




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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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__   :    Optional[int] 					=      16
lowercase__   :    Tuple 					=      32

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  int:
					# Initialize accelerator
					if args.with_tracking:
										a											=   Accelerator(
										    cpu=args.cpu	,      mixed_precision=args.mixed_precision	,      log_with="all"	,      project_dir=args.project_dir)
					else:
										a											=   Accelerator(cpu=args.cpu	,      mixed_precision=args.mixed_precision)

					if hasattr(args.checkpointing_steps	,      "isdigit"):
										if args.checkpointing_steps == "epoch":
															a											=   args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															a											=   int(args.checkpointing_steps)
										else:
															raise ValueError(
															    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''')
					else:
										a											=   None
					# 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"])

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										a											=   os.path.split(snake_case__)[-1].split(".")[0]
										accelerator.init_trackers(snake_case__	,      snake_case__)

					a											=   AutoTokenizer.from_pretrained("bert-base-cased")
					a											=   load_dataset("glue"	,      "mrpc")
					a											=   evaluate.load("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=snake_case__	,      max_length=snake_case__)
										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(
										    snake_case__	,      batched=snake_case__	,      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")

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

					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(
										    snake_case__	,      padding="longest"	,      max_length=snake_case__	,      pad_to_multiple_of=snake_case__	,      return_tensors="pt"	,      )

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

					set_seed(snake_case__)

					# 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=snake_case__)

					# 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=snake_case__)

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

					# We need to keep track of how many total steps we have iterated over
					a											=   0
					# We also need to keep track of the stating epoch so files are named properly
					a											=   0

					# Potentially load in the weights and states from a previous save
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
															accelerator.load_state(args.resume_from_checkpoint)
															a											=   os.path.basename(args.resume_from_checkpoint)
										else:
															# Get the most recent checkpoint
															a											=   [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
															dirs.sort(key=os.path.getctime)
															a											=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										a											=   os.path.splitext(snake_case__)[0]

										if "epoch" in training_difference:
															a											=   int(training_difference.replace("epoch_"	,      "")) + 1
															a											=   None
										else:
															a											=   int(training_difference.replace("step_"	,      ""))
															a											=   resume_step // len(snake_case__)
															resume_step -= starting_epoch * len(snake_case__)

    # Now we train the model
					for epoch in range(snake_case__	,      snake_case__):
										model.train()
										if args.with_tracking:
															a											=   0
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															a											=   accelerator.skip_first_batches(snake_case__	,      snake_case__)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															a											=   train_dataloader
										for step, batch in enumerate(snake_case__):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device)
															a											=   model(**snake_case__)
															a											=   outputs.loss
															a											=   loss / gradient_accumulation_steps
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(snake_case__)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

															if isinstance(snake_case__	,      snake_case__):
																				a											=   f'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														a											=   os.path.join(args.output_dir	,      snake_case__)
																									accelerator.save_state(snake_case__)

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

										a											=   metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}:'''	,      snake_case__)
										if args.with_tracking:
															accelerator.log(
															    {
															        "accuracy": eval_metric["accuracy"],
															        "f1": eval_metric["f1"],
															        "train_loss": total_loss.item() / len(snake_case__),
															        "epoch": epoch,
															    }	,      step=snake_case__	,      )

										if checkpointing_steps == "epoch":
															a											=   f'''epoch_{epoch}'''
															if args.output_dir is not None:
																				a											=   os.path.join(args.output_dir	,      snake_case__)
															accelerator.save_state(snake_case__)

					if args.with_tracking:
										accelerator.end_training()

def       SCREAMING_SNAKE_CASE							(     )    ->  List[str]:
					a											=   argparse.ArgumentParser(description="Simple example of training script.")
					parser.add_argument(
					    "--mixed_precision"	,      type=snake_case__	,      default=snake_case__	,      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(
					    "--checkpointing_steps"	,      type=snake_case__	,      default=snake_case__	,      help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."	,      )
					parser.add_argument(
					    "--resume_from_checkpoint"	,      type=snake_case__	,      default=snake_case__	,      help="If the training should continue from a checkpoint folder."	,      )
					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(
					    "--output_dir"	,      type=snake_case__	,      default="."	,      help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."	,      )
					parser.add_argument(
					    "--project_dir"	,      type=snake_case__	,      default="logs"	,      help="Location on where to store experiment tracking logs` and relevent project information"	,      )
					a											=   parser.parse_args()
					a											=   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(snake_case__	,      snake_case__)


if __name__ == "__main__":
			main()







from collections import OrderedDict
from typing import Mapping

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


lowercase__   :    str 					=      logging.get_logger(__name__)

lowercase__   :    List[str] 					=      {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}




class     a__ (      UpperCamelCase__   ):
     a	:    Union[str, Any]	      =			MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
     a	:    Optional[int]	      =			"""mobilebert"""







     def __init__(			self						,	A=30522						,	A=512						,	A=24						,	A=4						,	A=512						,	A="relu"						,	A=0.0						,	A=0.1						,	A=512						,	A=2						,	A=0.0_2						,	A=1e-12						,	A=0						,	A=128						,	A=True						,	A=True						,	A=128						,	A=False						,	A=True						,	A=4						,	A="no_norm"						,	A=True						,	A=None						,	**A						,	)						->					str:



          '''simple docstring'''



          super().__init__(pad_token_id=A						,	**A				)

          a											=   vocab_size
          a											=   hidden_size
          a											=   num_hidden_layers
          a											=   num_attention_heads
          a											=   hidden_act
          a											=   intermediate_size
          a											=   hidden_dropout_prob
          a											=   attention_probs_dropout_prob
          a											=   max_position_embeddings
          a											=   type_vocab_size
          a											=   initializer_range
          a											=   layer_norm_eps
          a											=   embedding_size
          a											=   trigram_input
          a											=   use_bottleneck
          a											=   intra_bottleneck_size
          a											=   use_bottleneck_attention
          a											=   key_query_shared_bottleneck
          a											=   num_feedforward_networks
          a											=   normalization_type
          a											=   classifier_activation

          if self.use_bottleneck:
               a											=   intra_bottleneck_size
          else:
               a											=   hidden_size

          a											=   classifier_dropout







class     a__ (      UpperCamelCase__   ):







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



          '''simple docstring'''



          if self.task == "multiple-choice":
               a											=   {0: "batch", 1: "choice", 2: "sequence"}
          else:
               a											=   {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ]				)

