


import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


SCREAMING_SNAKE_CASE						:List[Any]			=  16
SCREAMING_SNAKE_CASE						:Tuple			=  32



def       _lowerCAmelCase    (				lowerCAmelCase_				:Accelerator		,    lowerCAmelCase_				:int = 16		,    lowerCAmelCase_				:str = "bert-base-cased"				)->int:
   '''simple docstring'''






   snake_case_            =       AutoTokenizer.from_pretrained(lowerCAmelCase_				)
   snake_case_            =       load_dataset("glue"		,    "mrpc"				)

   def tokenize_function(lowerCAmelCase_				:Optional[Any]				):
      # max_length=None => use the model max length (it's actually the default)
      snake_case_            =       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
   snake_case_            =       datasets.map(
       lowerCAmelCase_		,    batched=lowerCAmelCase_		,    remove_columns=["idx", "sentence1", "sentence2"]		,    load_from_cache_file=lowerCAmelCase_				)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   snake_case_            =       tokenized_datasets.rename_column("label"		,    "labels"				)

   def collate_fn(lowerCAmelCase_				:Union[str, Any]				):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(lowerCAmelCase_		,    padding="max_length"		,    max_length=128		,    return_tensors="pt"				)
      return tokenizer.pad(lowerCAmelCase_		,    padding="longest"		,    return_tensors="pt"				)

   # Instantiate dataloaders.
   snake_case_            =       DataLoader(
       tokenized_datasets["train"]		,    shuffle=lowerCAmelCase_		,    collate_fn=lowerCAmelCase_		,    batch_size=lowerCAmelCase_				)
   snake_case_            =       DataLoader(
       tokenized_datasets["validation"]		,    shuffle=lowerCAmelCase_		,    collate_fn=lowerCAmelCase_		,    batch_size=lowerCAmelCase_				)

   return train_dataloader, eval_dataloader



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[int]		,    lowerCAmelCase_				:Optional[int]				)->Tuple:
   '''simple docstring'''






   snake_case_            =       Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   snake_case_            =       config["lr"]
   snake_case_            =       int(config["num_epochs"]				)
   snake_case_            =       int(config["seed"]				)
   snake_case_            =       int(config["batch_size"]				)
   snake_case_            =       args.model_name_or_path

   set_seed(lowerCAmelCase_				)
   snake_case_     ,     snake_case_            =       get_dataloaders(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   snake_case_            =       AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_		,    return_dict=lowerCAmelCase_				)

   # Instantiate optimizer
   snake_case_            =       (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   snake_case_            =       optimizer_cls(params=model.parameters()		,    lr=lowerCAmelCase_				)

   if accelerator.state.deepspeed_plugin is not None:
      snake_case_            =       accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      snake_case_            =       1
   snake_case_            =       (len(lowerCAmelCase_				) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      snake_case_            =       get_linear_schedule_with_warmup(
          optimizer=lowerCAmelCase_		,    num_warmup_steps=0		,    num_training_steps=lowerCAmelCase_		,    )
   else:
      snake_case_            =       DummyScheduler(lowerCAmelCase_		,    total_num_steps=lowerCAmelCase_		,    warmup_num_steps=0				)

   # 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.
   snake_case_     ,     snake_case_     ,     snake_case_     ,     snake_case_     ,     snake_case_            =       accelerator.prepare(
       lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)

   # We need to keep track of how many total steps we have iterated over
   snake_case_            =       0
   # We also need to keep track of the stating epoch so files are named properly
   snake_case_            =       0

   # Now we train the model
   snake_case_            =       evaluate.load("glue"		,    "mrpc"				)
   snake_case_            =       0
   snake_case_            =       {}
   for epoch in range(lowerCAmelCase_		,    lowerCAmelCase_				):
      model.train()
      for step, batch in enumerate(lowerCAmelCase_				):
         snake_case_            =       model(**lowerCAmelCase_				)
         snake_case_            =       outputs.loss
         snake_case_            =       loss / gradient_accumulation_steps
         accelerator.backward(lowerCAmelCase_				)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

