

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,
# specifically showcasing the checkpointing capability,
# 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 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 16					)							->   List[Any]:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     AutoTokenizer.from_pretrained("bert-base-cased"					)
     _lowerCAmelCase :       Tuple	   =     load_dataset("glue"		,				"mrpc"					)

     def tokenize_function(SCREAMING_SNAKE_CASE					):
          # max_length=None => use the model max length (it's actually the default)
          _lowerCAmelCase :       List[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():
          _lowerCAmelCase :       Union[str, 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 :       Tuple	   =     tokenized_datasets.rename_column("label"		,				"labels"					)

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

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

     # Instantiate dataloaders.
     _lowerCAmelCase :       Dict	   =     DataLoader(
         tokenized_datasets["train"]		,				shuffle=lowerCamelCase_		,				collate_fn=lowerCamelCase_		,				batch_size=lowerCamelCase_					)
     _lowerCAmelCase :       int	   =     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

  __UpperCAmelCase     					=  mocked_dataloaders  # noqa: F811







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[int]:

     """simple docstring"""







     if os.environ.get("TESTING_MOCKED_DATALOADERS"		,				lowerCamelCase_					) == "1":
          _lowerCAmelCase :       Optional[Any]	   =     2
     # Initialize accelerator
     _lowerCAmelCase :       Optional[Any]	   =     Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision					)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _lowerCAmelCase :       Optional[Any]	   =     config["""lr"""]
     _lowerCAmelCase :       Tuple	   =     int(config["num_epochs"]					)
     _lowerCAmelCase :       str	   =     int(config["seed"]					)
     _lowerCAmelCase :       Union[str, Any]	   =     int(config["batch_size"]					)

     # New Code #
     # Parse out whether we are saving every epoch or after a certain number of batches
     if hasattr(args.checkpointing_steps		,				"isdigit"					):
          if args.checkpointing_steps == "epoch":
               _lowerCAmelCase :       Any	   =     args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               _lowerCAmelCase :       Optional[int]	   =     int(args.checkpointing_steps					)
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''					)
     else:
          _lowerCAmelCase :       Optional[Any]	   =     None

     set_seed(lowerCamelCase_					)

     _lowerCAmelCase :       Optional[int]	   =     get_dataloaders(lowerCamelCase_		,				lowerCamelCase_					)
     _lowerCAmelCase :       Union[str, Any]	   =     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 :       Union[str, Any]	   =     batch_size // MAX_GPU_BATCH_SIZE
          _lowerCAmelCase :       List[Any]	   =     MAX_GPU_BATCH_SIZE

     # 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 :       Union[str, Any]	   =     model.to(accelerator.device					)

     # Instantiate optimizer
     _lowerCAmelCase :       Union[str, Any]	   =     AdamW(params=model.parameters()		,				lr=lowerCamelCase_					)

     # Instantiate scheduler
     _lowerCAmelCase :       int	   =     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.
     _lowerCAmelCase :       Tuple	   =     accelerator.prepare(
         lowerCamelCase_		,				lowerCamelCase_		,				lowerCamelCase_		,				lowerCamelCase_		,				lowerCamelCase_					)

     # New Code #
     # We need to keep track of how many total steps we have iterated over
     _lowerCAmelCase :       str	   =     0
     # We also need to keep track of the stating epoch so files are named properly
     _lowerCAmelCase :       Union[str, Any]	   =     0

     # We need to load the checkpoint back in before training here with `load_state`
     # The total number of epochs is adjusted based on where the state is being loaded from,
     # as we assume continuation of the same training script
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''					)
               accelerator.load_state(args.resume_from_checkpoint					)
               _lowerCAmelCase :       List[Any]	   =     os.path.basename(args.resume_from_checkpoint					)
          else:
               # Get the most recent checkpoint
               _lowerCAmelCase :       Optional[Any]	   =     [f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
               dirs.sort(key=os.path.getctime					)
               _lowerCAmelCase :       Tuple	   =     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          _lowerCAmelCase :       str	   =     os.path.splitext(lowerCamelCase_					)[0]

          if "epoch" in training_difference:
               _lowerCAmelCase :       Optional[int]	   =     int(training_difference.replace("epoch_"		,				""					)					) + 1
               _lowerCAmelCase :       Optional[int]	   =     None
          else:
               _lowerCAmelCase :       Dict	   =     int(training_difference.replace("step_"		,				""					)					)
               _lowerCAmelCase :       Optional[Any]	   =     resume_step // len(lowerCamelCase_					)
               resume_step -= starting_epoch * len(lowerCamelCase_					)

