

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


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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = 16				,	_UpperCAmelCase = "bert-base-cased"    ):
						SCREAMING_SNAKE_CASE_: Any  		=       AutoTokenizer.from_pretrained(A__    )
						SCREAMING_SNAKE_CASE_: List[str]  		=       load_dataset("glue"				,	"mrpc"    )

						def tokenize_function(_UpperCAmelCase    ):
												# max_length=None => use the model max length (it's actually the default)
												SCREAMING_SNAKE_CASE_: List[Any]  		=       tokenizer(examples["sentence1"]				,	examples["sentence2"]				,	truncation=A__				,	max_length=A__    )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       datasets.map(
						    A__				,	batched=A__				,	remove_columns=["idx", "sentence1", "sentence2"]				,	load_from_cache_file=A__    )

						# 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_: int  		=       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.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(A__				,	padding="max_length"				,	max_length=1_28				,	return_tensors="pt"    )
												return tokenizer.pad(A__				,	padding="longest"				,	return_tensors="pt"    )

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_: Any  		=       DataLoader(
						    tokenized_datasets["train"]				,	shuffle=A__				,	collate_fn=A__				,	batch_size=A__    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       DataLoader(
						    tokenized_datasets["validation"]				,	shuffle=A__				,	collate_fn=A__				,	batch_size=A__    )

						return train_dataloader, eval_dataloader







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						model.eval()
						SCREAMING_SNAKE_CASE_: Any  		=       0
						for step, batch in enumerate(A__    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE_: Dict  		=       model(**A__    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       outputs.logits.argmax(dim=-1    )
												# It is slightly faster to call this once, than multiple times
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, 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(A__    ) - 1:
																								SCREAMING_SNAKE_CASE_: Dict  		=       predictions[: len(eval_dataloader.dataset    ) - samples_seen]
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       references[: len(eval_dataloader.dataset    ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=A__				,	references=A__				,	)

						SCREAMING_SNAKE_CASE_: Any  		=       metric.compute()
						return eval_metric["accuracy"]







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       Accelerator()

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

						set_seed(A__    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       get_dataloaders(A__				,	A__				,	A__    )

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

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

						if accelerator.state.deepspeed_plugin is not None:
												SCREAMING_SNAKE_CASE_: int  		=       accelerator.state.deepspeed_plugin.deepspeed_config[
												    "gradient_accumulation_steps"
												]
						else:
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       1
						SCREAMING_SNAKE_CASE_: int  		=       (len(A__    ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       get_linear_schedule_with_warmup(
												    optimizer=A__				,	num_warmup_steps=0				,	num_training_steps=A__				,	)
						else:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       DummyScheduler(A__				,	total_num_steps=A__				,	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.
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       accelerator.prepare(
						    A__				,	A__				,	A__				,	A__				,	A__    )

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

						if args.partial_train_epoch is not None:
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint    )
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       args.resume_from_checkpoint.split("epoch_"    )[1]
												SCREAMING_SNAKE_CASE_: Any  		=       ""
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												SCREAMING_SNAKE_CASE_: List[str]  		=       int(A__    ) + 1
												SCREAMING_SNAKE_CASE_: Tuple  		=       evaluation_loop(A__				,	A__				,	A__				,	A__    )
												accelerator.print("resumed checkpoint performance:"				,	A__    )
												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:
																		SCREAMING_SNAKE_CASE_: List[str]  		=       json.load(A__    )
																		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
						SCREAMING_SNAKE_CASE_: Any  		=       {}
						for epoch in range(A__				,	A__    ):
												model.train()
												for step, batch in enumerate(A__    ):
																		SCREAMING_SNAKE_CASE_: List[str]  		=       model(**A__    )
																		SCREAMING_SNAKE_CASE_: Dict  		=       outputs.loss
																		SCREAMING_SNAKE_CASE_: List[str]  		=       loss / gradient_accumulation_steps
																		accelerator.backward(A__    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												SCREAMING_SNAKE_CASE_: Dict  		=       f"epoch_{epoch}"
												SCREAMING_SNAKE_CASE_: List[str]  		=       os.path.join(args.output_dir				,	A__    )
												accelerator.save_state(A__    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       evaluation_loop(A__				,	A__				,	A__				,	A__    )
												SCREAMING_SNAKE_CASE_: List[Any]  		=       accuracy
												SCREAMING_SNAKE_CASE_: str  		=       lr_scheduler.get_lr()[0]
												SCREAMING_SNAKE_CASE_: Tuple  		=       optimizer.param_groups[0]["lr"]
												SCREAMING_SNAKE_CASE_: List[str]  		=       epoch
												SCREAMING_SNAKE_CASE_: int  		=       overall_step
												accelerator.print(f"epoch {epoch}:"				,	A__    )

												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(A__				,	A__    )







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: str  		=       argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."    )
						parser.add_argument(
						    "--model_name_or_path"				,	type=A__				,	default="bert-base-cased"				,	help="Path to pretrained model or model identifier from huggingface.co/models."				,	required=A__				,	)
						parser.add_argument(
						    "--output_dir"				,	type=A__				,	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=A__				,	default=A__				,	help="If the training should continue from a checkpoint folder."				,	)
						parser.add_argument(
						    "--partial_train_epoch"				,	type=A__				,	default=A__				,	help="If passed, the training will stop after this number of epochs."				,	)
						parser.add_argument(
						    "--num_epochs"				,	type=A__				,	default=2				,	help="Number of train epochs."				,	)
						SCREAMING_SNAKE_CASE_: Dict  		=       parser.parse_args()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

						training_function(A__				,	A__    )


if __name__ == "__main__":
						main()












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_									=	{
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['VisionEncoderDecoderModel']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['TFVisionEncoderDecoderModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
       from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
       import sys

       UpperCAmelCase_									=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

