


"""simple docstring"""


import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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      :int	    =		16
__lowerCamelCase      :int	    =		32




def   lowerCAmelCase		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Any			,				UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						Optional[int] = 16    )   ->      Tuple:
      lowerCamelCase					:					Tuple        =	AutoTokenizer.from_pretrained("""bert-base-cased"""    )
      lowerCamelCase					:					Any        =	DatasetDict(
          {
              """train""": dataset["""train"""].select(UpperCamelCase__    ),
              """validation""": dataset["""train"""].select(UpperCamelCase__    ),
              """test""": dataset["""validation"""],
          }    )

      def tokenize_function(UpperCamelCase__       :						Tuple    ):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase					:					Tuple        =	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():
            lowerCamelCase					:					str        =	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
      lowerCamelCase					:					int        =	tokenized_datasets.rename_column("""label"""			,				"""labels"""    )

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

            return tokenizer.pad(
                UpperCamelCase__			,				padding="""longest"""			,				max_length=UpperCamelCase__			,				pad_to_multiple_of=UpperCamelCase__			,				return_tensors="""pt"""			,				)

      # Instantiate dataloaders.
      lowerCamelCase					:					Optional[int]        =	DataLoader(
          tokenized_datasets["""train"""]			,				shuffle=UpperCamelCase__			,				collate_fn=UpperCamelCase__			,				batch_size=UpperCamelCase__    )
      lowerCamelCase					:					Optional[int]        =	DataLoader(
          tokenized_datasets["""validation"""]			,				shuffle=UpperCamelCase__			,				collate_fn=UpperCamelCase__			,				batch_size=UpperCamelCase__    )

      lowerCamelCase					:					Dict        =	DataLoader(
          tokenized_datasets["""test"""]			,				shuffle=UpperCamelCase__			,				collate_fn=UpperCamelCase__			,				batch_size=UpperCamelCase__    )

      return train_dataloader, eval_dataloader, test_dataloader




def   lowerCAmelCase		(     UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						Tuple    )   ->      Optional[Any]:
      lowerCamelCase					:					List[str]        =	[]
      # Download the dataset
      lowerCamelCase					:					str        =	load_dataset("""glue"""			,				"""mrpc"""    )
      # Create our splits
      lowerCamelCase					:					List[str]        =	StratifiedKFold(n_splits=int(args.num_folds    )    )
      # Initialize accelerator
      lowerCamelCase					:					int        =	Accelerator(cpu=args.cpu			,				mixed_precision=args.mixed_precision    )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCamelCase					:					Tuple        =	config["""lr"""]
      lowerCamelCase					:					Tuple        =	int(config["""num_epochs"""]    )
      lowerCamelCase					:					List[Any]        =	int(config["""seed"""]    )
      lowerCamelCase					:					List[Any]        =	int(config["""batch_size"""]    )

      lowerCamelCase					:					str        =	evaluate.load("""glue"""			,				"""mrpc"""    )

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

      set_seed(UpperCamelCase__    )

      # New Code #
      # Create our folds:
      lowerCamelCase					:					Any        =	kfold.split(np.zeros(datasets["""train"""].num_rows    )			,				datasets["""train"""]["""label"""]    )
      lowerCamelCase					:					Tuple        =	[]
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase__    ):
            lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	get_fold_dataloaders(
                UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				)
            # 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=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).
            lowerCamelCase					:					Optional[int]        =	model.to(accelerator.device    )

            # Instantiate optimizer
            lowerCamelCase					:					Tuple        =	AdamW(params=model.parameters()			,				lr=UpperCamelCase__    )

            # Instantiate scheduler
            lowerCamelCase					:					Any        =	get_linear_schedule_with_warmup(
                optimizer=UpperCamelCase__			,				num_warmup_steps=100			,				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.
            lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	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    )
                        lowerCamelCase					:					int        =	model(**UpperCamelCase__    )
                        lowerCamelCase					:					Any        =	outputs.loss
                        lowerCamelCase					:					Tuple        =	loss / gradient_accumulation_steps
                        accelerator.backward(UpperCamelCase__    )
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                  model.eval()
                  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():
                              lowerCamelCase					:					int        =	model(**UpperCamelCase__    )
                        lowerCamelCase					:					Union[str, Any]        =	outputs.logits.argmax(dim=-1    )
                        lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
                        metric.add_batch(
                            predictions=UpperCamelCase__			,				references=UpperCamelCase__			,				)

                  lowerCamelCase					:					Union[str, Any]        =	metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(F'epoch {epoch}:'			,				UpperCamelCase__    )

