




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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


lowercase__				:      str	   = 16
lowercase__				:      Any	   = 32

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase = 16)      ->					Dict:
					a     =				AutoTokenizer.from_pretrained("bert-base-cased")
					a     =				load_dataset("glue"		, "mrpc")

					def tokenize_function(__UpperCamelCase):
										# max_length=None => use the model max length (it's actually the default)
										a     =				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():
										a     =				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
					a     =				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.
										a     =				1_28 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     =				16
										elif accelerator.mixed_precision != "no":
															a     =				8
										else:
															a     =				None

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

					# Instantiate dataloaders.
					a     =				DataLoader(
					    tokenized_datasets["train"]		, shuffle=__UpperCamelCase		, collate_fn=__UpperCamelCase		, batch_size=__UpperCamelCase)
					a     =				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__				:      Tuple	   = mocked_dataloaders  # noqa: F811

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:
					# For testing only
					if os.environ.get("TESTING_MOCKED_DATALOADERS"		, __UpperCamelCase) == "1":
										a     =				2
					# New Code #
					a     =				int(args.gradient_accumulation_steps)
					a     =				int(args.local_sgd_steps)
					# Initialize accelerator
					a     =				Accelerator(
					    cpu=args.cpu		, mixed_precision=args.mixed_precision		, gradient_accumulation_steps=__UpperCamelCase)
					if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
										raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)")
					# 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(__UpperCamelCase)
					a     =				get_dataloaders(__UpperCamelCase		, __UpperCamelCase)
					# 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=__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).
					a     =				model.to(accelerator.device)

					# Instantiate optimizer
					a     =				AdamW(params=model.parameters()		, lr=__UpperCamelCase)

					# Instantiate scheduler
					a     =				get_linear_schedule_with_warmup(
					    optimizer=__UpperCamelCase		, num_warmup_steps=1_00		, num_training_steps=(len(__UpperCamelCase) * 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     =				accelerator.prepare(
					    __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)

					# Now we train the model
					for epoch in range(__UpperCamelCase):
										model.train()
										with LocalSGD(
										    accelerator=__UpperCamelCase		, model=__UpperCamelCase		, local_sgd_steps=__UpperCamelCase		, enabled=local_sgd_steps is not None) as local_sgd:
															for step, batch in enumerate(__UpperCamelCase):
																				# 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(__UpperCamelCase):
																									a     =				model(**__UpperCamelCase)
																									a     =				output.loss
																									accelerator.backward(__UpperCamelCase)
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# LocalSGD-specific line
																									local_sgd.step()

										model.eval()
										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():
																				a     =				model(**__UpperCamelCase)
															a     =				outputs.logits.argmax(dim=-1)
															a     =				accelerator.gather_for_metrics((predictions, batch["labels"]))
															metric.add_batch(
															    predictions=__UpperCamelCase		, references=__UpperCamelCase		, )

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

def  SCREAMING_SNAKE_CASE							(       )      ->					Dict:
					a     =				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."		, )
					# New Code #
					parser.add_argument(
					    "--gradient_accumulation_steps"		, type=__UpperCamelCase		, default=1		, help="The number of minibatches to be ran before gradients are accumulated."		, )
					parser.add_argument(
					    "--local_sgd_steps"		, type=__UpperCamelCase		, default=8		, help="Number of local SGD steps or None to disable local SGD")
					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': 42, 'batch_size': 16}
					training_function(__UpperCamelCase		, __UpperCamelCase)


if __name__ == "__main__":
			main()







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase__				:      Optional[Any]	   = logging.get_logger(__name__)

lowercase__				:      str	   = {
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}




class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     """table-transformer"""
     a		:      Tuple		 =     ["""past_key_values"""]
     a		:      int		 =     {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(     self  ,		A=True  ,		A=None  ,		A=3  ,		A=100  ,		A=6  ,		A=2048  ,		A=8  ,		A=6  ,		A=2048  ,		A=8  ,		A=0.0  ,		A=0.0  ,		A=True  ,		A="relu"  ,		A=256  ,		A=0.1  ,		A=0.0  ,		A=0.0  ,		A=0.0_2  ,		A=1.0  ,		A=False  ,		A="sine"  ,		A="resnet50"  ,		A=True  ,		A=False  ,		A=1  ,		A=5  ,		A=2  ,		A=1  ,		A=1  ,		A=5  ,		A=2  ,		A=0.1  ,		**A  ,		)  ->   List[str]:



          '''simple docstring'''



          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."			)
                    a     =				CONFIG_MAPPING["resnet"](out_features=["stage4"]			)
               elif isinstance(A  ,		A			):
                    a     =				backbone_config.get("model_type"			)
                    a     =				CONFIG_MAPPING[backbone_model_type]
                    a     =				config_class.from_dict(A			)
               # set timm attributes to None
               a  ,				a  ,				a     =				None, None, None

          a     =				use_timm_backbone
          a     =				backbone_config
          a     =				num_channels
          a     =				num_queries
          a     =				d_model
          a     =				encoder_ffn_dim
          a     =				encoder_layers
          a     =				encoder_attention_heads
          a     =				decoder_ffn_dim
          a     =				decoder_layers
          a     =				decoder_attention_heads
          a     =				dropout
          a     =				attention_dropout
          a     =				activation_dropout
          a     =				activation_function
          a     =				init_std
          a     =				init_xavier_std
          a     =				encoder_layerdrop
          a     =				decoder_layerdrop
          a     =				encoder_layers
          a     =				auxiliary_loss
          a     =				position_embedding_type
          a     =				backbone
          a     =				use_pretrained_backbone
          a     =				dilation
          # Hungarian matcher
          a     =				class_cost
          a     =				bbox_cost
          a     =				giou_cost
          # Loss coefficients
          a     =				mask_loss_coefficient
          a     =				dice_loss_coefficient
          a     =				bbox_loss_coefficient
          a     =				giou_loss_coefficient
          a     =				eos_coefficient
          super().__init__(is_encoder_decoder=A  ,		**A			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.encoder_attention_heads







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.d_model







class       a__	( UpperCamelCase__				):
     a		:      Any		 =     version.parse("""1.11"""				)







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



          '''simple docstring'''



          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("pixel_mask", {0: "batch"}),
              ]			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-5







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return 12

