





"""simple docstring"""





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


_UpperCamelCase							=      16
_UpperCamelCase							=      32



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Accelerator    ,						SCREAMING_SNAKE_CASE       :							int = 16   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =AutoTokenizer.from_pretrained('''bert-base-cased'''   )
							__lowerCamelCase      :      List[Any]         =load_dataset('''glue'''    ,						'''mrpc'''   )

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

							def collate_fn(SCREAMING_SNAKE_CASE       :							str   ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														__lowerCamelCase      :      List[Any]         =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      :      Dict         =16
														elif accelerator.mixed_precision != "no":
																					__lowerCamelCase      :      int         =8
														else:
																					__lowerCamelCase      :      str         =None

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

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

							return train_dataloader, eval_dataloader



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







							__lowerCamelCase      :      Union[str, 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      :      Union[str, Any]         =config['''lr''']
							__lowerCamelCase      :      int         =int(config['''num_epochs''']   )
							__lowerCamelCase      :      List[Any]         =int(config['''seed''']   )
							__lowerCamelCase      :      Optional[Any]         =int(config['''batch_size''']   )

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

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

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

							# 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      :      str         =model.to(accelerator.device   )
							# Instantiate optimizer
							__lowerCamelCase      :      Optional[Any]         =AdamW(params=model.parameters()    ,						lr=SCREAMING_SNAKE_CASE   )

							# Instantiate scheduler
							__lowerCamelCase      :      Union[str, Any]         =get_linear_schedule_with_warmup(
							    optimizer=SCREAMING_SNAKE_CASE    ,						num_warmup_steps=100    ,						num_training_steps=(len(SCREAMING_SNAKE_CASE   ) * 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      :      List[str]         =accelerator.prepare(
							    SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

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

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

														__lowerCamelCase      :      Optional[int]         =metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'epoch {epoch}:'    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      Dict         =argparse.ArgumentParser(description='''Simple example of training script.'''   )
							parser.add_argument(
							    '''--mixed_precision'''    ,						type=SCREAMING_SNAKE_CASE    ,						default=SCREAMING_SNAKE_CASE    ,						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      :      Tuple         =parser.parse_args()
							__lowerCamelCase      :      Optional[Any]         ={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
							main()











"""simple docstring"""





from typing import List, Optional, Union

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


_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {
    '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 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Union[str, Any]	 =		"""time_series_transformer"""
				__snake_case   :       Any	 =		{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				    """num_hidden_layers""": """encoder_layers""",
				}




				def __init__(     self  :List[Any]						,    __lowercase  :Optional[int] = None						,    __lowercase  :Optional[int] = None						,    __lowercase  :str = "student_t"						,    __lowercase  :str = "nll"						,    __lowercase  :int = 1						,    __lowercase  :List[int] = [1, 2, 3, 4, 5, 6, 7]						,    __lowercase  :Optional[Union[str, bool]] = "mean"						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :Optional[List[int]] = None						,    __lowercase  :Optional[List[int]] = None						,    __lowercase  :int = 32						,    __lowercase  :int = 32						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :bool = True						,    __lowercase  :str = "gelu"						,    __lowercase  :int = 64						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :int = 100						,    __lowercase  :float = 0.02						,    __lowercase  :int=True						,    **__lowercase  :Optional[Any]						,    ):
											# time series specific configuration
											__lowerCamelCase      :      Tuple         =prediction_length
											__lowerCamelCase      :      List[Any]         =context_length or prediction_length
											__lowerCamelCase      :      Dict         =distribution_output
											__lowerCamelCase      :      str         =loss
											__lowerCamelCase      :      Tuple         =input_size
											__lowerCamelCase      :      int         =num_time_features
											__lowerCamelCase      :      int         =lags_sequence
											__lowerCamelCase      :      Optional[int]         =scaling
											__lowerCamelCase      :      str         =num_dynamic_real_features
											__lowerCamelCase      :      Optional[Any]         =num_static_real_features
											__lowerCamelCase      :      List[Any]         =num_static_categorical_features
											if cardinality 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`'''			)
																		__lowerCamelCase      :      Optional[int]         =cardinality
											else:
																		__lowerCamelCase      :      str         =[0]
											if embedding_dimension 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`'''			)
																		__lowerCamelCase      :      int         =embedding_dimension
											else:
																		__lowerCamelCase      :      Optional[int]         =[min(50						,    (cat + 1) // 2			) for cat in self.cardinality]
											__lowerCamelCase      :      List[str]         =num_parallel_samples

											# Transformer architecture configuration
											__lowerCamelCase      :      str         =input_size * len(__lowercase			) + self._number_of_features
											__lowerCamelCase      :      Union[str, Any]         =d_model
											__lowerCamelCase      :      int         =encoder_attention_heads
											__lowerCamelCase      :      int         =decoder_attention_heads
											__lowerCamelCase      :      Dict         =encoder_ffn_dim
											__lowerCamelCase      :      int         =decoder_ffn_dim
											__lowerCamelCase      :      List[Any]         =encoder_layers
											__lowerCamelCase      :      int         =decoder_layers

											__lowerCamelCase      :      Union[str, Any]         =dropout
											__lowerCamelCase      :      Optional[Any]         =attention_dropout
											__lowerCamelCase      :      List[str]         =activation_dropout
											__lowerCamelCase      :      List[str]         =encoder_layerdrop
											__lowerCamelCase      :      int         =decoder_layerdrop

											__lowerCamelCase      :      Tuple         =activation_function
											__lowerCamelCase      :      str         =init_std

											__lowerCamelCase      :      Dict         =use_cache

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




				@property
				def 					__lowercase  (     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
											)