    # Now we train the model
     for epoch in range(lowerCamelCase_		,				lowerCamelCase_					):
          model.train()
          # New Code #
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               _lowerCAmelCase :       int	   =     accelerator.skip_first_batches(lowerCamelCase_		,				lowerCamelCase_					)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               _lowerCAmelCase :       Optional[Any]	   =     train_dataloader
          for step, batch in enumerate(lowerCamelCase_					):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device					)
               _lowerCAmelCase :       Optional[Any]	   =     model(**lowerCamelCase_					)
               _lowerCAmelCase :       Optional[Any]	   =     outputs.loss
               _lowerCAmelCase :       Optional[int]	   =     loss / gradient_accumulation_steps
               accelerator.backward(lowerCamelCase_					)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
               # New Code #
               overall_step += 1

               # New Code #
               # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
               # These are saved to folders named `step_{overall_step}`
               # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
               # If mixed precision was used, will also save a "scalar.bin" file
               if isinstance(lowerCamelCase_		,				lowerCamelCase_					):
                    _lowerCAmelCase :       Any	   =     f'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              _lowerCAmelCase :       Dict	   =     os.path.join(args.output_dir		,				lowerCamelCase_					)
                         accelerator.save_state(lowerCamelCase_					)

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

          _lowerCAmelCase :       Any	   =     metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''		,				lowerCamelCase_					)

          # New Code #
          # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
          # These are saved to folders named `epoch_{epoch}`
          # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
          # If mixed precision was used, will also save a "scalar.bin" file
          if checkpointing_steps == "epoch":
               _lowerCAmelCase :       Dict	   =     f'''epoch_{epoch}'''
               if args.output_dir is not None:
                    _lowerCAmelCase :       Optional[int]	   =     os.path.join(args.output_dir		,				lowerCamelCase_					)
               accelerator.save_state(lowerCamelCase_					)







def 						_snake_case		(     )							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Union[str, Any]	   =     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."					)
     parser.add_argument(
         "--checkpointing_steps"		,				type=lowerCamelCase_		,				default=lowerCamelCase_		,				help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."		,				)
     parser.add_argument(
         "--output_dir"		,				type=lowerCamelCase_		,				default="."		,				help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		,				)
     parser.add_argument(
         "--resume_from_checkpoint"		,				type=lowerCamelCase_		,				default=lowerCamelCase_		,				help="If the training should continue from a checkpoint folder."		,				)
     _lowerCAmelCase :       int	   =     parser.parse_args()
     _lowerCAmelCase :       str	   =     {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     training_function(lowerCamelCase_		,				lowerCamelCase_					)


if __name__ == "__main__":
  main()








"""simple docstring"""
import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property




class  _snake_case	(    unittest.TestCase							):






							@cached_property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""     )






							@cached_property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""     )






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       self.tokenizer_ab
												__a:    Union[str, Any]		     =       """ I am a small frog."""
												__a:    str		     =       tok([src_text]    ,			padding=snake_case__    ,			truncation=snake_case__     )["""input_ids"""]
												__a:    Optional[Any]		     =       tok.batch_decode(snake_case__    ,			skip_special_tokens=snake_case__    ,			clean_up_tokenization_spaces=snake_case__     )[0]
												assert src_text == decoded






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    str		     =       self.rust_tokenizer_ab
												__a:    Optional[Any]		     =       """ I am a small frog."""
												__a:    str		     =       tok([src_text]    ,			padding=snake_case__    ,			truncation=snake_case__     )["""input_ids"""]
												__a:    Union[str, Any]		     =       tok.batch_decode(snake_case__    ,			skip_special_tokens=snake_case__    ,			clean_up_tokenization_spaces=snake_case__     )[0]
												assert src_text == decoded






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												assert self.tokenizer_ab.add_prefix_space
												assert self.tokenizer_ab([""" Sam""", """Sam"""]     ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												assert self.rust_tokenizer_ab.add_prefix_space
												assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]     ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]
