


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


_lowerCAmelCase:						Optional[int]   				=       16
_lowerCAmelCase:						Optional[Any]   				=       32






def   _lowercase(					__a				:						Accelerator    ,     __a				:						int = 16     ):
							a__      =AutoTokenizer.from_pretrained('bert-base-cased'     )
							a__      =load_dataset('glue'    ,     'mrpc'     )

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

							def collate_fn(__a				:						Dict     ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														a__      =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":
																					a__      =16
														elif accelerator.mixed_precision != "no":
																					a__      =8
														else:
																					a__      =None

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

							# Instantiate dataloaders.
							a__      =DataLoader(
							    tokenized_datasets['train']    ,     shuffle=__a    ,     collate_fn=__a    ,     batch_size=__a     )
							a__      =DataLoader(
							    tokenized_datasets['validation']    ,     shuffle=__a    ,     collate_fn=__a    ,     batch_size=__a     )

							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:						Any   				=       mocked_dataloaders  # noqa: F811






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

							a__      =evaluate.load('glue'    ,     'mrpc'     )

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

							set_seed(__a     )
							a__    ,     a__      =get_dataloaders(__a    ,     __a     )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							a__      =AutoModelForSequenceClassification.from_pretrained('bert-base-cased'    ,     return_dict=__a     )

							# 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__      =model.to(accelerator.device     )

							# Instantiate optimizer
							a__      =AdamW(params=model.parameters()    ,     lr=__a     )

							# Instantiate scheduler
							a__      =get_linear_schedule_with_warmup(
							    optimizer=__a    ,     num_warmup_steps=100    ,     num_training_steps=(len(__a     ) * 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.
							a__    ,     a__    ,     a__    ,     a__    ,     a__      =accelerator.prepare(
							    __a    ,     __a    ,     __a    ,     __a    ,     __a     )

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

														model.eval()
														a__      =0
														for step, batch in enumerate(__a     ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device     )
																					with torch.no_grad():
																												a__      =model(**__a     )
																					a__      =outputs.logits.argmax(dim=-1     )
																					a__    ,     a__      =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(__a     ) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			a__      =predictions[: len(eval_dataloader.dataset     ) - samples_seen]
																																			a__      =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=__a    ,     references=__a    ,     )

														a__      =metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""    ,     __a     )






def   _lowercase(					):
							a__      =argparse.ArgumentParser(description='Simple example of training script.'     )
							parser.add_argument(
							    '--mixed_precision'    ,     type=__a    ,     default=__a    ,     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.'     )
							a__      =parser.parse_args()
							a__      ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(__a    ,     __a     )


if __name__ == "__main__":
	main()





from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase:						List[Any]   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Any   				=       {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}




class       lowercase_					(lowercase__   ):
				snake_case	    ='autoformer'
				snake_case	    ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				    'num_hidden_layers': 'encoder_layers',
				}





				def __init__(				self       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = "student_t"       ,  lowercase_ = "nll"       ,  lowercase_ = 1       ,  lowercase_ = [1, 2, 3, 4, 5, 6, 7]       ,  lowercase_ = True       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = 64       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 32       ,  lowercase_ = 32       ,  lowercase_ = "gelu"       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 100       ,  lowercase_ = 0.02       ,  lowercase_ = True       ,  lowercase_=True       ,  lowercase_ = 10       ,  lowercase_ = 25       ,  lowercase_ = 3       ,  **lowercase_       ,  )  -> Union[str, Any]:
											# time series specific configuration
											a__      =prediction_length
											a__      =context_length if context_length is not None else prediction_length
											a__      =distribution_output
											a__      =loss
											a__      =input_size
											a__      =num_time_features
											a__      =lags_sequence
											a__      =scaling
											a__      =num_dynamic_real_features
											a__      =num_static_real_features
											a__      =num_static_categorical_features
											if cardinality is not None and num_static_categorical_features > 0:
																		if len(lowercase_) != num_static_categorical_features:
																									raise ValueError(
																									    'The cardinality should be a list of the same length as `num_static_categorical_features`')
																		a__      =cardinality
											else:
																		a__      =[0]
											if embedding_dimension is not None and num_static_categorical_features > 0:
																		if len(lowercase_) != num_static_categorical_features:
																									raise ValueError(
																									    'The embedding dimension should be a list of the same length as `num_static_categorical_features`')
																		a__      =embedding_dimension
											else:
																		a__      =[min(50       ,  (cat + 1) // 2) for cat in self.cardinality]
											a__      =num_parallel_samples

											# Transformer architecture configuration
											a__      =input_size * len(self.lags_sequence) + self._number_of_features
											a__      =d_model
											a__      =encoder_attention_heads
											a__      =decoder_attention_heads
											a__      =encoder_ffn_dim
											a__      =decoder_ffn_dim
											a__      =encoder_layers
											a__      =decoder_layers

											a__      =dropout
											a__      =attention_dropout
											a__      =activation_dropout
											a__      =encoder_layerdrop
											a__      =decoder_layerdrop

											a__      =activation_function
											a__      =init_std

											a__      =use_cache

											# Autoformer
											a__      =label_length
											a__      =moving_average
											a__      =autocorrelation_factor

											super().__init__(is_encoder_decoder=lowercase_       ,  **lowercase_)





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return (
											    sum(self.embedding_dimension)
											    + self.num_dynamic_real_features
											    + self.num_time_features
											    + self.num_static_real_features
											    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
											)

