





'''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,
# specifically showcasing the experiment tracking 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
#
########################################################################

lowercase__    :      Union[str, Any]						=    16
lowercase__    :      Dict						=    32






def        _lowerCAmelCase			(  __snake_case  : Accelerator ,							__snake_case  : int = 16			)     ->     Tuple:
     __A						: Dict      =       AutoTokenizer.from_pretrained('bert-base-cased'			)
     __A						: Dict      =       load_dataset('glue' ,							'mrpc'			)

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

     def collate_fn(__snake_case  : Union[str, Any]			):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __A						: Optional[Any]      =       1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __A						: List[str]      =       16
          elif accelerator.mixed_precision != "no":
               __A						: Dict      =       8
          else:
               __A						: Tuple      =       None

          return tokenizer.pad(
              __snake_case ,							padding='longest' ,							max_length=__snake_case ,							pad_to_multiple_of=__snake_case ,							return_tensors='pt' ,							)

     # Instantiate dataloaders.
     __A						: Tuple      =       DataLoader(
         tokenized_datasets['train'] ,							shuffle=__snake_case ,							collate_fn=__snake_case ,							batch_size=__snake_case			)
     __A						: Union[str, Any]      =       DataLoader(
         tokenized_datasets['validation'] ,							shuffle=__snake_case ,							collate_fn=__snake_case ,							batch_size=__snake_case			)

     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

  lowercase__    :      Union[str, Any]						=    mocked_dataloaders  # noqa: F811






def        _lowerCAmelCase			(  __snake_case  : Any ,							__snake_case  : Optional[Any]			)     ->     int:
     # For testing only
     if os.environ.get('TESTING_MOCKED_DATALOADERS' ,							__snake_case			) == "1":
          __A						: int      =       2
     # Initialize Accelerator

     # New Code #
     # We pass in "all" to `log_with` to grab all available trackers in the environment
     # Note: If using a custom `Tracker` class, should be passed in here such as:
     # >>> log_with = ["all", MyCustomTrackerClassInstance()]
     if args.with_tracking:
          __A						: int      =       Accelerator(
              cpu=args.cpu ,							mixed_precision=args.mixed_precision ,							log_with='all' ,							project_dir=args.project_dir			)
     else:
          __A						: 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
     __A						: Union[str, Any]      =       config['lr']
     __A						: Any      =       int(config['num_epochs']			)
     __A						: Dict      =       int(config['seed']			)
     __A						: Union[str, Any]      =       int(config['batch_size']			)
     set_seed(__snake_case			)

     __A ,__A						: List[str]      =       get_dataloaders(__snake_case ,							__snake_case			)
     __A						: Optional[Any]      =       evaluate.load('glue' ,							'mrpc'			)

     # If the batch size is too big we use gradient accumulation
     __A						: List[str]      =       1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          __A						: Union[str, Any]      =       batch_size // MAX_GPU_BATCH_SIZE
          __A						: Union[str, Any]      =       MAX_GPU_BATCH_SIZE

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __A						: str      =       AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,							return_dict=__snake_case			)

     # 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).
     __A						: int      =       model.to(accelerator.device			)

     # Instantiate optimizer
     __A						: str      =       AdamW(params=model.parameters() ,							lr=__snake_case			)

     # Instantiate scheduler
     __A						: Any      =       get_linear_schedule_with_warmup(
         optimizer=__snake_case ,							num_warmup_steps=1_00 ,							num_training_steps=(len(__snake_case			) * 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.
     __A ,__A ,__A ,__A ,__A						: Any      =       accelerator.prepare(
         __snake_case ,							__snake_case ,							__snake_case ,							__snake_case ,							__snake_case			)

     # New Code #
     # We need to initialize the trackers we use. Overall configurations can also be stored
     if args.with_tracking:
          __A						: int      =       os.path.split(__snake_case			)[-1].split('.'			)[0]
          accelerator.init_trackers(__snake_case ,							__snake_case			)

     # Now we train the model
     for epoch in range(__snake_case			):
          model.train()
          # New Code #
          # For our tracking example, we will log the total loss of each epoch
          if args.with_tracking:
               __A						: int      =       0
          for step, batch in enumerate(__snake_case			):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device			)
               __A						: List[Any]      =       model(**__snake_case			)
               __A						: List[str]      =       outputs.loss
               # New Code #
               if args.with_tracking:
                    total_loss += loss.detach().float()
               __A						: List[Any]      =       loss / gradient_accumulation_steps
               accelerator.backward(__snake_case			)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

          __A						: Optional[int]      =       metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:' ,							__snake_case			)

          # New Code #
          # To actually log, we call `Accelerator.log`
          # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
          if args.with_tracking:
               accelerator.log(
                   {
                       'accuracy': eval_metric['accuracy'],
                       'f1': eval_metric['f1'],
                       'train_loss': total_loss.item() / len(__snake_case			),
                       'epoch': epoch,
                   } ,							step=__snake_case ,							)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
     if args.with_tracking:
          accelerator.end_training()






def        _lowerCAmelCase			(  )     ->     Tuple:
     __A						: Union[str, Any]      =       argparse.ArgumentParser(description='Simple example of training script.'			)
     parser.add_argument(
         '--mixed_precision' ,							type=__snake_case ,							default=__snake_case ,							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(
         '--with_tracking' ,							action='store_true' ,							help='Whether to load in all available experiment trackers from the environment and use them for logging.' ,							)
     parser.add_argument(
         '--project_dir' ,							type=__snake_case ,							default='logs' ,							help='Location on where to store experiment tracking logs` and relevent project information' ,							)
     __A						: Tuple      =       parser.parse_args()
     __A						: Union[str, Any]      =       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(__snake_case ,							__snake_case			)


if __name__ == "__main__":
  main()






'''simple docstring'''




import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
  from PIL import Image
else:


  class  SCREAMING_SNAKE_CASE    :


       @staticmethod
       def 							SCREAMING_SNAKE_CASE  (						*_UpperCAmelCase						,	**_UpperCAmelCase):

            '''simple docstring'''






            pass






def        _lowerCAmelCase			(  __snake_case  : Image			)     ->     str:
     __A						: Dict      =       hashlib.mda(image.tobytes()			)
     return m.hexdigest()[:10]






def        _lowerCAmelCase			(  __snake_case  : Image			)     ->     Dict:
     __A						: Dict      =       np.array(__snake_case			)
     __A						: List[Any]      =       npimg.shape
     return {"hash": hashimage(__snake_case			), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):
     lowerCAmelCase					=       dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
     lowerCAmelCase					=       dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Any      =       MaskGenerationPipeline(model=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          pass


     @require_tf
     @unittest.skip('Image segmentation not implemented in TF')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     @slow
     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       pipeline('mask-generation'						,	model='facebook/sam-vit-huge')

          __A						: Tuple      =       image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'						,	points_per_batch=256)

          # Shortening by hashing
          __A						: int      =       []
          for i, o in enumerate(outputs['masks']):
               new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967},
                  {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
                  {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909},
                  {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879},
                  {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834},
                  {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716},
                  {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612},
                  {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552},
                  {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532},
                  {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499},
                  {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483},
                  {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
                  {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408},
                  {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326},
                  {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262},
                  {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}
              ]						,	)
          # fmt: on


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       'facebook/sam-vit-huge'
          __A						: Optional[Any]      =       pipeline('mask-generation'						,	model=_UpperCAmelCase)

          __A						: int      =       image_segmenter(
              'http://images.cocodataset.org/val2017/000000039769.jpg'						,	pred_iou_thresh=1						,	points_per_batch=256)

          # Shortening by hashing
          __A						: int      =       []
          for i, o in enumerate(outputs['masks']):
               new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0210},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
              ]						,	)