



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     							=						16
_lowercase     							=						32






def      _A				(UpperCamelCase		:      Accelerator      ,			UpperCamelCase		:      int = 16 )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		AutoTokenizer.from_pretrained("""bert-base-cased""" )
			lowerCamelCase__ :      Optional[int]								=		load_dataset("""glue"""      ,			"""mrpc""" )

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

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

						return tokenizer.pad(
						    UpperCamelCase      ,			padding="""longest"""      ,			max_length=UpperCamelCase      ,			pad_to_multiple_of=UpperCamelCase      ,			return_tensors="""pt"""      ,			)

			# Instantiate dataloaders.
			lowerCamelCase__ :      List[Any]								=		DataLoader(
			    tokenized_datasets["""train"""]      ,			shuffle=UpperCamelCase      ,			collate_fn=UpperCamelCase      ,			batch_size=UpperCamelCase )
			lowerCamelCase__ :      str								=		DataLoader(
			    tokenized_datasets["""validation"""]      ,			shuffle=UpperCamelCase      ,			collate_fn=UpperCamelCase      ,			batch_size=UpperCamelCase )

			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     							=						mocked_dataloaders  # noqa: F811






def      _A				(UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Any )  ->List[Any]:



			'''simple docstring'''







			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,			UpperCamelCase ) == "1":
						lowerCamelCase__ :      Dict								=		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:
						lowerCamelCase__ :      Dict								=		Accelerator(
						    cpu=args.cpu      ,			mixed_precision=args.mixed_precision      ,			log_with="""all"""      ,			project_dir=args.project_dir )
			else:
						lowerCamelCase__ :      str								=		Accelerator(cpu=args.cpu      ,			mixed_precision=args.mixed_precision )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase__ :      Dict								=		config["""lr"""]
			lowerCamelCase__ :      Dict								=		int(config["""num_epochs"""] )
			lowerCamelCase__ :      Optional[int]								=		int(config["""seed"""] )
			lowerCamelCase__ :      Dict								=		int(config["""batch_size"""] )
			set_seed(UpperCamelCase )

			lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		get_dataloaders(UpperCamelCase      ,			UpperCamelCase )
			lowerCamelCase__ :      Optional[Any]								=		evaluate.load("""glue"""      ,			"""mrpc""" )

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

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCamelCase__ :      Union[str, Any]								=		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""      ,			return_dict=UpperCamelCase )

			# 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).
			lowerCamelCase__ :      List[str]								=		model.to(accelerator.device )

			# Instantiate optimizer
			lowerCamelCase__ :      List[str]								=		AdamW(params=model.parameters()      ,			lr=UpperCamelCase )

			# Instantiate scheduler
			lowerCamelCase__ :      List[str]								=		get_linear_schedule_with_warmup(
			    optimizer=UpperCamelCase      ,			num_warmup_steps=100      ,			num_training_steps=(len(UpperCamelCase ) * 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.
			lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      List[Any]								=		accelerator.prepare(
			    UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )

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

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

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

						lowerCamelCase__ :      List[Any]								=		metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"epoch {epoch}:"      ,			UpperCamelCase )

						# 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(UpperCamelCase ),
									        """epoch""": epoch,
									    }      ,			step=UpperCamelCase      ,			)

    # 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      _A				()  ->Union[str, Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		argparse.ArgumentParser(description="""Simple example of training script.""" )
			parser.add_argument(
			    """--mixed_precision"""      ,			type=UpperCamelCase      ,			default=UpperCamelCase      ,			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=UpperCamelCase      ,			default="""logs"""      ,			help="""Location on where to store experiment tracking logs` and relevent project information"""      ,			)
			lowerCamelCase__ :      Any								=		parser.parse_args()
			lowerCamelCase__ :      List[Any]								=		{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(UpperCamelCase      ,			UpperCamelCase )


if __name__ == "__main__":
				main()








import unittest

import numpy as np
import requests

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

				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				_lowercase     							=						False

if is_vision_available():
				from PIL import Image

				from transformers import PixaStructImageProcessor
class 				__A					( unittest.TestCase      ):





