





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
# and perform gradient accumulation
#
# 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
#
########################################################################


A    :					Optional[int] =      1_6
A    :					Tuple =      3_2







def    __lowerCamelCase					(							__a   :Accelerator    ,     __a   :int = 1_6    )		->				Tuple:






		"""simple docstring"""

		A__        =   AutoTokenizer.from_pretrained("""bert-base-cased"""    )
		A__        =   load_dataset("""glue"""    ,     """mrpc"""    )

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

		def collate_fn(__a   :str    ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				A__        =   1_2_8 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__        =   1_6
				elif accelerator.mixed_precision != "no":
						A__        =   8
				else:
						A__        =   None

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

		# Instantiate dataloaders.
		A__        =   DataLoader(
		    tokenized_datasets["""train"""]    ,     shuffle=__a    ,     collate_fn=__a    ,     batch_size=__a    )
		A__        =   DataLoader(
		    tokenized_datasets["""validation"""]    ,     shuffle=__a    ,     collate_fn=__a    ,     batch_size=__a    )

		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

					A    :					List[str] =      mocked_dataloaders  # noqa: F811







def    __lowerCamelCase					(							__a   :Any    ,     __a   :Optional[Any]    )		->				List[str]:






		"""simple docstring"""

		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""    ,     __a    ) == "1":
				A__        =   2
		# New Code #
		A__        =   int(args.gradient_accumulation_steps    )
		# Initialize accelerator
		A__        =   Accelerator(
		    cpu=args.cpu    ,     mixed_precision=args.mixed_precision    ,     gradient_accumulation_steps=__a    )
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""    )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		A__        =   config["""lr"""]
		A__        =   int(config["""num_epochs"""]    )
		A__        =   int(config["""seed"""]    )
		A__        =   int(config["""batch_size"""]    )

		A__        =   evaluate.load("""glue"""    ,     """mrpc"""    )

		set_seed(__a    )
		A__      ,			A__        =   get_dataloaders(__a    ,     __a    )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		A__        =   AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""    ,     return_dict=__a    )

		# 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__        =   model.to(accelerator.device    )

		# Instantiate optimizer
		A__        =   AdamW(params=model.parameters()    ,     lr=__a    )

		# Instantiate scheduler
		A__        =   get_linear_schedule_with_warmup(
		    optimizer=__a    ,     num_warmup_steps=1_0_0    ,     num_training_steps=(len(__a    ) * num_epochs)    ,     )

		# 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__        =   accelerator.prepare(
		    __a    ,     __a    ,     __a    ,     __a    ,     __a    )

		# Now we train the model
		for epoch in range(__a    ):
				model.train()
				for step, batch in enumerate(__a    ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device    )
						# New code #
						# We use the new `accumulate` context manager to perform gradient accumulation
						# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
						with accelerator.accumulate(__a    ):
								A__        =   model(**__a    )
								A__        =   output.loss
								accelerator.backward(__a    )
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

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







def    __lowerCamelCase					(							)		->				Any:






		"""simple docstring"""

		A__        =   argparse.ArgumentParser(description="""Simple example of training script."""    )
		parser.add_argument(
		    """--mixed_precision"""    ,     type=__a    ,     default=__a    ,     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."""    ,     )
		# New Code #
		parser.add_argument(
		    """--gradient_accumulation_steps"""    ,     type=__a    ,     default=1    ,     help="""The number of minibatches to be ran before gradients are accumulated."""    ,     )
		parser.add_argument("""--cpu"""    ,     action="""store_true"""    ,     help="""If passed, will train on the CPU."""    )
		A__        =   parser.parse_args()
		A__        =   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
		training_function(__a    ,     __a    )


if __name__ == "__main__":
					main()










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


A    :					str =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Optional[Any]							=    '''layoutlmv3'''






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Optional[int]=5_02_65							,	__lowerCAmelCase   :    Tuple=7_68							,	__lowerCAmelCase   :    Union[str, Any]=12							,	__lowerCAmelCase   :    Any=12							,	__lowerCAmelCase   :    List[Any]=30_72							,	__lowerCAmelCase   :    Optional[int]="gelu"							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    Dict=5_12							,	__lowerCAmelCase   :    Any=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-5							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    int=0							,	__lowerCAmelCase   :    Dict=2							,	__lowerCAmelCase   :    Tuple=10_24							,	__lowerCAmelCase   :    List[str]=1_28							,	__lowerCAmelCase   :    Optional[int]=1_28							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Any=1_28							,	__lowerCAmelCase   :    str=64							,	__lowerCAmelCase   :    Optional[int]=2_56							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    int=2_24							,	__lowerCAmelCase   :    Dict=3							,	__lowerCAmelCase   :    List[Any]=16							,	__lowerCAmelCase   :    Dict=None							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Dict:







					"""simple docstring"""






					super().__init__(
					    vocab_size=__lowerCAmelCase							,	hidden_size=__lowerCAmelCase							,	num_hidden_layers=__lowerCAmelCase							,	num_attention_heads=__lowerCAmelCase							,	intermediate_size=__lowerCAmelCase							,	hidden_act=__lowerCAmelCase							,	hidden_dropout_prob=__lowerCAmelCase							,	attention_probs_dropout_prob=__lowerCAmelCase							,	max_position_embeddings=__lowerCAmelCase							,	type_vocab_size=__lowerCAmelCase							,	initializer_range=__lowerCAmelCase							,	layer_norm_eps=__lowerCAmelCase							,	pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase							,	)
					A__        =   max_ad_position_embeddings
					A__        =   coordinate_size
					A__        =   shape_size
					A__        =   has_relative_attention_bias
					A__        =   rel_pos_bins
					A__        =   max_rel_pos
					A__        =   has_spatial_attention_bias
					A__        =   rel_ad_pos_bins
					A__        =   max_rel_ad_pos
					A__        =   text_embed
					A__        =   visual_embed
					A__        =   input_size
					A__        =   num_channels
					A__        =   patch_size
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    version.parse('''1.12'''    )






			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task in ["question-answering", "sequence-classification"]:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]   )
					else:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
							    ]   )






			@property
			def    a_       (			self   :    Optional[int]   )	->	float:







					"""simple docstring"""






					return 1e-5






			@property
			def    a_       (			self   :    Tuple   )	->	int:







					"""simple docstring"""






					return 12







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    "ProcessorMixin"							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    Optional["TensorType"] = None							,	__lowerCAmelCase   :    int = 3							,	__lowerCAmelCase   :    int = 40							,	__lowerCAmelCase   :    int = 40							,	)	->	Mapping[str, Any]:







					"""simple docstring"""






					setattr(processor.image_processor							,	"""apply_ocr"""							,	__lowerCAmelCase   )

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_batch							,	num_token_to_add=0   )
					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					A__        =   processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase   )
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_sequence							,	num_token_to_add=__lowerCAmelCase   )
					# Generate dummy inputs according to compute batch and sequence
					A__        =   [[""" """.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					A__        =   [[[48, 84, 73, 1_28]]] * batch_size

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
					A__        =   self._generate_dummy_images(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

					A__        =   dict(
					    processor(
					        __lowerCAmelCase							,	text=__lowerCAmelCase							,	boxes=__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	)   )

					return inputs



