






"""simple docstring"""
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_							      = 16
lowerCamelCase_							      = 32

def 							snake_case (  A__      ,A__ = 16      ,A__ = "bert-base-cased"		):
			UpperCAmelCase_	:					str     	=   AutoTokenizer.from_pretrained(A__		)
			UpperCAmelCase_	:					Dict     	=   load_dataset("glue"      ,"mrpc"		)

			def tokenize_function(A__		):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_	:					str     	=   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
			UpperCAmelCase_	:					Optional[int]     	=   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
			UpperCAmelCase_	:					List[Any]     	=   tokenized_datasets.rename_column("label"      ,"labels"		)

			def collate_fn(A__		):
						# 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.
			UpperCAmelCase_	:					int     	=   DataLoader(
			    tokenized_datasets["train"]      ,shuffle=A__      ,collate_fn=A__      ,batch_size=A__		)
			UpperCAmelCase_	:					List[str]     	=   DataLoader(
			    tokenized_datasets["validation"]      ,shuffle=A__      ,collate_fn=A__      ,batch_size=A__		)

			return train_dataloader, eval_dataloader

def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			model.eval()
			UpperCAmelCase_	:					Optional[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():
									UpperCAmelCase_	:					Optional[Any]     	=   model(**A__		)
						UpperCAmelCase_	:					Optional[Any]     	=   outputs.logits.argmax(dim=-1		)
						# It is slightly faster to call this once, than multiple times
						UpperCAmelCase_	,      UpperCAmelCase_	:					int     	=   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:
												UpperCAmelCase_	:					List[Any]     	=   predictions[: len(eval_dataloader.dataset		) - samples_seen]
												UpperCAmelCase_	:					Union[str, Any]     	=   references[: len(eval_dataloader.dataset		) - samples_seen]
									else:
												samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=A__      ,references=A__      ,)

			UpperCAmelCase_	:					Union[str, Any]     	=   metric.compute()
			return eval_metric["accuracy"]

def 							snake_case (  A__      ,A__		):
			# Initialize accelerator
			UpperCAmelCase_	:					List[Any]     	=   Accelerator()

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

			set_seed(A__		)
			UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   get_dataloaders(A__      ,A__      ,A__		)

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

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

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_	:					str     	=   accelerator.state.deepspeed_plugin.deepspeed_config[
						    "gradient_accumulation_steps"
						]
			else:
						UpperCAmelCase_	:					Tuple     	=   1
			UpperCAmelCase_	:					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
			):
						UpperCAmelCase_	:					List[Any]     	=   get_linear_schedule_with_warmup(
						    optimizer=A__      ,num_warmup_steps=0      ,num_training_steps=A__      ,)
			else:
						UpperCAmelCase_	:					List[str]     	=   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.
			UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   accelerator.prepare(
			    A__      ,A__      ,A__      ,A__      ,A__		)

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

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

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

									overall_step += 1
						UpperCAmelCase_	:					Any     	=   F"""epoch_{epoch}"""
						UpperCAmelCase_	:					Optional[Any]     	=   os.path.join(args.output_dir      ,A__		)
						accelerator.save_state(A__		)
						UpperCAmelCase_	:					Optional[int]     	=   evaluation_loop(A__      ,A__      ,A__      ,A__		)
						UpperCAmelCase_	:					List[Any]     	=   accuracy
						UpperCAmelCase_	:					str     	=   lr_scheduler.get_lr()[0]
						UpperCAmelCase_	:					Dict     	=   optimizer.param_groups[0]["lr"]
						UpperCAmelCase_	:					List[str]     	=   epoch
						UpperCAmelCase_	:					str     	=   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 							snake_case (  ):
			UpperCAmelCase_	:					Optional[Any]     	=   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."      ,)
			UpperCAmelCase_	:					List[Any]     	=   parser.parse_args()
			UpperCAmelCase_	:					List[str]     	=   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

			training_function(A__      ,A__		)


if __name__ == "__main__":
			main()











from sklearn.metrics import fa_score

import datasets


lowerCamelCase      :       Union[str, Any]    =						'''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


lowerCamelCase      :       Optional[Any]    =						'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


lowerCamelCase      :       Optional[Any]    =						'''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,		_KWARGS_DESCRIPTION      )
class 				_UpperCamelCase      (datasets.Metric      ):


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           return datasets.MetricInfo(
               description=_DESCRIPTION       ,    citation=_CITATION       ,    inputs_description=_KWARGS_DESCRIPTION       ,    features=datasets.Features(
                   {
                       "predictions": datasets.Sequence(datasets.Value("int32"						)						),
                       "references": datasets.Sequence(datasets.Value("int32"						)						),
                   }
                   if self.config_name == "multilabel"
                   else {
                       "predictions": datasets.Value("int32"						),
                       "references": datasets.Value("int32"						),
                   }						)       ,    reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"]       ,    )


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase=None       ,    __UpperCamelCase=1       ,    __UpperCamelCase="binary"       ,    __UpperCamelCase=None						)->						Optional[int]:
           __lowerCAmelCase             =       fa_score(
               __UpperCamelCase       ,    __UpperCamelCase       ,    labels=__UpperCamelCase       ,    pos_label=__UpperCamelCase       ,    average=__UpperCamelCase       ,    sample_weight=__UpperCamelCase						)
           return {"f1": float(__UpperCamelCase						) if score.size == 1 else score}