            # New Code #
            # We also run predictions on the test set at the very end
            lowerCamelCase					:					int        =	[]
            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():
                        lowerCamelCase					:					List[Any]        =	model(**UpperCamelCase__    )
                  lowerCamelCase					:					Dict        =	outputs.logits
                  lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
                  fold_predictions.append(predictions.cpu()    )
                  if i == 0:
                        # We need all of the test predictions
                        test_references.append(references.cpu()    )
        # Use accelerator.print to print only on the main process.
            test_predictions.append(torch.cat(UpperCamelCase__			,				dim=0    )    )
            # We now need to release all our memory and get rid of the current model, optimizer, etc
            accelerator.free_memory()
      # New Code #
      # Finally we check the accuracy of our folded results:
      lowerCamelCase					:					int        =	torch.cat(UpperCamelCase__			,				dim=0    )
      lowerCamelCase					:					Optional[Any]        =	torch.stack(UpperCamelCase__			,				dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
      lowerCamelCase					:					int        =	metric.compute(predictions=UpperCamelCase__			,				references=UpperCamelCase__    )
      accelerator.print("""Average test metrics from all folds:"""			,				UpperCamelCase__    )




def   lowerCAmelCase		(     )   ->      Any:
      lowerCamelCase					:					List[Any]        =	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."""    )
      # New Code #
      parser.add_argument("""--num_folds"""			,				type=UpperCamelCase__			,				default=3			,				help="""The number of splits to perform across the dataset"""    )
      lowerCamelCase					:					List[Any]        =	parser.parse_args()
      lowerCamelCase					:					Tuple        =	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(UpperCamelCase__			,				UpperCamelCase__    )


if __name__ == "__main__":
    main()








"""simple docstring"""


from typing import List, Optional, Union

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


__lowerCamelCase      :str	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    '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 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					List[Any]													='''time_series_transformer'''
     snake_case__	:					List[Any]													={
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
         '''num_hidden_layers''': '''encoder_layers''',
     }

     def __init__(	self:						List[str]     ,		__a:						Optional[int] = None     ,		__a:						Optional[int] = None     ,		__a:						str = "student_t"     ,		__a:						str = "nll"     ,		__a:						int = 1     ,		__a:						List[int] = [1, 2, 3, 4, 5, 6, 7]     ,		__a:						Optional[Union[str, bool]] = "mean"     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						int = 0     ,		__a:						Optional[List[int]] = None     ,		__a:						Optional[List[int]] = None     ,		__a:						int = 32     ,		__a:						int = 32     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						int = 2     ,		__a:						bool = True     ,		__a:						str = "gelu"     ,		__a:						int = 64     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						float = 0.1     ,		__a:						int = 100     ,		__a:						float = 0.02     ,		__a:						Tuple=True     ,		**__a:						str     ,		)->    Any:
           # time series specific configuration
           lowerCamelCase					:					str        =	prediction_length
           lowerCamelCase					:					Optional[Any]        =	context_length or prediction_length
           lowerCamelCase					:					Tuple        =	distribution_output
           lowerCamelCase					:					Any        =	loss
           lowerCamelCase					:					List[Any]        =	input_size
           lowerCamelCase					:					int        =	num_time_features
           lowerCamelCase					:					Dict        =	lags_sequence
           lowerCamelCase					:					Optional[int]        =	scaling
           lowerCamelCase					:					int        =	num_dynamic_real_features
           lowerCamelCase					:					Tuple        =	num_static_real_features
           lowerCamelCase					:					Any        =	num_static_categorical_features
           if cardinality and num_static_categorical_features > 0:
                 if len(__a	) != num_static_categorical_features:
                       raise ValueError(
                           """The cardinality should be a list of the same length as `num_static_categorical_features`"""	)
                 lowerCamelCase					:					int        =	cardinality
           else:
                 lowerCamelCase					:					Dict        =	[0]
           if embedding_dimension and num_static_categorical_features > 0:
                 if len(__a	) != num_static_categorical_features:
                       raise ValueError(
                           """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""	)
                 lowerCamelCase					:					str        =	embedding_dimension
           else:
                 lowerCamelCase					:					str        =	[min(50     ,		(cat + 1) // 2	) for cat in self.cardinality]
           lowerCamelCase					:					Any        =	num_parallel_samples

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

           lowerCamelCase					:					Optional[int]        =	dropout
           lowerCamelCase					:					List[str]        =	attention_dropout
           lowerCamelCase					:					Tuple        =	activation_dropout
           lowerCamelCase					:					Optional[int]        =	encoder_layerdrop
           lowerCamelCase					:					int        =	decoder_layerdrop

           lowerCamelCase					:					Optional[int]        =	activation_function
           lowerCamelCase					:					Optional[Any]        =	init_std

           lowerCamelCase					:					Optional[Any]        =	use_cache

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







     @property
     def 		a__						(	self:						int	)->    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
           )




