import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase		:				Optional[Any]	       =		16
_lowercase		:				int	       =		32




def 							lowerCamelCase			(   UpperCAmelCase__ :      Accelerator   ,   UpperCAmelCase__ :      int = 16   ,   UpperCAmelCase__ :      str = "bert-base-cased"				)    ->   Tuple:
							lowercase_      :    str       		= AutoTokenizer.from_pretrained(UpperCAmelCase__				)
							lowercase_      :    Optional[int]       		= load_dataset("""glue"""   ,   """mrpc"""				)

							def tokenize_function(UpperCAmelCase__ :      str				):
														# max_length=None => use the model max length (it's actually the default)
														lowercase_      :    Optional[int]       		= 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
							lowercase_      :    Optional[Any]       		= datasets.map(
							    UpperCAmelCase__   ,   batched=UpperCAmelCase__   ,   remove_columns=["""idx""", """sentence1""", """sentence2"""]   ,   load_from_cache_file=UpperCAmelCase__				)

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							lowercase_      :    Tuple       		= tokenized_datasets.rename_column("""label"""   ,   """labels"""				)

							def collate_fn(UpperCAmelCase__ :      List[str]				):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(UpperCAmelCase__   ,   padding="""max_length"""   ,   max_length=128   ,   return_tensors="""pt"""				)
														return tokenizer.pad(UpperCAmelCase__   ,   padding="""longest"""   ,   return_tensors="""pt"""				)

							# Instantiate dataloaders.
							lowercase_      :    Optional[int]       		= DataLoader(
							    tokenized_datasets["""train"""]   ,   shuffle=UpperCAmelCase__   ,   collate_fn=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__				)
							lowercase_      :    str       		= DataLoader(
							    tokenized_datasets["""validation"""]   ,   shuffle=UpperCAmelCase__   ,   collate_fn=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__				)

							return train_dataloader, eval_dataloader




def 							lowerCamelCase			(   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      Dict				)    ->   Tuple:
							model.eval()
							lowercase_      :    List[str]       		= 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():
																					lowercase_      :    Union[str, Any]       		= model(**UpperCAmelCase__				)
														lowercase_      :    int       		= outputs.logits.argmax(dim=-1				)
														# It is slightly faster to call this once, than multiple times
														lowercase_      :    List[Any]       		= accelerator.gather(
														    (predictions, batch["""labels"""])				)  # If we are in a multiprocess environment, the last batch has duplicates
														if accelerator.use_distributed:
																					if step == len(UpperCAmelCase__				) - 1:
																												lowercase_      :    Optional[Any]       		= predictions[: len(eval_dataloader.dataset				) - samples_seen]
																												lowercase_      :    List[str]       		= references[: len(eval_dataloader.dataset				) - samples_seen]
																					else:
																												samples_seen += references.shape[0]
														metric.add_batch(
														    predictions=UpperCAmelCase__   ,   references=UpperCAmelCase__   ,   )

							lowercase_      :    List[str]       		= metric.compute()
							return eval_metric["accuracy"]




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[Any]   ,   UpperCAmelCase__ :      Dict				)    ->   List[str]:
							# Initialize accelerator
							lowercase_      :    Any       		= Accelerator()

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowercase_      :    List[Any]       		= config["""lr"""]
							lowercase_      :    int       		= int(config["""num_epochs"""]				)
							lowercase_      :    Dict       		= int(config["""seed"""]				)
							lowercase_      :    Optional[Any]       		= int(config["""batch_size"""]				)
							lowercase_      :    Tuple       		= args.model_name_or_path

							set_seed(UpperCAmelCase__				)
							lowercase_      :    Tuple       		= get_dataloaders(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							lowercase_      :    int       		= AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__   ,   return_dict=UpperCAmelCase__				)

							# Instantiate optimizer
							lowercase_      :    Optional[int]       		= (
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							lowercase_      :    str       		= optimizer_cls(params=model.parameters()   ,   lr=UpperCAmelCase__				)

							if accelerator.state.deepspeed_plugin is not None:
														lowercase_      :    Tuple       		= accelerator.state.deepspeed_plugin.deepspeed_config[
														    """gradient_accumulation_steps"""
														]
							else:
														lowercase_      :    List[Any]       		= 1
							lowercase_      :    List[str]       		= (len(UpperCAmelCase__				) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														lowercase_      :    Tuple       		= get_linear_schedule_with_warmup(
														    optimizer=UpperCAmelCase__   ,   num_warmup_steps=0   ,   num_training_steps=UpperCAmelCase__   ,   )
							else:
														lowercase_      :    str       		= DummyScheduler(UpperCAmelCase__   ,   total_num_steps=UpperCAmelCase__   ,   warmup_num_steps=0				)

							# 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.
							lowercase_      :    Optional[int]       		= accelerator.prepare(
							    UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)

