


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


lowercase_			       =      1_6
lowercase_			       =      3_2
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__ = 16     ,			SCREAMING_SNAKE_CASE__ = "bert-base-cased"		):
 __lowerCamelCase						:				Optional[int]						   =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__		)
 __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						:				Dict						   =		tokenizer(examples['sentence1']     ,			examples['sentence2']     ,			truncation=SCREAMING_SNAKE_CASE__     ,			max_length=SCREAMING_SNAKE_CASE__		)
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 __lowerCamelCase						:				Any						   =		datasets.map(
     SCREAMING_SNAKE_CASE__     ,			batched=SCREAMING_SNAKE_CASE__     ,			remove_columns=['idx', 'sentence1', 'sentence2']     ,			load_from_cache_file=SCREAMING_SNAKE_CASE__		)

 # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
 # transformers library
 __lowerCamelCase						:				Union[str, Any]						   =		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.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(SCREAMING_SNAKE_CASE__     ,			padding='max_length'     ,			max_length=128     ,			return_tensors='pt'		)
  return tokenizer.pad(SCREAMING_SNAKE_CASE__     ,			padding='longest'     ,			return_tensors='pt'		)

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

 return train_dataloader, eval_dataloader
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		):
 model.eval()
 __lowerCamelCase						:				List[Any]						   =		0
 for step, batch in enumerate(SCREAMING_SNAKE_CASE__		):
  # We could avoid this line since we set the accelerator with `device_placement=True`.
  batch.to(accelerator.device		)
  with torch.no_grad():
   __lowerCamelCase						:				List[str]						   =		model(**SCREAMING_SNAKE_CASE__		)
  __lowerCamelCase						:				Dict						   =		outputs.logits.argmax(dim=-1		)
  # It is slightly faster to call this once, than multiple times
  __lowerCamelCase						:				int						   =		accelerator.gather(
      (predictions, batch['labels'])		)  # If we are in a multiprocess environment, the last batch has duplicates
  if accelerator.use_distributed:
   if step == len(SCREAMING_SNAKE_CASE__		) - 1:
    __lowerCamelCase						:				List[str]						   =		predictions[: len(eval_dataloader.dataset		) - samples_seen]
    __lowerCamelCase						:				Any						   =		references[: len(eval_dataloader.dataset		) - samples_seen]
   else:
    samples_seen += references.shape[0]
  metric.add_batch(
      predictions=SCREAMING_SNAKE_CASE__     ,			references=SCREAMING_SNAKE_CASE__     ,			)

 __lowerCamelCase						:				Optional[int]						   =		metric.compute()
 return eval_metric["accuracy"]
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		):
 # Initialize accelerator
 __lowerCamelCase						:				Union[str, Any]						   =		Accelerator()

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __lowerCamelCase						:				int						   =		config["lr"]
 __lowerCamelCase						:				List[str]						   =		int(config['num_epochs']		)
 __lowerCamelCase						:				Dict						   =		int(config['seed']		)
 __lowerCamelCase						:				List[Any]						   =		int(config['batch_size']		)
 __lowerCamelCase						:				Optional[Any]						   =		args.model_name_or_path

 set_seed(SCREAMING_SNAKE_CASE__		)
 __lowerCamelCase						:				Tuple						   =		get_dataloaders(SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __lowerCamelCase						:				str						   =		AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__     ,			return_dict=SCREAMING_SNAKE_CASE__		)

 # Instantiate optimizer
 __lowerCamelCase						:				List[str]						   =		(
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 __lowerCamelCase						:				int						   =		optimizer_cls(params=model.parameters()     ,			lr=SCREAMING_SNAKE_CASE__		)

 if accelerator.state.deepspeed_plugin is not None:
  __lowerCamelCase						:				Dict						   =		accelerator.state.deepspeed_plugin.deepspeed_config[
      "gradient_accumulation_steps"
  ]
 else:
  __lowerCamelCase						:				List[Any]						   =		1
 __lowerCamelCase						:				int						   =		(len(SCREAMING_SNAKE_CASE__		) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  __lowerCamelCase						:				Tuple						   =		get_linear_schedule_with_warmup(
      optimizer=SCREAMING_SNAKE_CASE__     ,			num_warmup_steps=0     ,			num_training_steps=SCREAMING_SNAKE_CASE__     ,			)
 else:
  __lowerCamelCase						:				Dict						   =		DummyScheduler(SCREAMING_SNAKE_CASE__     ,			total_num_steps=SCREAMING_SNAKE_CASE__     ,			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.
 __lowerCamelCase						:				List[Any]						   =		accelerator.prepare(
     SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		)

 # 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						:				List[Any]						   =		0
 __lowerCamelCase						:				List[str]						   =		evaluate.load('glue'     ,			'mrpc'		)
 __lowerCamelCase						:				str						   =		num_epochs

 if args.partial_train_epoch is not None:
  __lowerCamelCase						:				Optional[int]						   =		args.partial_train_epoch

