

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
#
########################################################################


A__						=							16
A__						=							32




def 					_lowercase     (				a_				:					Accelerator					,a_				:					int = 1_6				)				->	Any:




					'''simple docstring'''







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

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

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

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

					# Instantiate dataloaders.
					__magic_name__							=    DataLoader(
					    tokenized_datasets['train']					,shuffle=a_					,collate_fn=a_					,batch_size=a_					,drop_last=a_				)
					__magic_name__							=    DataLoader(
					    tokenized_datasets['validation']					,shuffle=a_					,collate_fn=a_					,batch_size=a_					,drop_last=(accelerator.mixed_precision == 'fp8')					,)

					return train_dataloader, eval_dataloader




def 					_lowercase     (				a_				:					Union[str, Any]					,a_				:					Optional[Any]				)				->	Union[str, Any]:




					'''simple docstring'''







					__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(a_				)
					__magic_name__,	__magic_name__							=    get_dataloaders(a_					,a_				)
					# 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=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).
					__magic_name__							=    model.to(accelerator.device				)
					# Instantiate optimizer
					__magic_name__							=    AdamW(params=model.parameters()					,lr=a_				)

					# Instantiate scheduler
					__magic_name__							=    get_linear_schedule_with_warmup(
					    optimizer=a_					,num_warmup_steps=1_0_0					,num_training_steps=(len(a_				) * 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__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							=    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				)
															__magic_name__							=    model(**a_				)
															__magic_name__							=    outputs.loss
															__magic_name__							=    loss / gradient_accumulation_steps
															accelerator.backward(a_				)
															if step % gradient_accumulation_steps == 0:
																				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():
																				__magic_name__							=    model(**a_				)
															__magic_name__							=    outputs.logits.argmax(dim=-1				)
															__magic_name__,	__magic_name__							=    accelerator.gather_for_metrics((predictions, batch['labels'])				)
															metric.add_batch(
															    predictions=a_					,references=a_					,)

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




def 					_lowercase     (				)				->	Optional[Any]:




					'''simple docstring'''







					__magic_name__							=    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.'					,)
					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': 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__						=							logging.get_logger(__name__)

A__						=							{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[Any]       				=  "layoutlmv3"







	def __init__(   self: Tuple  , __UpperCamelCase: List[Any]=5_02_65  , __UpperCamelCase: Union[str, Any]=7_68  , __UpperCamelCase: Optional[int]=12  , __UpperCamelCase: List[str]=12  , __UpperCamelCase: List[Any]=30_72  , __UpperCamelCase: Union[str, Any]="gelu"  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: List[Any]=5_12  , __UpperCamelCase: int=2  , __UpperCamelCase: Union[str, Any]=0.02  , __UpperCamelCase: Dict=1E-5  , __UpperCamelCase: int=1  , __UpperCamelCase: Dict=0  , __UpperCamelCase: Tuple=2  , __UpperCamelCase: Any=10_24  , __UpperCamelCase: Union[str, Any]=1_28  , __UpperCamelCase: Dict=1_28  , __UpperCamelCase: Any=True  , __UpperCamelCase: Any=32  , __UpperCamelCase: int=1_28  , __UpperCamelCase: List[str]=64  , __UpperCamelCase: Union[str, Any]=2_56  , __UpperCamelCase: int=True  , __UpperCamelCase: Optional[int]=True  , __UpperCamelCase: Union[str, Any]=True  , __UpperCamelCase: Union[str, Any]=2_24  , __UpperCamelCase: Tuple=3  , __UpperCamelCase: Any=16  , __UpperCamelCase: List[Any]=None  , **__UpperCamelCase: str  , ):

						'''simple docstring'''





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




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Any       				=  version.parse("1.12"			)







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''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 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						return 1E-5







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return 12







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: "ProcessorMixin"  , __UpperCamelCase: int = -1  , __UpperCamelCase: int = -1  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional["TensorType"] = None  , __UpperCamelCase: int = 3  , __UpperCamelCase: int = 40  , __UpperCamelCase: int = 40  , ):

						'''simple docstring'''





						setattr(processor.image_processor  , 'apply_ocr'  , __UpperCamelCase      )

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

						# Generate dummy bounding boxes
						__magic_name__							=    [[[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)
						__magic_name__							=    self._generate_dummy_images(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )

						__magic_name__							=    dict(
						    processor(
						        __UpperCamelCase  , text=__UpperCamelCase  , boxes=__UpperCamelCase  , return_tensors=__UpperCamelCase  , )      )

						return inputs




