

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


_lowerCAmelCase  :      int			 =logging.getLogger(__name__)

class __UpperCamelCase     (				_lowerCAmelCase		):






					'''simple docstring'''



					__magic_name__        =   "sequence-classification"





					def __init__(				self				,      lowerCamelCase__						):
						if type(SCREAMING_SNAKE_CASE__						) == dict:
							UpperCAmelCase__:	Any									= Namespace(**SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	Any									= glue_output_modes[hparams.task]
						UpperCAmelCase__:	Tuple									= glue_tasks_num_labels[hparams.task]

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





					def    _UpperCAmelCase    (				self				,      **lowerCamelCase__						):
						return self.model(**SCREAMING_SNAKE_CASE__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	List[str]									= {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

						UpperCAmelCase__:	Dict									= self(**SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	Any									= outputs[0]

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	int									= self.hparams
						UpperCAmelCase__:	Union[str, Any]									= processors[args.task]()
						UpperCAmelCase__:	str									= processor.get_labels()

						for mode in ["train", "dev"]:
							UpperCAmelCase__:	Union[str, Any]									= self._feature_file(SCREAMING_SNAKE_CASE__						)
							if os.path.exists(SCREAMING_SNAKE_CASE__						) and not args.overwrite_cache:
								logger.info("Loading features from cached file %s"				,      SCREAMING_SNAKE_CASE__						)
							else:
								logger.info("Creating features from dataset file at %s"				,      args.data_dir						)
								UpperCAmelCase__:	int									= (
								    processor.get_dev_examples(args.data_dir						)
								    if mode == "dev"
								    else processor.get_train_examples(args.data_dir						)
								)
								UpperCAmelCase__:	Union[str, Any]									= convert_examples_to_features(
								    SCREAMING_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"				,      SCREAMING_SNAKE_CASE__						)
								torch.save(SCREAMING_SNAKE_CASE__				,      SCREAMING_SNAKE_CASE__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = False						):
						UpperCAmelCase__:	str									= "dev" if mode == "test" else mode

						UpperCAmelCase__:	Union[str, Any]									= self._feature_file(SCREAMING_SNAKE_CASE__						)
						logger.info("Loading features from cached file %s"				,      SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	Union[str, Any]									= torch.load(SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	List[str]									= torch.tensor([f.input_ids for f in features]				,      dtype=torch.long						)
						UpperCAmelCase__:	int									= torch.tensor([f.attention_mask for f in features]				,      dtype=torch.long						)
						UpperCAmelCase__:	Union[str, Any]									= torch.tensor([f.token_type_ids for f in features]				,      dtype=torch.long						)
						if self.hparams.glue_output_mode == "classification":
							UpperCAmelCase__:	int									= torch.tensor([f.label for f in features]				,      dtype=torch.long						)
						elif self.hparams.glue_output_mode == "regression":
							UpperCAmelCase__:	Dict									= torch.tensor([f.label for f in features]				,      dtype=torch.float						)

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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[Any]									= {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

						UpperCAmelCase__:	List[str]									= self(**SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__							,						UpperCAmelCase__:	Dict									= outputs[:2]
						UpperCAmelCase__:	Union[str, Any]									= logits.detach().cpu().numpy()
						UpperCAmelCase__:	Dict									= inputs["labels"].detach().cpu().numpy()

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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[Any]									= torch.stack([x["val_loss"] for x in outputs]						).mean().detach().cpu().item()
						UpperCAmelCase__:	Optional[Any]									= np.concatenate([x["pred"] for x in outputs]				,      axis=0						)

						if self.hparams.glue_output_mode == "classification":
							UpperCAmelCase__:	Optional[Any]									= np.argmax(SCREAMING_SNAKE_CASE__				,      axis=1						)
						elif self.hparams.glue_output_mode == "regression":
							UpperCAmelCase__:	Optional[Any]									= np.squeeze(SCREAMING_SNAKE_CASE__						)

						UpperCAmelCase__:	Tuple									= np.concatenate([x["target"] for x in outputs]				,      axis=0						)
						UpperCAmelCase__:	Union[str, Any]									= [[] for _ in range(out_label_ids.shape[0]						)]
						UpperCAmelCase__:	str									= [[] for _ in range(out_label_ids.shape[0]						)]

						UpperCAmelCase__:	Dict									= {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task				,      SCREAMING_SNAKE_CASE__				,      SCREAMING_SNAKE_CASE__						)}

						UpperCAmelCase__:	Any									= dict(results.items()						)
						UpperCAmelCase__:	str									= results
						return ret, preds_list, out_label_list





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	int									= self._eval_end(SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	Tuple									= ret["log"]
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	int									= self._eval_end(SCREAMING_SNAKE_CASE__						)
						UpperCAmelCase__:	Optional[int]									= 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    _UpperCAmelCase    (				lowerCamelCase__				,      lowerCamelCase__						):
						BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE__				,      SCREAMING_SNAKE_CASE__						)
						parser.add_argument(
						    "--max_seq_length"				,      default=1_2_8				,      type=SCREAMING_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=SCREAMING_SNAKE_CASE__				,      required=SCREAMING_SNAKE_CASE__				,      help="The GLUE task to run"				,      )
						parser.add_argument(
						    "--gpus"				,      default=0				,      type=SCREAMING_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 			_A	(      ):
	UpperCAmelCase__:	List[Any]									= argparse.ArgumentParser()
	add_generic_args(UpperCAmelCase_	,os.getcwd()      )
	UpperCAmelCase__:	Union[str, Any]									= GLUETransformer.add_model_specific_args(UpperCAmelCase_	,os.getcwd()      )
	UpperCAmelCase__:	int									= parser.parse_args()

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

	UpperCAmelCase__:	List[Any]									= GLUETransformer(UpperCAmelCase_      )
	UpperCAmelCase__:	int									= generic_train(UpperCAmelCase_	,UpperCAmelCase_      )

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


if __name__ == "__main__":
					main()




"""simple docstring"""







from typing import TYPE_CHECKING

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


UpperCamelCase     =							{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							["""PerceiverFeatureExtractor"""]
			UpperCamelCase     =							["""PerceiverImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PerceiverForImageClassificationConvProcessing""",
			    """PerceiverForImageClassificationFourier""",
			    """PerceiverForImageClassificationLearned""",
			    """PerceiverForMaskedLM""",
			    """PerceiverForMultimodalAutoencoding""",
			    """PerceiverForOpticalFlow""",
			    """PerceiverForSequenceClassification""",
			    """PerceiverLayer""",
			    """PerceiverModel""",
			    """PerceiverPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
			from .tokenization_perceiver import PerceiverTokenizer

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_perceiver import PerceiverFeatureExtractor
						from .image_processing_perceiver import PerceiverImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_perceiver import (
						    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PerceiverForImageClassificationConvProcessing,
						    PerceiverForImageClassificationFourier,
						    PerceiverForImageClassificationLearned,
						    PerceiverForMaskedLM,
						    PerceiverForMultimodalAutoencoding,
						    PerceiverForOpticalFlow,
						    PerceiverForSequenceClassification,
						    PerceiverLayer,
						    PerceiverModel,
						    PerceiverPreTrainedModel,
						)

else:
			import sys

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




