






'''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
#
########################################################################


_A     :      List[Any]   					=16
_A     :      List[str]   					=32





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase = 16				)				->	Any:
							lowerCamelCase__ :     Union[str, Any]	  =	AutoTokenizer.from_pretrained("""bert-base-cased"""				)
							lowerCamelCase__ :     Dict	  =	load_dataset("""glue"""    ,      """mrpc"""				)

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

							def collate_fn(UpperCamelCase				):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														lowerCamelCase__ :     str	  =	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__ :     List[Any]	  =	16
														elif accelerator.mixed_precision != "no":
																					lowerCamelCase__ :     Dict	  =	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__ :     List[Any]	  =	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

			_A     :      List[Any]   					=mocked_dataloaders  # noqa: F811





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Optional[Any]:
							# For testing only
							if os.environ.get("""TESTING_MOCKED_DATALOADERS"""    ,      UpperCamelCase				) == "1":
														lowerCamelCase__ :     str	  =	2
							# Initialize accelerator
							lowerCamelCase__ :     Optional[Any]	  =	Accelerator(cpu=args.cpu    ,      mixed_precision=args.mixed_precision				)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowerCamelCase__ :     Any	  =	config["""lr"""]
							lowerCamelCase__ :     Tuple	  =	int(config["""num_epochs"""]				)
							lowerCamelCase__ :     List[str]	  =	int(config["""seed"""]				)
							lowerCamelCase__ :     Dict	  =	int(config["""batch_size"""]				)

							lowerCamelCase__ :     List[Any]	  =	evaluate.load("""glue"""    ,      """mrpc"""				)

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

							set_seed(UpperCamelCase				)
							lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	get_dataloaders(UpperCamelCase    ,      UpperCamelCase				)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							lowerCamelCase__ :     List[str]	  =	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[Any]	  =	model.to(accelerator.device				)

							# Instantiate optimizer
							lowerCamelCase__ :     Union[str, Any]	  =	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__ :     Tuple	  =	accelerator.prepare(
							    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							# Now we train the model
							for epoch in range(UpperCamelCase				):
														model.train()
														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__ :     Union[str, Any]	  =	outputs.loss
																					lowerCamelCase__ :     str	  =	loss / gradient_accumulation_steps
																					accelerator.backward(UpperCamelCase				)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														lowerCamelCase__ :     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				)
																					with torch.no_grad():
																												lowerCamelCase__ :     int	  =	model(**UpperCamelCase				)
																					lowerCamelCase__ :     List[str]	  =	outputs.logits.argmax(dim=-1				)
																					lowerCamelCase__  ,		lowerCamelCase__ :     Any	  =	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(UpperCamelCase				) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			lowerCamelCase__ :     Union[str, Any]	  =	predictions[: len(eval_dataloader.dataset				) - samples_seen]
																																			lowerCamelCase__ :     List[Any]	  =	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=UpperCamelCase    ,      references=UpperCamelCase    ,      )

														lowerCamelCase__ :     int	  =	metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f'''epoch {epoch}:'''    ,      UpperCamelCase				)





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							lowerCamelCase__ :     Dict	  =	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."""				)
							lowerCamelCase__ :     Tuple	  =	parser.parse_args()
							lowerCamelCase__ :     Dict	  =	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(UpperCamelCase    ,      UpperCamelCase				)


if __name__ == "__main__":
			main()









'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A     :      Union[str, Any]   					=logging.get_logger(__name__)

_A     :      List[Any]   					={
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}

class _lowercase     (    _lowercase		):
							a    				=	"""deformable_detr"""
							a    				=	{
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    Tuple=True      ,							UpperCamelCase__:    Tuple=None      ,							UpperCamelCase__:    List[str]=3      ,							UpperCamelCase__:    Any=300      ,							UpperCamelCase__:    Optional[int]=1_024      ,							UpperCamelCase__:    int=6      ,							UpperCamelCase__:    str=1_024      ,							UpperCamelCase__:    Optional[Any]=8      ,							UpperCamelCase__:    Optional[Any]=6      ,							UpperCamelCase__:    Union[str, Any]=1_024      ,							UpperCamelCase__:    Tuple=8      ,							UpperCamelCase__:    str=0.0      ,							UpperCamelCase__:    Tuple=True      ,							UpperCamelCase__:    Any="relu"      ,							UpperCamelCase__:    Any=256      ,							UpperCamelCase__:    List[Any]=0.1      ,							UpperCamelCase__:    Optional[int]=0.0      ,							UpperCamelCase__:    Any=0.0      ,							UpperCamelCase__:    List[str]=0.02      ,							UpperCamelCase__:    str=1.0      ,							UpperCamelCase__:    Any=True      ,							UpperCamelCase__:    List[Any]=False      ,							UpperCamelCase__:    str="sine"      ,							UpperCamelCase__:    Optional[Any]="resnet50"      ,							UpperCamelCase__:    Optional[int]=True      ,							UpperCamelCase__:    Optional[int]=False      ,							UpperCamelCase__:    List[Any]=4      ,							UpperCamelCase__:    Any=4      ,							UpperCamelCase__:    int=4      ,							UpperCamelCase__:    int=False      ,							UpperCamelCase__:    Optional[Any]=300      ,							UpperCamelCase__:    str=False      ,							UpperCamelCase__:    int=1      ,							UpperCamelCase__:    Tuple=5      ,							UpperCamelCase__:    List[Any]=2      ,							UpperCamelCase__:    Optional[int]=1      ,							UpperCamelCase__:    int=1      ,							UpperCamelCase__:    Tuple=5      ,							UpperCamelCase__:    str=2      ,							UpperCamelCase__:    str=0.1      ,							UpperCamelCase__:    List[str]=0.25      ,							UpperCamelCase__:    Any=False      ,							**UpperCamelCase__:    Optional[Any]      ,							):
														if backbone_config is not None and use_timm_backbone:
																					raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""					)

