





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

a        =						16
a        =						32

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase = 16      )						->					int:

   '''simple docstring'''







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

   def tokenize_function(__UpperCAmelCase      ):
      # max_length=None => use the model max length (it's actually the default)
      __SCREAMING_SNAKE_CASE      							=       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():
      __SCREAMING_SNAKE_CASE      							=       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
   __SCREAMING_SNAKE_CASE      							=       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.
      __SCREAMING_SNAKE_CASE      							=       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":
         __SCREAMING_SNAKE_CASE      							=       16
      elif accelerator.mixed_precision != "no":
         __SCREAMING_SNAKE_CASE      							=       8
      else:
         __SCREAMING_SNAKE_CASE      							=       None

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

   # Instantiate dataloaders.
   __SCREAMING_SNAKE_CASE      							=       DataLoader(
       tokenized_datasets["""train"""]							,	shuffle=__UpperCAmelCase							,	collate_fn=__UpperCAmelCase							,	batch_size=__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       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        =						mocked_dataloaders  # noqa: F811

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					int:

   '''simple docstring'''







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

   __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       get_dataloaders(__UpperCAmelCase							,	__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       evaluate.load("""glue"""							,	"""mrpc"""      )

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

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __SCREAMING_SNAKE_CASE      							=       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).
   __SCREAMING_SNAKE_CASE      							=       model.to(accelerator.device      )

   # Instantiate optimizer
   __SCREAMING_SNAKE_CASE      							=       AdamW(params=model.parameters()							,	lr=__UpperCAmelCase      )

   # Instantiate scheduler
   __SCREAMING_SNAKE_CASE      							=       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.
   __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       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:
      __SCREAMING_SNAKE_CASE      							=       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:
         __SCREAMING_SNAKE_CASE      							=       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      )
         __SCREAMING_SNAKE_CASE      							=       model(**__UpperCAmelCase      )
         __SCREAMING_SNAKE_CASE      							=       outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         __SCREAMING_SNAKE_CASE      							=       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():
            __SCREAMING_SNAKE_CASE      							=       model(**__UpperCAmelCase      )
         __SCREAMING_SNAKE_CASE      							=       outputs.logits.argmax(dim=-1      )
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       accelerator.gather_for_metrics((predictions, batch["""labels"""])      )
         metric.add_batch(
             predictions=__UpperCAmelCase							,	references=__UpperCAmelCase							,	)

      __SCREAMING_SNAKE_CASE      							=       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 						__magic_name__       (							)						->					Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       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"""							,	)
   __SCREAMING_SNAKE_CASE      							=       parser.parse_args()
   __SCREAMING_SNAKE_CASE      							=       {"""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        =						logging.get_logger(__name__)

a        =						{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class __a (   _snake_case			):
   __UpperCamelCase   : Dict	      =	'deta'
   __UpperCamelCase   : List[str]	      =	{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }





   def __init__(					self				:			Tuple				,lowerCamelCase				:			List[Any]=None				,lowerCamelCase				:			Any=900				,lowerCamelCase				:			int=2048				,lowerCamelCase				:			Any=6				,lowerCamelCase				:			Optional[Any]=2048				,lowerCamelCase				:			str=8				,lowerCamelCase				:			Union[str, Any]=6				,lowerCamelCase				:			List[str]=1024				,lowerCamelCase				:			int=8				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Optional[int]="relu"				,lowerCamelCase				:			int=256				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Optional[Any]=0.0				,lowerCamelCase				:			Tuple=0.0				,lowerCamelCase				:			List[str]=0.02				,lowerCamelCase				:			Any=1.0				,lowerCamelCase				:			Optional[int]=True				,lowerCamelCase				:			int=False				,lowerCamelCase				:			Optional[Any]="sine"				,lowerCamelCase				:			Dict=5				,lowerCamelCase				:			List[Any]=4				,lowerCamelCase				:			Optional[Any]=4				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			int=300				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Tuple=True				,lowerCamelCase				:			int=1				,lowerCamelCase				:			Tuple=5				,lowerCamelCase				:			Union[str, Any]=2				,lowerCamelCase				:			Tuple=1				,lowerCamelCase				:			int=1				,lowerCamelCase				:			str=5				,lowerCamelCase				:			Optional[Any]=2				,lowerCamelCase				:			List[Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.25				,**lowerCamelCase				:			int				,):

      '''simple docstring'''

      if backbone_config is None:
         logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""    )
         __SCREAMING_SNAKE_CASE      							=       CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]    )
      else:
         if isinstance(lowerCamelCase				,lowerCamelCase    ):
            __SCREAMING_SNAKE_CASE      							=       backbone_config.pop("""model_type"""    )
            __SCREAMING_SNAKE_CASE      							=       CONFIG_MAPPING[backbone_model_type]
            __SCREAMING_SNAKE_CASE      							=       config_class.from_dict(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       backbone_config
      __SCREAMING_SNAKE_CASE      							=       num_queries
      __SCREAMING_SNAKE_CASE      							=       max_position_embeddings
      __SCREAMING_SNAKE_CASE      							=       d_model
      __SCREAMING_SNAKE_CASE      							=       encoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       encoder_layers
      __SCREAMING_SNAKE_CASE      							=       encoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       decoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       decoder_layers
      __SCREAMING_SNAKE_CASE      							=       decoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       dropout
      __SCREAMING_SNAKE_CASE      							=       attention_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_function
      __SCREAMING_SNAKE_CASE      							=       init_std
      __SCREAMING_SNAKE_CASE      							=       init_xavier_std
      __SCREAMING_SNAKE_CASE      							=       encoder_layerdrop
      __SCREAMING_SNAKE_CASE      							=       auxiliary_loss
      __SCREAMING_SNAKE_CASE      							=       position_embedding_type
      # deformable attributes
      __SCREAMING_SNAKE_CASE      							=       num_feature_levels
      __SCREAMING_SNAKE_CASE      							=       encoder_n_points
      __SCREAMING_SNAKE_CASE      							=       decoder_n_points
      __SCREAMING_SNAKE_CASE      							=       two_stage
      __SCREAMING_SNAKE_CASE      							=       two_stage_num_proposals
      __SCREAMING_SNAKE_CASE      							=       with_box_refine
      __SCREAMING_SNAKE_CASE      							=       assign_first_stage
      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
      __SCREAMING_SNAKE_CASE      							=       class_cost
      __SCREAMING_SNAKE_CASE      							=       bbox_cost
      __SCREAMING_SNAKE_CASE      							=       giou_cost
      # Loss coefficients
      __SCREAMING_SNAKE_CASE      							=       mask_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       dice_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       bbox_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       giou_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       eos_coefficient
      __SCREAMING_SNAKE_CASE      							=       focal_alpha
      super().__init__(is_encoder_decoder=lowerCamelCase				,**lowerCamelCase    )





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      return self.encoder_attention_heads





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      return self.d_model





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       copy.deepcopy(self.__dict__    )
      __SCREAMING_SNAKE_CASE      							=       self.backbone_config.to_dict()
      __SCREAMING_SNAKE_CASE      							=       self.__class__.model_type
      return output





