





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A							   =							logging.get_logger(__name__)

__A							   =							{
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     "cvt"

 def __init__(self							:  List[str]			,					UpperCAmelCase_							:  int=3			,					UpperCAmelCase_							:  List[Any]=[7, 3, 3]			,					UpperCAmelCase_							:  List[Any]=[4, 2, 2]			,					UpperCAmelCase_							:  Union[str, Any]=[2, 1, 1]			,					UpperCAmelCase_							:  Optional[int]=[64, 192, 384]			,					UpperCAmelCase_							:  List[Any]=[1, 3, 6]			,					UpperCAmelCase_							:  Union[str, Any]=[1, 2, 10]			,					UpperCAmelCase_							:  List[Any]=[4.0, 4.0, 4.0]			,					UpperCAmelCase_							:  int=[0.0, 0.0, 0.0]			,					UpperCAmelCase_							:  Dict=[0.0, 0.0, 0.0]			,					UpperCAmelCase_							:  List[Any]=[0.0, 0.0, 0.1]			,					UpperCAmelCase_							:  str=[True, True, True]			,					UpperCAmelCase_							:  Optional[Any]=[False, False, True]			,					UpperCAmelCase_							:  Optional[Any]=["dw_bn", "dw_bn", "dw_bn"]			,					UpperCAmelCase_							:  Dict=[3, 3, 3]			,					UpperCAmelCase_							:  Optional[int]=[1, 1, 1]			,					UpperCAmelCase_							:  Optional[int]=[2, 2, 2]			,					UpperCAmelCase_							:  Any=[1, 1, 1]			,					UpperCAmelCase_							:  List[Any]=[1, 1, 1]			,					UpperCAmelCase_							:  Tuple=0.02			,					UpperCAmelCase_							:  Union[str, Any]=1E-1_2			,					**UpperCAmelCase_							:  Optional[Any]			,					)			->str:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__: Optional[int]         =num_channels
   lowerCamelCase__: Any         =patch_sizes
   lowerCamelCase__: List[Any]         =patch_stride
   lowerCamelCase__: List[str]         =patch_padding
   lowerCamelCase__: str         =embed_dim
   lowerCamelCase__: int         =num_heads
   lowerCamelCase__: int         =depth
   lowerCamelCase__: Dict         =mlp_ratio
   lowerCamelCase__: Union[str, Any]         =attention_drop_rate
   lowerCamelCase__: List[str]         =drop_rate
   lowerCamelCase__: Optional[Any]         =drop_path_rate
   lowerCamelCase__: Union[str, Any]         =qkv_bias
   lowerCamelCase__: List[str]         =cls_token
   lowerCamelCase__: Dict         =qkv_projection_method
   lowerCamelCase__: Tuple         =kernel_qkv
   lowerCamelCase__: Any         =padding_kv
   lowerCamelCase__: List[Any]         =stride_kv
   lowerCamelCase__: str         =padding_q
   lowerCamelCase__: int         =stride_q
   lowerCamelCase__: str         =initializer_range
   lowerCamelCase__: List[str]         =layer_norm_eps






"""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


UpperCamelCase     =							logging.getLogger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					List[Any]							  =     "sequence-classification"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				if type(SCREAMING_SNAKE_CASE__		) == dict:
					A__											=					Namespace(**SCREAMING_SNAKE_CASE__		)
				A__											=					glue_output_modes[hparams.task]
				A__											=					glue_tasks_num_labels[hparams.task]

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


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return self.model(**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Union[str, Any]:
				A__											=					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

				A__											=					self(**SCREAMING_SNAKE_CASE__		)
				A__											=					outputs[0]

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


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					self.hparams
				A__											=					processors[args.task]()
				A__											=					processor.get_labels()

				for mode in ["train", "dev"]:
					A__											=					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		)
						A__											=					(
						    processor.get_dev_examples(args.data_dir		)
						    if mode == "dev"
						    else processor.get_train_examples(args.data_dir		)
						)
						A__											=					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   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = False		)      ->	DataLoader:
				A__											=					"dev" if mode == "test" else mode

				A__											=					self._feature_file(SCREAMING_SNAKE_CASE__		)
				logger.info("Loading features from cached file %s"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					torch.load(SCREAMING_SNAKE_CASE__		)
				A__											=					torch.tensor([f.input_ids for f in features]						,	dtype=torch.long		)
				A__											=					torch.tensor([f.attention_mask for f in features]						,	dtype=torch.long		)
				A__											=					torch.tensor([f.token_type_ids for f in features]						,	dtype=torch.long		)
				if self.hparams.glue_output_mode == "classification":
					A__											=					torch.tensor([f.label for f in features]						,	dtype=torch.long		)
				elif self.hparams.glue_output_mode == "regression":
					A__											=					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   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

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

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	tuple:
				A__											=					torch.stack([x["val_loss"] for x in outputs]		).mean().detach().cpu().item()
				A__											=					np.concatenate([x["pred"] for x in outputs]						,	axis=0		)

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

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

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

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	dict:
				A__							,							A__							,							A__											=					self._eval_end(SCREAMING_SNAKE_CASE__		)
				A__											=					ret["log"]
				return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	dict:
				A__							,							A__							,							A__											=					self._eval_end(SCREAMING_SNAKE_CASE__		)
				A__											=					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   snake_case__	(      SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Union[str, Any]:
				BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				parser.add_argument(
				    "--max_seq_length"						,	default=128						,	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       _lowerCamelCase				(							)	-> str:






	"""simple docstring"""





	A__											=					argparse.ArgumentParser()
	add_generic_args(UpperCAmelCase_,	os.getcwd()     )
	A__											=					GLUETransformer.add_model_specific_args(UpperCAmelCase_,	os.getcwd()     )
	A__											=					parser.parse_args()

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

	A__											=					GLUETransformer(UpperCAmelCase_     )
	A__											=					generic_train(UpperCAmelCase_,	UpperCAmelCase_     )

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


if __name__ == "__main__":
			main()




