'''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
#
########################################################################


__UpperCAmelCase												=1_6
__UpperCAmelCase												=3_2






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = 16    )     ->   str:
    __lowerCamelCase          =  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
    __lowerCamelCase          =  DatasetDict(
        {
            '''train''': dataset['''train'''].select(UpperCamelCase__    ),
            '''validation''': dataset['''train'''].select(UpperCamelCase__    ),
            '''test''': dataset['''validation'''],
        }    )

    def tokenize_function(UpperCamelCase__    ):
        # max_length=None => use the model max length (it's actually the default)
        __lowerCamelCase          =  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          =  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          =  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.
        __lowerCamelCase          =  1_28 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          =  16
        elif accelerator.mixed_precision != "no":
            __lowerCamelCase          =  8
        else:
            __lowerCamelCase          =  None

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

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

    __lowerCamelCase          =  DataLoader(
        tokenized_datasets['''test''']    ,  shuffle=UpperCamelCase__    ,  collate_fn=UpperCamelCase__    ,  batch_size=UpperCamelCase__    )

    return train_dataloader, eval_dataloader, test_dataloader






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   int:
    # New Code #
    __lowerCamelCase          =  []
    # Download the dataset
    __lowerCamelCase          =  load_dataset('''glue'''    ,  '''mrpc'''    )
    # Create our splits
    __lowerCamelCase          =  StratifiedKFold(n_splits=int(args.num_folds    )    )
    # Initialize accelerator
    __lowerCamelCase          =  Accelerator(cpu=args.cpu    ,  mixed_precision=args.mixed_precision    )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __lowerCamelCase          =  config['''lr''']
    __lowerCamelCase          =  int(config['''num_epochs''']    )
    __lowerCamelCase          =  int(config['''seed''']    )
    __lowerCamelCase          =  int(config['''batch_size''']    )

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

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

    set_seed(UpperCamelCase__    )

    # New Code #
    # Create our folds:
    __lowerCamelCase          =  kfold.split(np.zeros(datasets['''train'''].num_rows    )    ,  datasets['''train''']['''label''']    )
    __lowerCamelCase          =  []
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase__    ):
        __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  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          =  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          =  model.to(accelerator.device    )

        # Instantiate optimizer
        __lowerCamelCase          =  AdamW(params=model.parameters()    ,  lr=UpperCamelCase__    )

        # Instantiate scheduler
        __lowerCamelCase          =  get_linear_schedule_with_warmup(
            optimizer=UpperCamelCase__    ,  num_warmup_steps=1_00    ,  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          =  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          =  model(**UpperCamelCase__    )
                __lowerCamelCase          =  outputs.loss
                __lowerCamelCase          =  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          =  model(**UpperCamelCase__    )
                __lowerCamelCase          =  outputs.logits.argmax(dim=-1    )
                __lowerCamelCase     ,  __lowerCamelCase          =  accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
                metric.add_batch(
                    predictions=UpperCamelCase__    ,  references=UpperCamelCase__    ,  )

            __lowerCamelCase          =  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          =  []
        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          =  model(**UpperCamelCase__    )
            __lowerCamelCase          =  outputs.logits
            __lowerCamelCase     ,  __lowerCamelCase          =  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          =  torch.cat(UpperCamelCase__    ,  dim=0    )
    __lowerCamelCase          =  torch.stack(UpperCamelCase__    ,  dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
    __lowerCamelCase          =  metric.compute(predictions=UpperCamelCase__    ,  references=UpperCamelCase__    )
    accelerator.print('''Average test metrics from all folds:'''    ,  UpperCamelCase__    )






def __lowerCAmelCase			(							)     ->   Optional[Any]:
    __lowerCamelCase          =  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          =  parser.parse_args()
    __lowerCamelCase          =  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(UpperCamelCase__    ,  UpperCamelCase__    )


if __name__ == "__main__":
  main()

'''simple docstring'''




import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  __UpperCAmelCase												=None


__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase												={
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__UpperCAmelCase												={
    "t5-small": 5_1_2,
    "t5-base": 5_1_2,
    "t5-large": 5_1_2,
    "t5-3b": 5_1_2,
    "t5-11b": 5_1_2,
}




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=VOCAB_FILES_NAMES
     lowerCamelCase  :					Optional[Any]							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase  :					Dict							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase  :					int							=["input_ids", "attention_mask"]
     lowerCamelCase  :					Tuple							=TaTokenizer

     lowerCamelCase  :					List[int]							=[]






     def __init__(			self		:		int  ,    a		:		List[str]=None  ,    a		:		List[str]=None  ,    a		:		Dict="</s>"  ,    a		:		Optional[int]="<unk>"  ,    a		:		Any="<pad>"  ,    a		:		Optional[Any]=1_00  ,    a		:		List[Any]=None  ,    **a		:		Union[str, Any]  ,    ):





         """simple docstring"""



         if extra_ids > 0 and additional_special_tokens is None:
             __lowerCamelCase          =  [f"""<extra_id_{i}>""" for i in range(a   )]
         elif extra_ids > 0 and additional_special_tokens is not None:
             # Check that we have the right number of extra special tokens
             __lowerCamelCase          =  len(set(filter(lambda a   : bool('''extra_id_''' in str(a   )   )  ,    a   )   )   )
             if extra_tokens != extra_ids:
                 raise ValueError(
                     f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                     ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                     ''' tokens'''   )

         super().__init__(
             a  ,    tokenizer_file=a  ,    eos_token=a  ,    unk_token=a  ,    pad_token=a  ,    extra_ids=a  ,    additional_special_tokens=a  ,    **a  ,    )

         __lowerCamelCase          =  vocab_file
         __lowerCamelCase          =  False if not self.vocab_file else True
         __lowerCamelCase          =  extra_ids






     @staticmethod
     def        SCREAMING_SNAKE_CASE__       (			a		:		Optional[int]  ,    a		:		List[str]  ,    a		:		Union[str, Any]   ):





         """simple docstring"""



         if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
             __lowerCamelCase          =  TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
             if init_max_model_length is not None and init_max_model_length != max_model_length:
                 return init_max_model_length
             elif init_max_model_length is None:
                 warnings.warn(
                     '''This tokenizer was incorrectly instantiated with a model max length of'''
                     f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                     ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                     ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                     f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                     f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                     f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                     ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                     ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''  ,    a  ,    )

         return max_model_length






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		str  ,    a		:		Optional[str] = None   ):





         """simple docstring"""



         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                 '''tokenizer.'''   )

         if not os.path.isdir(a   ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
             return
         __lowerCamelCase          =  os.path.join(
             a  ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

         if os.path.abspath(self.vocab_file   ) != os.path.abspath(a   ):
             copyfile(self.vocab_file  ,    a   )
             logger.info(f"""Copy vocab file to {out_vocab_file}"""   )

         return (out_vocab_file,)






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a
         else:
             __lowerCamelCase          =  token_ids_a + [self.eos_token_id]
             return self.prefix_tokens + token_ids_a + token_ids_a






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  [self.eos_token_id]

         if token_ids_a is None:
             return len(token_ids_a + eos   ) * [0]
         return len(token_ids_a + eos + token_ids_a + eos   ) * [0]






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         return list(
             set(filter(lambda a   : bool(re.search(R'''<extra_id_\d+>'''  ,    a   )   ) is not None  ,    self.additional_special_tokens   )   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         return [self.convert_tokens_to_ids(a   ) for token in self.get_sentinel_tokens()]