														if not use_timm_backbone:
																					if backbone_config is None:
																												logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
																												lowerCamelCase__ :     Union[str, Any]	  =	CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]					)
																					elif isinstance(UpperCamelCase__      ,							UpperCamelCase__					):
																												lowerCamelCase__ :     Tuple	  =	backbone_config.get("""model_type"""					)
																												lowerCamelCase__ :     Dict	  =	CONFIG_MAPPING[backbone_model_type]
																												lowerCamelCase__ :     Optional[Any]	  =	config_class.from_dict(UpperCamelCase__					)
														lowerCamelCase__ :     Tuple	  =	use_timm_backbone
														lowerCamelCase__ :     Tuple	  =	backbone_config
														lowerCamelCase__ :     Union[str, Any]	  =	num_channels
														lowerCamelCase__ :     str	  =	num_queries
														lowerCamelCase__ :     int	  =	max_position_embeddings
														lowerCamelCase__ :     str	  =	d_model
														lowerCamelCase__ :     Dict	  =	encoder_ffn_dim
														lowerCamelCase__ :     Union[str, Any]	  =	encoder_layers
														lowerCamelCase__ :     int	  =	encoder_attention_heads
														lowerCamelCase__ :     str	  =	decoder_ffn_dim
														lowerCamelCase__ :     Optional[Any]	  =	decoder_layers
														lowerCamelCase__ :     Optional[int]	  =	decoder_attention_heads
														lowerCamelCase__ :     int	  =	dropout
														lowerCamelCase__ :     Optional[Any]	  =	attention_dropout
														lowerCamelCase__ :     List[Any]	  =	activation_dropout
														lowerCamelCase__ :     List[Any]	  =	activation_function
														lowerCamelCase__ :     int	  =	init_std
														lowerCamelCase__ :     Dict	  =	init_xavier_std
														lowerCamelCase__ :     Tuple	  =	encoder_layerdrop
														lowerCamelCase__ :     str	  =	auxiliary_loss
														lowerCamelCase__ :     int	  =	position_embedding_type
														lowerCamelCase__ :     Tuple	  =	backbone
														lowerCamelCase__ :     Tuple	  =	use_pretrained_backbone
														lowerCamelCase__ :     Optional[int]	  =	dilation
														# deformable attributes
														lowerCamelCase__ :     Optional[int]	  =	num_feature_levels
														lowerCamelCase__ :     Tuple	  =	encoder_n_points
														lowerCamelCase__ :     Tuple	  =	decoder_n_points
														lowerCamelCase__ :     Dict	  =	two_stage
														lowerCamelCase__ :     int	  =	two_stage_num_proposals
														lowerCamelCase__ :     Any	  =	with_box_refine
														if two_stage is True and with_box_refine is False:
																					raise ValueError("""If two_stage is True, with_box_refine must be True."""					)
														# Hungarian matcher
														lowerCamelCase__ :     Optional[int]	  =	class_cost
														lowerCamelCase__ :     List[str]	  =	bbox_cost
														lowerCamelCase__ :     Any	  =	giou_cost
														# Loss coefficients
														lowerCamelCase__ :     Union[str, Any]	  =	mask_loss_coefficient
														lowerCamelCase__ :     Tuple	  =	dice_loss_coefficient
														lowerCamelCase__ :     int	  =	bbox_loss_coefficient
														lowerCamelCase__ :     Optional[Any]	  =	giou_loss_coefficient
														lowerCamelCase__ :     Dict	  =	eos_coefficient
														lowerCamelCase__ :     Any	  =	focal_alpha
														lowerCamelCase__ :     Optional[Any]	  =	disable_custom_kernels
														super().__init__(is_encoder_decoder=UpperCamelCase__      ,							**UpperCamelCase__					)




							@property
							def 		lowerCamelCase_    (							self:    Dict					):
														return self.encoder_attention_heads




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														return self.d_model




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     Optional[int]	  =	copy.deepcopy(self.__dict__					)
														if self.backbone_config is not None:
																					lowerCamelCase__ :     int	  =	self.backbone_config.to_dict()
														lowerCamelCase__ :     int	  =	self.__class__.model_type
														return output

