
"""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
#
########################################################################


_a		       =							16
_a		       =							32







def 	__a     (		__lowerCamelCase,		__lowerCamelCase = 16  ):
   UpperCAmelCase_					:					Any			     =    AutoTokenizer.from_pretrained("bert-base-cased"  )
   UpperCAmelCase_					:					Optional[Any]			     =    load_dataset("glue",		"mrpc"  )

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

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

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

   # Instantiate dataloaders.
   UpperCAmelCase_					:					Optional[int]			     =    DataLoader(
       tokenized_datasets["train"],		shuffle=__lowerCamelCase,		collate_fn=__lowerCamelCase,		batch_size=__lowerCamelCase,		drop_last=__lowerCamelCase  )
   UpperCAmelCase_					:					int			     =    DataLoader(
       tokenized_datasets["validation"],		shuffle=__lowerCamelCase,		collate_fn=__lowerCamelCase,		batch_size=__lowerCamelCase,		drop_last=(accelerator.mixed_precision == "fp8"),		)

   return train_dataloader, eval_dataloader







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   # Initialize accelerator
   UpperCAmelCase_					:					Any			     =    Accelerator(cpu=args.cpu,		mixed_precision=args.mixed_precision  )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCAmelCase_					:					str			     =    config["lr"]
   UpperCAmelCase_					:					str			     =    int(config["num_epochs"]  )
   UpperCAmelCase_					:					Dict			     =    int(config["seed"]  )
   UpperCAmelCase_					:					str			     =    int(config["batch_size"]  )

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

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

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

   # 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).
   UpperCAmelCase_					:					Dict			     =    model.to(accelerator.device  )
   # Instantiate optimizer
   UpperCAmelCase_					:					str			     =    AdamW(params=model.parameters(),		lr=__lowerCamelCase  )

   # Instantiate scheduler
   UpperCAmelCase_					:					Any			     =    get_linear_schedule_with_warmup(
       optimizer=__lowerCamelCase,		num_warmup_steps=100,		num_training_steps=(len(__lowerCamelCase  ) * 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.

   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Union[str, Any]			     =    accelerator.prepare(
       __lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )

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

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

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







def 	__a     (		):
   UpperCAmelCase_					:					Optional[int]			     =    argparse.ArgumentParser(description="Simple example of training script."  )
   parser.add_argument(
       "--mixed_precision",		type=__lowerCamelCase,		default=__lowerCamelCase,		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."  )
   UpperCAmelCase_					:					List[Any]			     =    parser.parse_args()
   UpperCAmelCase_					:					Optional[int]			     =    {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(__lowerCamelCase,		__lowerCamelCase  )


if __name__ == "__main__":
    main()




"""simple docstring"""





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

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


if is_sentencepiece_available():
    from .tokenization_fnet import FNetTokenizer
else:
    _a		       =							None

_a		       =							logging.get_logger(__name__)
_a		       =							{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_a		       =							{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

_a		       =							{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

_a		       =							'▁'


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[str]       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						Tuple       =		PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						Any       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		["""input_ids""", """token_type_ids"""]
  SCREAMING_SNAKE_CASE__			:						Tuple       =		FNetTokenizer







  def __init__(		self				,   lowercase_=None				,   lowercase_=None				,   lowercase_=False				,   lowercase_=True				,   lowercase_=True				,   lowercase_="<unk>"				,   lowercase_="[SEP]"				,   lowercase_="<pad>"				,   lowercase_="[CLS]"				,   lowercase_="[MASK]"				,   **lowercase_				,   ):

     """simple docstring"""
     # Mask token behave like a normal word, i.e. include the space before it and
     # is included in the raw text, there should be a match in a non-normalized sentence.
     UpperCAmelCase_					:					int			     =    (
         AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_				,   normalized=lowercase_  )
         if isinstance(lowercase_				,   lowercase_  )
         else mask_token
     )

     super().__init__(
         lowercase_				,   tokenizer_file=lowercase_				,   do_lower_case=lowercase_				,   remove_space=lowercase_				,   keep_accents=lowercase_				,   unk_token=lowercase_				,   sep_token=lowercase_				,   pad_token=lowercase_				,   cls_token=lowercase_				,   mask_token=lowercase_				,   **lowercase_				,   )

     UpperCAmelCase_					:					Any			     =    do_lower_case
     UpperCAmelCase_					:					Tuple			     =    remove_space
     UpperCAmelCase_					:					str			     =    keep_accents
     UpperCAmelCase_					:					Any			     =    vocab_file
     UpperCAmelCase_					:					List[Any]			     =    False if not self.vocab_file else True







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [self.cls_token_id]
     if token_ids_a is None:
        return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Any			     =    [self.cls_token_id]

     if token_ids_a is None:
        return len(cls + token_ids_a + sep  ) * [0]
     return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     if not os.path.isdir(lowercase_  ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
        return
     UpperCAmelCase_					:					List[str]			     =    os.path.join(
         lowercase_				,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

     if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowercase_  ):
        copyfile(self.vocab_file				,   lowercase_  )

     return (out_vocab_file,)


