

"""simple docstring"""

import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__ = 1_6   )			->						str:
 lowerCAmelCase__   :				Optional[Any] 			=				AutoTokenizer.from_pretrained("bert-base-cased"   )
 lowerCAmelCase__   :				Optional[Any] 			=				load_dataset("glue"					,					"mrpc"   )

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

 def collate_fn(lowercase__   ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  lowerCAmelCase__   :				Tuple 			=				1_2_8 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__   :				Union[str, Any] 			=				1_6
  elif accelerator.mixed_precision != "no":
   lowerCAmelCase__   :				Dict 			=				8
  else:
   lowerCAmelCase__   :				Union[str, Any] 			=				None

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

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

 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

    _UpperCamelCase		     =    mocked_dataloaders  # noqa: F811



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						List[str]:
 # For testing only
 if os.environ.get("TESTING_MOCKED_DATALOADERS"					,					lowercase__   ) == "1":
  lowerCAmelCase__   :				Optional[int] 			=				2
 # Initialize accelerator
 lowerCAmelCase__   :				Optional[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__   :				Union[str, Any] 			=				config["lr"]
 lowerCAmelCase__   :				Optional[Any] 			=				int(config["num_epochs"]   )
 lowerCAmelCase__   :				List[str] 			=				int(config["seed"]   )
 lowerCAmelCase__   :				List[str] 			=				int(config["batch_size"]   )

 lowerCAmelCase__   :				int 			=				evaluate.load("glue"					,					"mrpc"   )

 # New Code #
 # We now can define an inner training loop function. It should take a batch size as the only parameter,
 # and build the dataloaders in there.
 # It also gets our decorator
 @find_executable_batch_size(starting_batch_size=lowercase__   )
 def inner_training_loop(lowercase__   ):
  # And now just move everything below under this function
  # We need to bring in the Accelerator object from earlier
  nonlocal accelerator
  # And reset all of its attributes that could hold onto any memory:
  accelerator.free_memory()

  # Then we can declare the model, optimizer, and everything else:
  set_seed(lowercase__   )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCAmelCase__   :				Optional[int] 			=				AutoModelForSequenceClassification.from_pretrained("bert-base-cased"					,					return_dict=lowercase__   )

  # 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__   :				Dict 			=				model.to(accelerator.device   )

  # Instantiate optimizer
  lowerCAmelCase__   :				Optional[Any] 			=				AdamW(params=model.parameters()					,					lr=lowercase__   )
  lowerCAmelCase__			,						lowerCAmelCase__   :				Any 			=				get_dataloaders(lowercase__					,					lowercase__   )

  # Instantiate scheduler
  lowerCAmelCase__   :				Dict 			=				get_linear_schedule_with_warmup(
      optimizer=lowercase__					,					num_warmup_steps=1_0_0					,					num_training_steps=(len(lowercase__   ) * 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(
      lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )

  # Now we train the model
  for epoch in range(lowercase__   ):
   model.train()
   for step, batch in enumerate(lowercase__   ):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device   )
    lowerCAmelCase__   :				Optional[int] 			=				model(**lowercase__   )
    lowerCAmelCase__   :				int 			=				outputs.loss
    accelerator.backward(lowercase__   )
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
 inner_training_loop()



def       SCREAMING_SNAKE_CASE							( )			->						List[str]:
 lowerCAmelCase__   :				Optional[int] 			=				argparse.ArgumentParser(description="Simple example of training script."   )
 parser.add_argument(
     "--mixed_precision"					,					type=lowercase__					,					default=lowercase__					,					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__   :				int 			=				{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
 training_function(lowercase__					,					lowercase__   )


if __name__ == "__main__":
    main()









"""simple docstring"""

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__a					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizer
       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizerFast
       __UpperCamelCase					:      str    =						True
       __UpperCamelCase					:      Any    =						True






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        super().setUp()

        lowerCAmelCase__   :				Tuple 			=				[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        lowerCAmelCase__   :				Dict 			=				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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				"UNwant\u00E9d,running"
        lowerCAmelCase__   :				Union[str, Any] 			=				"unwanted, running"
        return input_text, output_text






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.tokenizer_class(self.vocab_file   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        if not self.test_rust_tokenizer:
         return

        lowerCAmelCase__   :				int 			=				self.get_tokenizer()
        lowerCAmelCase__   :				List[str] 			=				self.get_rust_tokenizer()

        lowerCAmelCase__   :				Optional[Any] 			=				"I was born in 92000, and this is falsé."

        lowerCAmelCase__   :				int 			=				tokenizer.tokenize(snake_case   )
        lowerCAmelCase__   :				int 			=				rust_tokenizer.tokenize(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				str 			=				tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        lowerCAmelCase__   :				List[str] 			=				rust_tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				Tuple 			=				self.get_rust_tokenizer()
        lowerCAmelCase__   :				Tuple 			=				tokenizer.encode(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				rust_tokenizer.encode(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )






