
"""simple docstring"""







import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


_SCREAMING_SNAKE_CASE	       =      logging.getLogger(__name__)







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   'sequence-classification'





					def __init__(				self   :							List[Any]							,						snake_case_   :							str    ):
												if type(snake_case_    ) == dict:
																			__snake_case     			=       Namespace(**snake_case_    )
												__snake_case     			=       glue_output_modes[hparams.task]
												__snake_case     			=       glue_tasks_num_labels[hparams.task]

												super().__init__(snake_case_							,						snake_case_							,						self.mode    )





					def 							lowerCAmelCase      (				self   :							List[Any]							,						**snake_case_   :							Optional[Any]    ):
												return self.model(**snake_case_    )





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							List[Any]    ):
												__snake_case     			=       {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

												if self.config.model_type not in ["distilbert", "bart"]:
																			__snake_case     			=       batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

												__snake_case     			=       self(**snake_case_    )
												__snake_case     			=       outputs[0]

												__snake_case     			=       self.trainer.lr_schedulers[0]["scheduler"]
												__snake_case     			=       {"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
												return {"loss": loss, "log": tensorboard_logs}





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       self.hparams
												__snake_case     			=       processors[args.task]()
												__snake_case     			=       processor.get_labels()

												for mode in ["train", "dev"]:
																			__snake_case     			=       self._feature_file(snake_case_    )
																			if os.path.exists(snake_case_    ) and not args.overwrite_cache:
																										logger.info("Loading features from cached file %s"							,						snake_case_    )
																			else:
																										logger.info("Creating features from dataset file at %s"							,						args.data_dir    )
																										__snake_case     			=       (
																										    processor.get_dev_examples(args.data_dir    )
																										    if mode == "dev"
																										    else processor.get_train_examples(args.data_dir    )
																										)
																										__snake_case     			=       convert_examples_to_features(
																										    snake_case_							,						self.tokenizer							,						max_length=args.max_seq_length							,						label_list=self.labels							,						output_mode=args.glue_output_mode							,						)
																										logger.info("Saving features into cached file %s"							,						snake_case_    )
																										torch.save(snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							str							,						snake_case_   :							int							,						snake_case_   :							bool = False    ):
												__snake_case     			=       "dev" if mode == "test" else mode

												__snake_case     			=       self._feature_file(snake_case_    )
												logger.info("Loading features from cached file %s"							,						snake_case_    )
												__snake_case     			=       torch.load(snake_case_    )
												__snake_case     			=       torch.tensor([f.input_ids for f in features]							,						dtype=torch.long    )
												__snake_case     			=       torch.tensor([f.attention_mask for f in features]							,						dtype=torch.long    )
												__snake_case     			=       torch.tensor([f.token_type_ids for f in features]							,						dtype=torch.long    )
												if self.hparams.glue_output_mode == "classification":
																			__snake_case     			=       torch.tensor([f.label for f in features]							,						dtype=torch.long    )
												elif self.hparams.glue_output_mode == "regression":
																			__snake_case     			=       torch.tensor([f.label for f in features]							,						dtype=torch.float    )

												return DataLoader(
												    TensorDataset(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )							,						batch_size=snake_case_							,						shuffle=snake_case_							,						)





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							str    ):
												__snake_case     			=       {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

												if self.config.model_type not in ["distilbert", "bart"]:
																			__snake_case     			=       batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

												__snake_case     			=       self(**snake_case_    )
												__snake_case			,   __snake_case     			=       outputs[:2]
												__snake_case     			=       logits.detach().cpu().numpy()
												__snake_case     			=       inputs["labels"].detach().cpu().numpy()

												return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							int    ):
												__snake_case     			=       torch.stack([x["val_loss"] for x in outputs]    ).mean().detach().cpu().item()
												__snake_case     			=       np.concatenate([x["pred"] for x in outputs]							,						axis=0    )

												if self.hparams.glue_output_mode == "classification":
																			__snake_case     			=       np.argmax(snake_case_							,						axis=1    )
												elif self.hparams.glue_output_mode == "regression":
																			__snake_case     			=       np.squeeze(snake_case_    )

												__snake_case     			=       np.concatenate([x["target"] for x in outputs]							,						axis=0    )
												__snake_case     			=       [[] for _ in range(out_label_ids.shape[0]    )]
												__snake_case     			=       [[] for _ in range(out_label_ids.shape[0]    )]

												__snake_case     			=       {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task							,						snake_case_							,						snake_case_    )}

												__snake_case     			=       dict(results.items()    )
												__snake_case     			=       results
												return ret, preds_list, out_label_list





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							list    ):
												__snake_case			,   __snake_case			,   __snake_case     			=       self._eval_end(snake_case_    )
												__snake_case     			=       ret["log"]
												return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Dict    ):
												__snake_case			,   __snake_case			,   __snake_case     			=       self._eval_end(snake_case_    )
												__snake_case     			=       ret["log"]
												# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
												return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





					@staticmethod
					def 							lowerCAmelCase      (				snake_case_   :							List[Any]							,						snake_case_   :							List[str]    ):
												BaseTransformer.add_model_specific_args(snake_case_							,						snake_case_    )
												parser.add_argument(
												    "--max_seq_length"							,						default=128							,						type=snake_case_							,						help=(
												        "The maximum total input sequence length after tokenization. Sequences longer "
												        "than this will be truncated, sequences shorter will be padded."
												    )							,						)

												parser.add_argument(
												    "--task"							,						default=""							,						type=snake_case_							,						required=snake_case_							,						help="The GLUE task to run"							,						)
												parser.add_argument(
												    "--gpus"							,						default=0							,						type=snake_case_							,						help="The number of GPUs allocated for this, it is by default 0 meaning none"							,						)

												parser.add_argument(
												    "--overwrite_cache"							,						action="store_true"							,						help="Overwrite the cached training and evaluation sets"    )

												return parser




def   __UpperCamelCase   (					)  ->     Union[str, Any]:







							"""simple docstring"""


							__snake_case     			=       argparse.ArgumentParser()
							add_generic_args(SCREAMING_SNAKE_CASE							,    os.getcwd() )
							__snake_case     			=       GLUETransformer.add_model_specific_args(SCREAMING_SNAKE_CASE							,    os.getcwd() )
							__snake_case     			=       parser.parse_args()

							# If output_dir not provided, a folder will be generated in pwd
							if args.output_dir is None:
														__snake_case     			=       os.path.join(
														    "./results"							,    F'''{args.task}_{time.strftime("%Y%m%d_%H%M%S" )}'''							,    )
														os.makedirs(args.output_dir )

							__snake_case     			=       GLUETransformer(SCREAMING_SNAKE_CASE )
							__snake_case     			=       generic_train(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							# Optionally, predict on dev set and write to output_dir
							if args.do_predict:
														__snake_case     			=       sorted(glob.glob(os.path.join(args.output_dir							,    "checkpoint-epoch=*.ckpt" )							,    recursive=SCREAMING_SNAKE_CASE ) )
														__snake_case     			=       model.load_from_checkpoint(checkpoints[-1] )
														return trainer.test(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
					main()


"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTFeatureExtractor"""]
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MobileViTForImageClassification""",
		    """MobileViTForSemanticSegmentation""",
		    """MobileViTModel""",
		    """MobileViTPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFMobileViTForImageClassification""",
		    """TFMobileViTForSemanticSegmentation""",
		    """TFMobileViTModel""",
		    """TFMobileViTPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