			def __init__(self							,    __magic_name__							,    __magic_name__=7							,    __magic_name__=3							,    __magic_name__=18							,    __magic_name__=30							,    __magic_name__=400							,    __magic_name__=None							,    __magic_name__=True							,    __magic_name__=True							,    __magic_name__=None							,    ):
						lowerCamelCase__ :      Optional[Any]								=		size if size is not None else {"""height""": 20, """width""": 20}
						lowerCamelCase__ :      List[str]								=		parent
						lowerCamelCase__ :      str								=		batch_size
						lowerCamelCase__ :      List[str]								=		num_channels
						lowerCamelCase__ :      Optional[int]								=		image_size
						lowerCamelCase__ :      Union[str, Any]								=		min_resolution
						lowerCamelCase__ :      List[Any]								=		max_resolution
						lowerCamelCase__ :      Optional[int]								=		size
						lowerCamelCase__ :      List[str]								=		do_normalize
						lowerCamelCase__ :      str								=		do_convert_rgb
						lowerCamelCase__ :      str								=		[512, 1024, 2048, 4096]
						lowerCamelCase__ :      Any								=		patch_size if patch_size is not None else {"""height""": 16, """width""": 16}





			def        _snake_case    (self				):
						return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
						lowerCamelCase__ :      int								=		Image.open(requests.get(__magic_name__							,    stream=__magic_name__				).raw				).convert("""RGB"""				)
						return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					, reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''					, )
@require_torch
@require_vision
class 				__A					( A_					, unittest.TestCase      ):
			UpperCamelCase					:Union[str, Any]     =			PixaStructImageProcessor if is_vision_available() else None





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		PixaStructImageProcessingTester(self				)





			@property
			def        _snake_case    (self				):
						return self.image_processor_tester.prepare_image_processor_dict()





			def        _snake_case    (self				):
						lowerCamelCase__ :      int								=		self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(__magic_name__							,    """do_normalize"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """do_convert_rgb"""				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		self.image_processor_tester.prepare_dummy_image()

						lowerCamelCase__ :      Tuple								=		self.image_processing_class(**self.image_processor_dict				)
						lowerCamelCase__ :      List[str]								=		2048

						lowerCamelCase__ :      Any								=		image_processor(__magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				)
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,    torch.tensor(0.06_06				)							,    atol=1E-3							,    rtol=1E-3				)				)





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      List[Any]								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						lowerCamelCase__ :      Optional[Any]								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    Image.Image				)

						# Test not batched input
						lowerCamelCase__ :      Optional[Any]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      List[str]								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						lowerCamelCase__ :      Optional[Any]								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    Image.Image				)

						# Test not batched input
						lowerCamelCase__ :      List[Any]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						lowerCamelCase__ :      List[str]								=		True

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(__magic_name__				):
												lowerCamelCase__ :      Tuple								=		image_processor(
												    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches

									lowerCamelCase__ :      Optional[Any]								=		"""Hello"""

									lowerCamelCase__ :      Dict								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__							,    header_text=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      Tuple								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__							,    header_text=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      Any								=		self.image_processing_class(**self.image_processor_dict				)
						# create random numpy tensors
						lowerCamelCase__ :      Dict								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__							,    numpify=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    np.ndarray				)

						lowerCamelCase__ :      Optional[int]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      List[str]								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      List[str]								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      Optional[int]								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PyTorch tensors
						lowerCamelCase__ :      Dict								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__							,    torchify=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    torch.Tensor				)

						# Test not batched input
						lowerCamelCase__ :      List[str]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      Tuple								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					, reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''					, )
@require_torch
@require_vision
class 				__A					( A_					, unittest.TestCase      ):
			UpperCamelCase					:Optional[int]     =			PixaStructImageProcessor if is_vision_available() else None





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		PixaStructImageProcessingTester(self							,    num_channels=4				)
						lowerCamelCase__ :      Optional[int]								=		3





			@property
			def        _snake_case    (self				):
						return self.image_processor_tester.prepare_image_processor_dict()





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(__magic_name__							,    """do_normalize"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """do_convert_rgb"""				)				)





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      str								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						lowerCamelCase__ :      List[Any]								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    Image.Image				)

						# Test not batched input
						lowerCamelCase__ :      List[str]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * (self.image_processor_tester.num_channels - 1)
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      str								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )



