'''simple docstring'''


import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


snake_case__					:      int				      =  16
snake_case__					:      Optional[Any]				      =  32


def  _lowerCamelCase   (				lowerCamelCase_ :							Accelerator     ,					lowerCamelCase_ :							int = 16       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[int]       =				AutoTokenizer.from_pretrained('bert-base-cased'       )
     UpperCAmelCase_							:       int       =				load_dataset('glue'     ,					'mrpc'       )

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

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

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

     # Instantiate dataloaders.
     UpperCAmelCase_							:       List[str]       =				DataLoader(
         tokenized_datasets['train']     ,					shuffle=lowerCamelCase_     ,					collate_fn=lowerCamelCase_     ,					batch_size=lowerCamelCase_     ,					drop_last=lowerCamelCase_       )
     UpperCAmelCase_							:       Union[str, Any]       =				DataLoader(
         tokenized_datasets['validation']     ,					shuffle=lowerCamelCase_     ,					collate_fn=lowerCamelCase_     ,					batch_size=lowerCamelCase_     ,					drop_last=(accelerator.mixed_precision == 'fp8')     ,					)

     return train_dataloader, eval_dataloader


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]     ,					lowerCamelCase_ :							str       ):



     """simple docstring"""





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

     UpperCAmelCase_							:       int       =				evaluate.load('glue'     ,					'mrpc'       )

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

     set_seed(lowerCamelCase_       )
     UpperCAmelCase_       ,      UpperCAmelCase_							:       Dict       =				get_dataloaders(lowerCamelCase_     ,					lowerCamelCase_       )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCAmelCase_							:       Union[str, Any]       =				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).
     UpperCAmelCase_							:       Tuple       =				model.to(accelerator.device       )
     # Instantiate optimizer
     UpperCAmelCase_							:       str       =				AdamW(params=model.parameters()     ,					lr=lowerCamelCase_       )

     # Instantiate scheduler
     UpperCAmelCase_							:       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.

     UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       List[str]       =				accelerator.prepare(
         lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_       )

     # Now we train the model
     for epoch in range(lowerCamelCase_       ):
          model.train()
          for step, batch in enumerate(lowerCamelCase_       ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device       )
               UpperCAmelCase_							:       str       =				model(**lowerCamelCase_       )
               UpperCAmelCase_							:       List[str]       =				outputs.loss
               UpperCAmelCase_							:       int       =				loss / gradient_accumulation_steps
               accelerator.backward(lowerCamelCase_       )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          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():
                    UpperCAmelCase_							:       Dict       =				model(**lowerCamelCase_       )
               UpperCAmelCase_							:       Tuple       =				outputs.logits.argmax(dim=-1       )
               UpperCAmelCase_       ,      UpperCAmelCase_							:       str       =				accelerator.gather_for_metrics((predictions, batch['labels'])       )
               metric.add_batch(
                   predictions=lowerCamelCase_     ,					references=lowerCamelCase_     ,					)

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


def  _lowerCamelCase   (				):



     """simple docstring"""





     UpperCAmelCase_							:       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.'       )
     UpperCAmelCase_							:       Any       =				parser.parse_args()
     UpperCAmelCase_							:       str       =				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(lowerCamelCase_     ,					lowerCamelCase_       )


if __name__ == "__main__":
 main()







'''simple docstring'''


import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:






 class 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   @staticmethod
   def _UpperCamelCase					(			*snake_case_				,		**snake_case_						):




        '''simple docstring'''

        pass


def  _lowerCamelCase   (				lowerCamelCase_ :							Image       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				hashlib.mda(image.tobytes()       )
     return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				DepthEstimationPipeline(model=snake_case_				,		image_processor=snake_case_						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
       self.assertEqual({'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)}				,		snake_case_						)
       import datasets

       UpperCAmelCase_							:       Optional[int]       =				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'				,		'image'				,		split='test'						)
       UpperCAmelCase_							:       Union[str, Any]       =				depth_estimator(
           [
               Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						),
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               # RGBA
               dataset[0]['file'],
               # LA
               dataset[1]['file'],
               # L
               dataset[2]['file'],
           ]						)
       self.assertEqual(
           [
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
           ]				,		snake_case_				,		)


  @require_tf
  @unittest.skip('Depth estimation is not implemented in TF'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @slow
  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				'Intel/dpt-large'
       UpperCAmelCase_							:       Dict       =				pipeline('depth-estimation'				,		model=snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       UpperCAmelCase_							:       Union[str, Any]       =				hashimage(outputs['depth']						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()						)				,		29.3_04						)
       self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()						)				,		2.6_62						)






  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'						)






