





"""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 						lowercase   (   __snake_case   ):
      _SCREAMING_SNAKE_CASE							      = "sequence-classification"





      def __init__(			self ,		lowercase					)						->	List[str]:
            if type(A__					) == dict:
                  lowerCAmelCase    		=			Namespace(**A__					)
            lowerCAmelCase    		=			glue_output_modes[hparams.task]
            lowerCAmelCase    		=			glue_tasks_num_labels[hparams.task]

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





      def        _snake_case     (			self ,		**lowercase					)						->	int:
            return self.model(**A__					)





      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Union[str, Any]:
            lowerCAmelCase    		=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

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

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





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			self.hparams
            lowerCAmelCase    		=			processors[args.task]()
            lowerCAmelCase    		=			processor.get_labels()

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





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = False					)						->	Optional[Any]:
            lowerCAmelCase    		=			'''dev''' if mode == '''test''' else mode

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

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





      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	str:
            lowerCAmelCase    		=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

            lowerCAmelCase    		=			self(**A__					)
            lowerCAmelCase    		=			outputs[:2]
            lowerCAmelCase    		=			logits.detach().cpu().numpy()
            lowerCAmelCase    		=			inputs['''labels'''].detach().cpu().numpy()

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





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			torch.stack([x["""val_loss"""] for x in outputs]					).mean().detach().cpu().item()
            lowerCAmelCase    		=			np.concatenate([x["""pred"""] for x in outputs] ,		axis=0					)

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

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

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

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





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            lowerCAmelCase    		=			self._eval_end(A__					)
            lowerCAmelCase    		=			ret['''log''']
            return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            lowerCAmelCase    		=			self._eval_end(A__					)
            lowerCAmelCase    		=			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     (			lowercase ,		lowercase					)						->	Dict:
            BaseTransformer.add_model_specific_args(A__ ,		A__					)
            parser.add_argument(
                """--max_seq_length""" ,		default=128 ,		type=A__ ,		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=A__ ,		required=A__ ,		help="""The GLUE task to run""" ,		)
            parser.add_argument(
                """--gpus""" ,		default=0 ,		type=A__ ,		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__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			argparse.ArgumentParser()
      add_generic_args(__lowercase				,		os.getcwd()     )
      lowerCAmelCase    		=			GLUETransformer.add_model_specific_args(__lowercase				,		os.getcwd()     )
      lowerCAmelCase    		=			parser.parse_args()

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

      lowerCAmelCase    		=			GLUETransformer(__lowercase     )
      lowerCAmelCase    		=			generic_train(__lowercase				,		__lowercase     )

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


if __name__ == "__main__":
       main()














from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCamelCase			:	Any								=logging.get_logger(__name__)


if is_vision_available():
	import PIL






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Union[str, Any]     =   ["pixel_values"]







		def __init__(					self       ,A__ = True       ,A__ = None       ,A__ = PILImageResampling.BICUBIC       ,A__ = True       ,A__ = None       ,A__ = True       ,A__ = 1 / 255       ,A__ = True       ,A__ = None       ,A__ = None       ,A__ = True       ,**A__       ,):
						super().__init__(**A__		)
						_A	:				List[str]   =      size if size is not None else {'''shortest_edge''': 224}
						_A	:				int   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				Optional[int]   =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
						_A	:				Dict   =      get_size_dict(A__       ,default_to_square=A__       ,param_name='''crop_size'''		)

						_A	:				List[str]   =      do_resize
						_A	:				List[str]   =      size
						_A	:				Tuple   =      resample
						_A	:				List[Any]   =      do_center_crop
						_A	:				Dict   =      crop_size
						_A	:				Any   =      do_rescale
						_A	:				Optional[Any]   =      rescale_factor
						_A	:				Dict   =      do_normalize
						_A	:				Optional[int]   =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						_A	:				Optional[int]   =      image_std if image_std is not None else OPENAI_CLIP_STD
						_A	:				Any   =      do_convert_rgb







		def  A__      (					self       ,A__       ,A__       ,A__ = PILImageResampling.BICUBIC       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      get_size_dict(A__       ,default_to_square=A__		)
						if "shortest_edge" not in size:
										raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
						_A	:				Any   =      get_resize_output_image_size(A__       ,size=size['''shortest_edge''']       ,default_to_square=A__		)
						return resize(A__       ,size=A__       ,resample=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						_A	:				Union[str, Any]   =      get_size_dict(A__		)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""		)
						return center_crop(A__       ,size=(size['''height'''], size['''width'''])       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return rescale(A__       ,scale=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return normalize(A__       ,mean=A__       ,std=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
						_A	:				str   =      do_resize if do_resize is not None else self.do_resize
						_A	:				List[str]   =      size if size is not None else self.size
						_A	:				List[str]   =      get_size_dict(A__       ,param_name='''size'''       ,default_to_square=A__		)
						_A	:				Optional[Any]   =      resample if resample is not None else self.resample
						_A	:				int   =      do_center_crop if do_center_crop is not None else self.do_center_crop
						_A	:				Any   =      crop_size if crop_size is not None else self.crop_size
						_A	:				int   =      get_size_dict(A__       ,param_name='''crop_size'''       ,default_to_square=A__		)
						_A	:				int   =      do_rescale if do_rescale is not None else self.do_rescale
						_A	:				List[str]   =      rescale_factor if rescale_factor is not None else self.rescale_factor
						_A	:				Tuple   =      do_normalize if do_normalize is not None else self.do_normalize
						_A	:				Union[str, Any]   =      image_mean if image_mean is not None else self.image_mean
						_A	:				int   =      image_std if image_std is not None else self.image_std
						_A	:				Optional[Any]   =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						_A	:				Optional[Any]   =      make_list_of_images(A__		)

						if not valid_images(A__		):
										raise ValueError(
										    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
										    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

						if do_resize and size is None:
										raise ValueError('''Size must be specified if do_resize is True.'''		)

						if do_center_crop and crop_size is None:
										raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

						if do_rescale and rescale_factor is None:
										raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

						if do_normalize and (image_mean is None or image_std is None):
										raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
										_A	:				int   =      [convert_to_rgb(A__		) for image in images]

						# All transformations expect numpy arrays.
						_A	:				int   =      [to_numpy_array(A__		) for image in images]

						if do_resize:
										_A	:				List[str]   =      [self.resize(image=A__       ,size=A__       ,resample=A__		) for image in images]

						if do_center_crop:
										_A	:				Union[str, Any]   =      [self.center_crop(image=A__       ,size=A__		) for image in images]

						if do_rescale:
										_A	:				Tuple   =      [self.rescale(image=A__       ,scale=A__		) for image in images]

						if do_normalize:
										_A	:				Dict   =      [self.normalize(image=A__       ,mean=A__       ,std=A__		) for image in images]

						_A	:				Dict   =      [to_channel_dimension_format(A__       ,A__		) for image in images]

						_A	:				Dict   =      {'''pixel_values''': images}
						return BatchFeature(data=A__       ,tensor_type=A__		)



