

'''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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


_lowerCAmelCase         =						16
_lowerCAmelCase         =						32




def 						UpperCamelCase	(			a		,						a = 16			)				->     Optional[Any]:






					'''simple docstring'''
					__magic_name__						       =    AutoTokenizer.from_pretrained('''bert-base-cased'''			)
					__magic_name__						       =    load_dataset('''glue'''		,						'''mrpc'''			)

					def tokenize_function(a			):
										# max_length=None => use the model max length (it's actually the default)
										__magic_name__						       =    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():
										__magic_name__						       =    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
					__magic_name__						       =    tokenized_datasets.rename_column('''label'''		,						'''labels'''			)

					def collate_fn(a			):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										__magic_name__						       =    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":
															__magic_name__						       =    16
										elif accelerator.mixed_precision != "no":
															__magic_name__						       =    8
										else:
															__magic_name__						       =    None

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

					# Instantiate dataloaders.
					__magic_name__						       =    DataLoader(
					    tokenized_datasets['''train''']		,						shuffle=__lowerCAmelCase		,						collate_fn=__lowerCAmelCase		,						batch_size=__lowerCAmelCase			)
					__magic_name__						       =    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

						_lowerCAmelCase         =						mocked_dataloaders  # noqa: F811




def 						UpperCamelCase	(			a		,						a			)				->     str:






					'''simple docstring'''
					# For testing only
					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,						__lowerCAmelCase			) == "1":
										__magic_name__						       =    2
					# Initialize accelerator
					__magic_name__						       =    Accelerator(cpu=args.cpu		,						mixed_precision=args.mixed_precision			)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__magic_name__						       =    config["""lr"""]
					__magic_name__						       =    int(config['''num_epochs''']			)
					__magic_name__						       =    int(config['''seed''']			)
					__magic_name__						       =    int(config['''batch_size''']			)

					__magic_name__						       =    evaluate.load('''glue'''		,						'''mrpc'''			)

					# If the batch size is too big we use gradient accumulation
					__magic_name__						       =    1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										__magic_name__						       =    batch_size // MAX_GPU_BATCH_SIZE
										__magic_name__						       =    MAX_GPU_BATCH_SIZE

					set_seed(__lowerCAmelCase			)
					__magic_name__						       =    get_dataloaders(__lowerCAmelCase		,						__lowerCAmelCase			)
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__magic_name__						       =    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).
					__magic_name__						       =    model.to(accelerator.device			)

					# Instantiate optimizer
					__magic_name__						       =    AdamW(params=model.parameters()		,						lr=__lowerCAmelCase			)

					# Instantiate scheduler
					__magic_name__						       =    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.
					__magic_name__						       =    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			)
															__magic_name__						       =    model(**__lowerCAmelCase			)
															__magic_name__						       =    outputs.loss
															__magic_name__						       =    loss / gradient_accumulation_steps
															accelerator.backward(__lowerCAmelCase			)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

										model.eval()
										__magic_name__						       =    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():
																				__magic_name__						       =    model(**__lowerCAmelCase			)
															__magic_name__						       =    outputs.logits.argmax(dim=-1			)
															__magic_name__						       =    accelerator.gather((predictions, batch['''labels'''])			)
															# New Code #
															# First we check if it's a distributed system
															if accelerator.use_distributed:
																				# Then see if we're on the last batch of our eval dataloader
																				if step == len(__lowerCAmelCase			) - 1:
																									# Last batch needs to be truncated on distributed systems as it contains additional samples
																									__magic_name__						       =    predictions[: len(eval_dataloader.dataset			) - samples_seen]
																									__magic_name__						       =    references[: len(eval_dataloader.dataset			) - samples_seen]
																				else:
																									# Otherwise we add the number of samples seen
																									samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
															metric.add_batch(
															    predictions=__lowerCAmelCase		,						references=__lowerCAmelCase		,						)

										__magic_name__						       =    metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F'''epoch {epoch}:'''		,						__lowerCAmelCase			)




def 						UpperCamelCase	(			)				->     Tuple:






					'''simple docstring'''
					__magic_name__						       =    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.'''			)
					__magic_name__						       =    parser.parse_args()
					__magic_name__						       =    {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
					training_function(__lowerCAmelCase		,						__lowerCAmelCase			)


if __name__ == "__main__":
						main()