							# We need to keep track of how many total steps we have iterated over
							lowercase_      :    Any       		= 0
							# We also need to keep track of the stating epoch so files are named properly
							lowercase_      :    Optional[Any]       		= 0
							lowercase_      :    Union[str, Any]       		= evaluate.load("""glue"""   ,   """mrpc"""				)
							lowercase_      :    Optional[Any]       		= num_epochs

							if args.partial_train_epoch is not None:
														lowercase_      :    List[str]       		= args.partial_train_epoch

							if args.resume_from_checkpoint:
														accelerator.load_state(args.resume_from_checkpoint				)
														lowercase_      :    Optional[int]       		= args.resume_from_checkpoint.split("""epoch_"""				)[1]
														lowercase_      :    str       		= """"""
														for char in epoch_string:
																					if char.isdigit():
																												state_epoch_num += char
																					else:
																												break
														lowercase_      :    Union[str, Any]       		= int(UpperCAmelCase__				) + 1
														lowercase_      :    Optional[Any]       		= evaluation_loop(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)
														accelerator.print("""resumed checkpoint performance:"""   ,   UpperCAmelCase__				)
														accelerator.print("""resumed checkpoint's scheduler's lr:"""   ,   lr_scheduler.get_lr()[0]				)
														accelerator.print("""resumed optimizers's lr:"""   ,   optimizer.param_groups[0]["""lr"""]				)
														with open(os.path.join(args.output_dir   ,   F'''state_{starting_epoch-1}.json'''				)   ,   """r"""				) as f:
																					lowercase_      :    Any       		= json.load(UpperCAmelCase__				)
																					assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																					assert (
																					    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																					), "Scheduler learning rate mismatch, loading from checkpoint failed"
																					assert (
																					    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																					), "Optimizer learning rate mismatch, loading from checkpoint failed"
																					assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																					return

    # Now we train the model
							lowercase_      :    Optional[int]       		= {}
							for epoch in range(UpperCAmelCase__   ,   UpperCAmelCase__				):
														model.train()
														for step, batch in enumerate(UpperCAmelCase__				):
																					lowercase_      :    Union[str, Any]       		= model(**UpperCAmelCase__				)
																					lowercase_      :    Dict       		= outputs.loss
																					lowercase_      :    List[str]       		= loss / gradient_accumulation_steps
																					accelerator.backward(UpperCAmelCase__				)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1
														lowercase_      :    List[str]       		= F'''epoch_{epoch}'''
														lowercase_      :    Dict       		= os.path.join(args.output_dir   ,   UpperCAmelCase__				)
														accelerator.save_state(UpperCAmelCase__				)
														lowercase_      :    Dict       		= evaluation_loop(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)
														lowercase_      :    Optional[int]       		= accuracy
														lowercase_      :    List[str]       		= lr_scheduler.get_lr()[0]
														lowercase_      :    List[str]       		= optimizer.param_groups[0]["""lr"""]
														lowercase_      :    List[str]       		= epoch
														lowercase_      :    List[str]       		= overall_step
														accelerator.print(F'''epoch {epoch}:'''   ,   UpperCAmelCase__				)

														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					with open(os.path.join(args.output_dir   ,   F'''state_{epoch}.json'''				)   ,   """w"""				) as f:
																												json.dump(UpperCAmelCase__   ,   UpperCAmelCase__				)




def 							lowerCamelCase			(   )    ->   str:
							lowercase_      :    Optional[int]       		= argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""				)
							parser.add_argument(
							    """--model_name_or_path"""   ,   type=UpperCAmelCase__   ,   default="""bert-base-cased"""   ,   help="""Path to pretrained model or model identifier from huggingface.co/models."""   ,   required=UpperCAmelCase__   ,   )
							parser.add_argument(
							    """--output_dir"""   ,   type=UpperCAmelCase__   ,   default="""."""   ,   help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""   ,   )
							parser.add_argument(
							    """--resume_from_checkpoint"""   ,   type=UpperCAmelCase__   ,   default=UpperCAmelCase__   ,   help="""If the training should continue from a checkpoint folder."""   ,   )
							parser.add_argument(
							    """--partial_train_epoch"""   ,   type=UpperCAmelCase__   ,   default=UpperCAmelCase__   ,   help="""If passed, the training will stop after this number of epochs."""   ,   )
							parser.add_argument(
							    """--num_epochs"""   ,   type=UpperCAmelCase__   ,   default=2   ,   help="""Number of train epochs."""   ,   )
							lowercase_      :    Dict       		= parser.parse_args()
							lowercase_      :    Tuple       		= {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

							training_function(UpperCAmelCase__   ,   UpperCAmelCase__				)


if __name__ == "__main__":
						main()






'''simple docstring'''




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase		:				List[Any]	       =		{"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				int	       =		[
						    "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "FocalNetForImageClassification",
						    "FocalNetForMaskedImageModeling",
						    "FocalNetBackbone",
						    "FocalNetModel",
						    "FocalNetPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_focalnet import (
												    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FocalNetBackbone,
												    FocalNetForImageClassification,
												    FocalNetForMaskedImageModeling,
												    FocalNetModel,
												    FocalNetPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Union[str, Any]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





