


"""simple docstring"""






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


_lowerCAmelCase							:    str					   =	16
_lowerCAmelCase							:    Dict					   =	32







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase = 16       )    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				List[Any]							=     AutoTokenizer.from_pretrained("bert-base-cased"       )
   _lowerCamelCase   :				List[Any]							=     load_dataset("glue"			,		"mrpc"       )

   def tokenize_function(_lowerCamelCase       ):
      # max_length=None => use the model max length (it's actually the default)
      _lowerCamelCase   :				Dict							=     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():
      _lowerCamelCase   :				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
   _lowerCamelCase   :				List[Any]							=     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.
      _lowerCamelCase   :				Union[str, 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   :				str							=     16
      elif accelerator.mixed_precision != "no":
         _lowerCamelCase   :				Union[str, Any]							=     8
      else:
         _lowerCamelCase   :				Optional[int]							=     None

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

   # Instantiate dataloaders.
   _lowerCamelCase   :				Any							=     DataLoader(
       tokenized_datasets["train"]			,		shuffle=_lowerCamelCase			,		collate_fn=_lowerCamelCase			,		batch_size=_lowerCamelCase       )
   _lowerCamelCase   :				List[str]							=     DataLoader(
       tokenized_datasets["validation"]			,		shuffle=_lowerCamelCase			,		collate_fn=_lowerCamelCase			,		batch_size=_lowerCamelCase       )

   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

  _lowerCAmelCase							:    Dict					   =	mocked_dataloaders  # noqa: F811







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     List[Any]:
   '''simple docstring'''





   if os.environ.get("TESTING_MOCKED_DATALOADERS"			,		_lowerCamelCase       ) == "1":
      _lowerCamelCase   :				List[str]							=     2
   # New Code #
   _lowerCamelCase   :				Any							=     int(args.gradient_accumulation_steps       )
   # Initialize accelerator
   _lowerCamelCase   :				Dict							=     Accelerator(
       cpu=args.cpu			,		mixed_precision=args.mixed_precision			,		gradient_accumulation_steps=_lowerCamelCase       )
   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
   _lowerCamelCase   :				Tuple							=     config["lr"]
   _lowerCamelCase   :				int							=     int(config["num_epochs"]       )
   _lowerCamelCase   :				Any							=     int(config["seed"]       )
   _lowerCamelCase   :				Optional[int]							=     int(config["batch_size"]       )

   _lowerCamelCase   :				Optional[int]							=     evaluate.load("glue"			,		"mrpc"       )

   set_seed(_lowerCamelCase       )
   _lowerCamelCase,					_lowerCamelCase   :				Union[str, Any]							=     get_dataloaders(_lowerCamelCase			,		_lowerCamelCase       )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _lowerCamelCase   :				Tuple							=     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).
   _lowerCamelCase   :				List[str]							=     model.to(accelerator.device       )

   # Instantiate optimizer
   _lowerCamelCase   :				Any							=     AdamW(params=model.parameters()			,		lr=_lowerCamelCase       )

   # Instantiate scheduler
   _lowerCamelCase   :				List[Any]							=     get_linear_schedule_with_warmup(
       optimizer=_lowerCamelCase			,		num_warmup_steps=100			,		num_training_steps=(len(_lowerCamelCase       ) * 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.
   _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				Optional[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       )
         # 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(_lowerCamelCase       ):
            _lowerCamelCase   :				List[str]							=     model(**_lowerCamelCase       )
            _lowerCamelCase   :				Dict							=     output.loss
            accelerator.backward(_lowerCamelCase       )
            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():
            _lowerCamelCase   :				str							=     model(**_lowerCamelCase       )
         _lowerCamelCase   :				Any							=     outputs.logits.argmax(dim=-1       )
         _lowerCamelCase,					_lowerCamelCase   :				Dict							=     accelerator.gather_for_metrics((predictions, batch["labels"])       )
         metric.add_batch(
             predictions=_lowerCamelCase			,		references=_lowerCamelCase			,		)

      _lowerCamelCase   :				int							=     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""			,		_lowerCamelCase       )







def        lowerCamelCase_(		)    ->     Tuple:
   '''simple docstring'''





   _lowerCamelCase   :				List[str]							=     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."			,		)
   # New Code #
   parser.add_argument(
       "--gradient_accumulation_steps"			,		type=_lowerCamelCase			,		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."       )
   _lowerCamelCase   :				Any							=     parser.parse_args()
   _lowerCamelCase   :				Dict							=     {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(_lowerCamelCase			,		_lowerCamelCase       )


if __name__ == "__main__":
  main()


import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						__magic_name__					(							snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:List[Any]										=      LayoutLMTokenizer
     UpperCamelCase_	:Dict										=      LayoutLMTokenizerFast
     UpperCamelCase_	:List[str]										=      True
     UpperCamelCase_	:Dict										=      True






     def 	UpperCAmelCase_				(				self	)->     str:
         super().setUp()

         UpperCamelCase_			  =						[
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["vocab_file"]	)
         with open(self.vocab_file		,						"w"		,						encoding="utf-8"	) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     List[str]:
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						"UNwant\u00E9d,running"
         UpperCamelCase_			  =						"unwanted, running"
         return input_text, output_text






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						self.tokenizer_class(self.vocab_file	)

         UpperCamelCase_			  =						tokenizer.tokenize("UNwant\u00E9d,running"	)
         self.assertListEqual(_lowercase		,						["un", "##want", "##ed", ",", "runn", "##ing"]	)
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase	)		,						[7, 4, 5, 10, 8, 9]	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         pass
