelCase__			)) for _ in range(self.num_layers			)
																			]

							_UpperCamelCase :		Union[str, Any]			    =       common_inputs['attention_mask']
							if self.use_past:
													_UpperCamelCase :		Any			    =       ordered_inputs['attention_mask'].dtype
													_UpperCamelCase :		List[str]			    =       torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__			,lowerCamelCase__			,dtype=lowerCamelCase__			)]			,dim=1			)

							return ordered_inputs





	@property
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return 13


