

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


A__						=							16
A__						=							32




def 					_lowercase     (				a_				:					Accelerator					,a_				:					int = 1_6				)				->	Union[str, Any]:




					'''simple docstring'''







					__magic_name__							=    AutoTokenizer.from_pretrained('bert-base-cased'				)
					__magic_name__							=    load_dataset('glue'					,'mrpc'				)

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

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

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

					# Instantiate dataloaders.
					__magic_name__							=    DataLoader(
					    tokenized_datasets['train']					,shuffle=a_					,collate_fn=a_					,batch_size=a_				)
					__magic_name__							=    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

						A__						=							mocked_dataloaders  # noqa: F811




def 					_lowercase     (				a_				:					List[Any]					,a_				:					List[str]				)				->	str:




					'''simple docstring'''







					if os.environ.get('TESTING_MOCKED_DATALOADERS'					,a_				) == "1":
										__magic_name__							=    2
					# New Code #
					__magic_name__							=    int(args.gradient_accumulation_steps				)
					# Initialize accelerator
					__magic_name__							=    Accelerator(
					    cpu=args.cpu					,mixed_precision=args.mixed_precision					,gradient_accumulation_steps=a_				)
					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
					__magic_name__							=    config['lr']
					__magic_name__							=    int(config['num_epochs']				)
					__magic_name__							=    int(config['seed']				)
					__magic_name__							=    int(config['batch_size']				)

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

					set_seed(a_				)
					__magic_name__,	__magic_name__							=    get_dataloaders(a_					,a_				)
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__magic_name__							=    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).
					__magic_name__							=    model.to(accelerator.device				)

					# Instantiate optimizer
					__magic_name__							=    AdamW(params=model.parameters()					,lr=a_				)

					# Instantiate scheduler
					__magic_name__							=    get_linear_schedule_with_warmup(
					    optimizer=a_					,num_warmup_steps=1_0_0					,num_training_steps=(len(a_				) * 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.
					__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							=    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				)
															# 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(a_				):
																				__magic_name__							=    model(**a_				)
																				__magic_name__							=    output.loss
																				accelerator.backward(a_				)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

										model.eval()
										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():
																				__magic_name__							=    model(**a_				)
															__magic_name__							=    outputs.logits.argmax(dim=-1				)
															__magic_name__,	__magic_name__							=    accelerator.gather_for_metrics((predictions, batch['labels'])				)
															metric.add_batch(
															    predictions=a_					,references=a_					,)

										__magic_name__							=    metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F'epoch {epoch}:'					,a_				)




def 					_lowercase     (				)				->	Union[str, Any]:




					'''simple docstring'''







					__magic_name__							=    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.'					,)
					# New Code #
					parser.add_argument(
					    '--gradient_accumulation_steps'					,type=a_					,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.'				)
					__magic_name__							=    parser.parse_args()
					__magic_name__							=    {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
					training_function(a_					,a_				)


if __name__ == "__main__":
						main()







from typing import List, Optional, Union

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


A__						=							logging.get_logger(__name__)

A__						=							{
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[int]       				=  "time_series_transformer"
	_lowercase  :   Dict       				=  {
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	    "num_hidden_layers": "encoder_layers",
	}







	def __init__(   self: str  , __UpperCamelCase: Optional[int] = None  , __UpperCamelCase: Optional[int] = None  , __UpperCamelCase: str = "student_t"  , __UpperCamelCase: str = "nll"  , __UpperCamelCase: int = 1  , __UpperCamelCase: List[int] = [1, 2, 3, 4, 5, 6, 7]  , __UpperCamelCase: Optional[Union[str, bool]] = "mean"  , __UpperCamelCase: int = 0  , __UpperCamelCase: int = 0  , __UpperCamelCase: int = 0  , __UpperCamelCase: int = 0  , __UpperCamelCase: Optional[List[int]] = None  , __UpperCamelCase: Optional[List[int]] = None  , __UpperCamelCase: int = 32  , __UpperCamelCase: int = 32  , __UpperCamelCase: int = 2  , __UpperCamelCase: int = 2  , __UpperCamelCase: int = 2  , __UpperCamelCase: int = 2  , __UpperCamelCase: bool = True  , __UpperCamelCase: str = "gelu"  , __UpperCamelCase: int = 64  , __UpperCamelCase: float = 0.1  , __UpperCamelCase: float = 0.1  , __UpperCamelCase: float = 0.1  , __UpperCamelCase: float = 0.1  , __UpperCamelCase: float = 0.1  , __UpperCamelCase: int = 1_00  , __UpperCamelCase: float = 0.02  , __UpperCamelCase: Any=True  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						__magic_name__							=    prediction_length
						__magic_name__							=    context_length or prediction_length
						__magic_name__							=    distribution_output
						__magic_name__							=    loss
						__magic_name__							=    input_size
						__magic_name__							=    num_time_features
						__magic_name__							=    lags_sequence
						__magic_name__							=    scaling
						__magic_name__							=    num_dynamic_real_features
						__magic_name__							=    num_static_real_features
						__magic_name__							=    num_static_categorical_features
						if cardinality and num_static_categorical_features > 0:
											if len(__UpperCamelCase      ) != num_static_categorical_features:
																raise ValueError(
																    'The cardinality should be a list of the same length as `num_static_categorical_features`'      )
											__magic_name__							=    cardinality
						else:
											__magic_name__							=    [0]
						if embedding_dimension and num_static_categorical_features > 0:
											if len(__UpperCamelCase      ) != num_static_categorical_features:
																raise ValueError(
																    'The embedding dimension should be a list of the same length as `num_static_categorical_features`'      )
											__magic_name__							=    embedding_dimension
						else:
											__magic_name__							=    [min(50  , (cat + 1) // 2      ) for cat in self.cardinality]
						__magic_name__							=    num_parallel_samples

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

						__magic_name__							=    dropout
						__magic_name__							=    attention_dropout
						__magic_name__							=    activation_dropout
						__magic_name__							=    encoder_layerdrop
						__magic_name__							=    decoder_layerdrop

						__magic_name__							=    activation_function
						__magic_name__							=    init_std

						__magic_name__							=    use_cache

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







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						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
						)