 if args.resume_from_checkpoint:
  accelerator.load_state(args.resume_from_checkpoint		)
  __lowerCamelCase						:				Optional[Any]						   =		args.resume_from_checkpoint.split('epoch_'		)[1]
  __lowerCamelCase						:				Optional[Any]						   =		""
  for char in epoch_string:
   if char.isdigit():
    state_epoch_num += char
   else:
    break
  __lowerCamelCase						:				Optional[int]						   =		int(SCREAMING_SNAKE_CASE__		) + 1
  __lowerCamelCase						:				Optional[int]						   =		evaluation_loop(SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		)
  accelerator.print('resumed checkpoint performance:'     ,			SCREAMING_SNAKE_CASE__		)
  accelerator.print('resumed checkpoint\'s scheduler\'s lr:'     ,			lr_scheduler.get_lr()[0]		)
  accelerator.print('resumed optimizers\'s lr:'     ,			optimizer.param_groups[0]['lr']		)
  with open(os.path.join(args.output_dir     ,			f'state_{starting_epoch-1}.json'		)     ,			'r'		) as f:
   __lowerCamelCase						:				List[Any]						   =		json.load(SCREAMING_SNAKE_CASE__		)
   assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
   assert (
       resumed_state["lr"] == lr_scheduler.get_lr()[0]
   ), "Scheduler learning rate mismatch, loading from checkpoint failed"
   assert (
       resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
   ), "Optimizer learning rate mismatch, loading from checkpoint failed"
   assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
   return

    # Now we train the model
 __lowerCamelCase						:				Any						   =		{}
 for epoch in range(SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		):
  model.train()
  for step, batch in enumerate(SCREAMING_SNAKE_CASE__		):
   __lowerCamelCase						:				List[Any]						   =		model(**SCREAMING_SNAKE_CASE__		)
   __lowerCamelCase						:				Optional[int]						   =		outputs.loss
   __lowerCamelCase						:				str						   =		loss / gradient_accumulation_steps
   accelerator.backward(SCREAMING_SNAKE_CASE__		)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1
  __lowerCamelCase						:				Dict						   =		f'epoch_{epoch}'
  __lowerCamelCase						:				Optional[Any]						   =		os.path.join(args.output_dir     ,			SCREAMING_SNAKE_CASE__		)
  accelerator.save_state(SCREAMING_SNAKE_CASE__		)
  __lowerCamelCase						:				Optional[int]						   =		evaluation_loop(SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		)
  __lowerCamelCase						:				List[str]						   =		accuracy
  __lowerCamelCase						:				int						   =		lr_scheduler.get_lr()[0]
  __lowerCamelCase						:				List[Any]						   =		optimizer.param_groups[0]["lr"]
  __lowerCamelCase						:				Dict						   =		epoch
  __lowerCamelCase						:				int						   =		overall_step
  accelerator.print(f'epoch {epoch}:'     ,			SCREAMING_SNAKE_CASE__		)

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
   with open(os.path.join(args.output_dir     ,			f'state_{epoch}.json'		)     ,			'w'		) as f:
    json.dump(SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		)
def  UpperCamelCase__	(    ):
 __lowerCamelCase						:				Any						   =		argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'		)
 parser.add_argument(
     '--model_name_or_path'     ,			type=SCREAMING_SNAKE_CASE__     ,			default='bert-base-cased'     ,			help='Path to pretrained model or model identifier from huggingface.co/models.'     ,			required=SCREAMING_SNAKE_CASE__     ,			)
 parser.add_argument(
     '--output_dir'     ,			type=SCREAMING_SNAKE_CASE__     ,			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=SCREAMING_SNAKE_CASE__     ,			default=SCREAMING_SNAKE_CASE__     ,			help='If the training should continue from a checkpoint folder.'     ,			)
 parser.add_argument(
     '--partial_train_epoch'     ,			type=SCREAMING_SNAKE_CASE__     ,			default=SCREAMING_SNAKE_CASE__     ,			help='If passed, the training will stop after this number of epochs.'     ,			)
 parser.add_argument(
     '--num_epochs'     ,			type=SCREAMING_SNAKE_CASE__     ,			default=2     ,			help='Number of train epochs.'     ,			)
 __lowerCamelCase						:				List[Any]						   =		parser.parse_args()
 __lowerCamelCase						:				int						   =		{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

 training_function(SCREAMING_SNAKE_CASE__     ,			SCREAMING_SNAKE_CASE__		)


if __name__ == "__main__":
       main()


import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 		snake_case__	(		lowercase					):
   return EnvironmentCommand()







class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	parser.add_parser("env" )
       download_parser.set_defaults(func=lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							int       =	huggingface_hub.__version__

       lowerCAmelCase_:							List[str]       =	"not installed"
       lowerCAmelCase_:							Dict       =	"NA"
       if is_torch_available():
          import torch

          lowerCAmelCase_:							List[str]       =	torch.__version__
          lowerCAmelCase_:							int       =	torch.cuda.is_available()

       lowerCAmelCase_:							Tuple       =	"not installed"
       if is_transformers_available():
          import transformers

          lowerCAmelCase_:							Tuple       =	transformers.__version__

       lowerCAmelCase_:							Optional[int]       =	"not installed"
       if is_accelerate_available():
          import accelerate

          lowerCAmelCase_:							List[Any]       =	accelerate.__version__

       lowerCAmelCase_:							Dict       =	"not installed"
       if is_xformers_available():
          import xformers

          lowerCAmelCase_:							Dict       =	xformers.__version__

       lowerCAmelCase_:							Union[str, Any]       =	{
           "`diffusers` version": version,
           "Platform": platform.platform(),
           "Python version": platform.python_version(),
           "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
           "Huggingface_hub version": hub_version,
           "Transformers version": transformers_version,
           "Accelerate version": accelerate_version,
           "xFormers version": xformers_version,
           "Using GPU in script?": "<fill in>",
           "Using distributed or parallel set-up in script?": "<fill in>",
       }

       print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
       print(self.format_dict(lowerCamelCase__ ) )

       return info




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"