      model.eval()
      snake_case_            =       0
      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():
            snake_case_            =       model(**lowerCAmelCase_				)
         snake_case_            =       outputs.logits.argmax(dim=-1				)
         # It is slightly faster to call this once, than multiple times
         snake_case_     ,     snake_case_            =       accelerator.gather(
             (predictions, batch["labels"])				)  # If we are in a multiprocess environment, the last batch has duplicates
         if accelerator.use_distributed:
            if step == len(lowerCAmelCase_				) - 1:
               snake_case_            =       predictions[: len(eval_dataloader.dataset				) - samples_seen]
               snake_case_            =       references[: len(eval_dataloader.dataset				) - samples_seen]
            else:
               samples_seen += references.shape[0]
         metric.add_batch(
             predictions=lowerCAmelCase_		,    references=lowerCAmelCase_		,    )

      snake_case_            =       metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''		,    lowerCAmelCase_				)
      snake_case_            =       eval_metric["accuracy"]

      if best_performance < eval_metric["accuracy"]:
         snake_case_            =       eval_metric["accuracy"]

   if args.performance_lower_bound is not None:
      assert (
          args.performance_lower_bound <= best_performance
      ), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

   accelerator.wait_for_everyone()
   if accelerator.is_main_process:
      with open(os.path.join(args.output_dir		,    "all_results.json"				)		,    "w"				) as f:
         json.dump(lowerCAmelCase_		,    lowerCAmelCase_				)



def       _lowerCAmelCase    (				)->str:
   '''simple docstring'''






   snake_case_            =       argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."				)
   parser.add_argument(
       "--model_name_or_path"		,    type=lowerCAmelCase_		,    default="bert-base-cased"		,    help="Path to pretrained model or model identifier from huggingface.co/models."		,    required=lowerCAmelCase_		,    )
   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(
       "--performance_lower_bound"		,    type=lowerCAmelCase_		,    default=lowerCAmelCase_		,    help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."		,    )
   parser.add_argument(
       "--num_epochs"		,    type=lowerCAmelCase_		,    default=3		,    help="Number of train epochs."		,    )
   snake_case_            =       parser.parse_args()
   snake_case_            =       {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
   training_function(lowerCAmelCase_		,    lowerCAmelCase_				)


if __name__ == "__main__":
   main()










'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_snake_case : str  				=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_snake_case : List[str]  				=						'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'

class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							tempfile.mkdtemp()
														os.makedirs(os.path.join(self.diffusers_dir				,     '''schedulers/'''   )   )
														_a      	=							self.diffusers_dir
														shutil.copy(
														    os.path.join(lowerCAmelCase_				,     '''src/diffusers/schedulers/scheduling_ddpm.py'''   )				,     os.path.join(self.diffusers_dir				,     '''schedulers/scheduling_ddpm.py'''   )				,     )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														_a      	=							'''src/diffusers'''
														shutil.rmtree(self.diffusers_dir   )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    str=None   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + class_code
														if overwrite_result is not None:
																					_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
														_a      	=							black.Mode(target_versions={black.TargetVersion.PYaa}				,     line_length=1_19   )
														_a      	=							black.format_str(lowerCAmelCase_				,     mode=lowerCAmelCase_   )
														_a      	=							os.path.join(self.diffusers_dir				,     '''new_code.py'''   )
														with open(lowerCAmelCase_				,     '''w'''				,     newline='''\n'''   ) as f:
																					f.write(lowerCAmelCase_   )
														if overwrite_result is None:
																					self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase_   )   ) == 0   )
														else:
																					check_copies.is_copy_consistent(f.name				,     overwrite=lowerCAmelCase_   )
																					with open(lowerCAmelCase_				,     '''r'''   ) as f:
																												self.assertTrue(f.read()				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''   )
														self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Union[str, Any]:


														"""simple docstring"""


														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     REFERENCE_CODE + '''\n'''				,     )

														# With no empty line at the end
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     lowerCAmelCase_				,     )

														# Copy consistency with rename
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

														# Copy consistency with a really long name
														_a      	=							'''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
														self.check_copy_consistency(
														    F'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'				,     F'{long_class_name}SchedulerOutput'				,     re.sub('''Bert'''				,     lowerCAmelCase_				,     lowerCAmelCase_   )				,     )

														# Copy consistency with overwrite
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     lowerCAmelCase_				,     overwrite_result=re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