'''simple docstring'''




import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def __init__(   self		:   Tuple			,				a__		:   Union[str, Any]			,				a__		:   Union[str, Any]=7			,				a__		:   Dict=3			,				a__		:   Optional[Any]=18			,				a__		:   Optional[Any]=30			,				a__		:   Tuple=400			,				a__		:   Optional[int]=True			,				a__		:   int=None			,				a__		:   Union[str, Any]=True			,				a__		:   Optional[Any]=None			,				a__		:   str=True			,				a__		:   List[Any]=[0.5, 0.5, 0.5]			,				a__		:   Tuple=[0.5, 0.5, 0.5]			,				a__		:   Union[str, Any]=False			,				):
      __magic_name__						       =    size if size is not None else {'''height''': 20, '''width''': 20}
      __magic_name__						       =    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
      __magic_name__						       =    parent
      __magic_name__						       =    batch_size
      __magic_name__						       =    num_channels
      __magic_name__						       =    image_size
      __magic_name__						       =    min_resolution
      __magic_name__						       =    max_resolution
      __magic_name__						       =    do_resize
      __magic_name__						       =    size
      __magic_name__						       =    do_center_crop
      __magic_name__						       =    crop_size
      __magic_name__						       =    do_normalize
      __magic_name__						       =    image_mean
      __magic_name__						       =    image_std
      __magic_name__						       =    do_reduce_labels



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }




def 						UpperCamelCase	(			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    load_dataset('''hf-internal-testing/fixtures_ade20k'''		,						split='''test'''			)

     __magic_name__						       =    Image.open(dataset[0]['''file''']			)
     __magic_name__						       =    Image.open(dataset[1]['''file''']			)

     return image, map




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    load_dataset('''hf-internal-testing/fixtures_ade20k'''		,						split='''test'''			)

     __magic_name__						       =    Image.open(ds[0]['''file''']			)
     __magic_name__						       =    Image.open(ds[1]['''file''']			)
     __magic_name__						       =    Image.open(ds[2]['''file''']			)
     __magic_name__						       =    Image.open(ds[3]['''file''']			)

     return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					BeitImageProcessor if is_vision_available() else None



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    BeitImageProcessingTester(self      )



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return self.image_processor_tester.prepare_image_processor_dict()



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(a__			,				'''do_resize'''      )      )
      self.assertTrue(hasattr(a__			,				'''size'''      )      )
      self.assertTrue(hasattr(a__			,				'''do_center_crop'''      )      )
      self.assertTrue(hasattr(a__			,				'''center_crop'''      )      )
      self.assertTrue(hasattr(a__			,				'''do_normalize'''      )      )
      self.assertTrue(hasattr(a__			,				'''image_mean'''      )      )
      self.assertTrue(hasattr(a__			,				'''image_std'''      )      )



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    self.image_processing_class.from_dict(self.image_processor_dict      )
      self.assertEqual(image_processor.size			,				{'''height''': 20, '''width''': 20}      )
      self.assertEqual(image_processor.crop_size			,				{'''height''': 18, '''width''': 18}      )
      self.assertEqual(image_processor.do_reduce_labels			,				a__      )

      __magic_name__						       =    self.image_processing_class.from_dict(
          self.image_processor_dict			,				size=42			,				crop_size=84			,				reduce_labels=a__      )
      self.assertEqual(image_processor.size			,				{'''height''': 42, '''width''': 42}      )
      self.assertEqual(image_processor.crop_size			,				{'''height''': 84, '''width''': 84}      )
      self.assertEqual(image_processor.do_reduce_labels			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      pass



 def 			snake_case__  (   self		:   Dict      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				Image.Image      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   Optional[int]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random numpy tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				numpify=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				np.ndarray      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   List[str]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				torchify=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				torch.Tensor      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				torchify=a__      )
      __magic_name__						       =    []
      for image in image_inputs:
           self.assertIsInstance(a__			,				torch.Tensor      )
           maps.append(torch.zeros(image.shape[-2:]      ).long()      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				maps[0]			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test batched
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test not batched input (PIL images)
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_single_inputs()

      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test batched input (PIL images)
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_batch_inputs()

      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              2,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )



 def 			snake_case__  (   self		:   Any      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_single_inputs()
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 150      )

      __magic_name__						       =    True
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )






