'''simple docstring'''

from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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


lowercase_     		= logging.get_logger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      List[str]       =							['pixel_values']






		def __init__(   self		,						__A = True		,						__A = None		,						__A = PILImageResampling.BICUBIC		,						__A = True		,						__A = None		,						__A = True		,						__A = 1 / 255		,						__A = True		,						__A = IMAGENET_DEFAULT_MEAN		,						__A = IMAGENET_DEFAULT_STD		,						**__A		,						) ->     None:
								super().__init__(**__A				)
								_lowerCAmelCase										=size if size is not None else {'shortest_edge': 224}
								_lowerCAmelCase										=get_size_dict(__A		,						default_to_square=__A				)
								_lowerCAmelCase										=crop_size if crop_size is not None else {'height': 224, 'width': 224}
								_lowerCAmelCase										=get_size_dict(__A		,						param_name='crop_size'				)

								_lowerCAmelCase										=do_resize
								_lowerCAmelCase										=size
								_lowerCAmelCase										=resample
								_lowerCAmelCase										=do_center_crop
								_lowerCAmelCase										=crop_size
								_lowerCAmelCase										=do_rescale
								_lowerCAmelCase										=rescale_factor
								_lowerCAmelCase										=do_normalize
								_lowerCAmelCase										=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								_lowerCAmelCase										=image_std if image_std is not None else IMAGENET_DEFAULT_STD






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = PILImageResampling.BICUBIC		,						__A = None		,						**__A		,						) ->     np.ndarray:
								_lowerCAmelCase										=get_size_dict(__A		,						default_to_square=__A				)
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
														_lowerCAmelCase										=int((256 / 224) * size['shortest_edge']				)
														_lowerCAmelCase										=get_resize_output_image_size(__A		,						size=__A		,						default_to_square=__A				)
														_lowerCAmelCase										={'height': output_size[0], 'width': output_size[1]}
								if "height" not in size_dict or "width" not in size_dict:
														raise ValueError(
														    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''				)
								return resize(
								    __A		,						size=(size_dict['height'], size_dict['width'])		,						resample=__A		,						data_format=__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = None		,						**__A		,						) ->     np.ndarray:
								_lowerCAmelCase										=get_size_dict(__A				)
								if "height" not in size or "width" not in size:
														raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''				)
								return center_crop(__A		,						size=(size['height'], size['width'])		,						data_format=__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = None		,						**__A		,						) ->     np.ndarray:
								return rescale(__A		,						scale=__A		,						data_format=__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A = None		,						**__A		,						) ->     np.ndarray:
								return normalize(__A		,						mean=__A		,						std=__A		,						data_format=__A		,						**__A				)






		def 		UpperCamelCase__       (   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 = ChannelDimension.FIRST		,						**__A		,						) ->     BatchFeature:
								_lowerCAmelCase										=do_resize if do_resize is not None else self.do_resize
								_lowerCAmelCase										=resample if resample is not None else self.resample
								_lowerCAmelCase										=do_center_crop if do_center_crop is not None else self.do_center_crop
								_lowerCAmelCase										=do_rescale if do_rescale is not None else self.do_rescale
								_lowerCAmelCase										=rescale_factor if rescale_factor is not None else self.rescale_factor
								_lowerCAmelCase										=do_normalize if do_normalize is not None else self.do_normalize
								_lowerCAmelCase										=image_mean if image_mean is not None else self.image_mean
								_lowerCAmelCase										=image_std if image_std is not None else self.image_std

								_lowerCAmelCase										=size if size is not None else self.size
								_lowerCAmelCase										=get_size_dict(__A		,						default_to_square=__A				)
								_lowerCAmelCase										=crop_size if crop_size is not None else self.crop_size
								_lowerCAmelCase										=get_size_dict(__A		,						param_name='crop_size'				)

								_lowerCAmelCase										=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.'				)

								# All transformations expect numpy arrays.
								_lowerCAmelCase										=[to_numpy_array(__A				) for image in images]

								if do_resize:
														_lowerCAmelCase										=[self.resize(__A		,						__A		,						__A				) for image in images]

								if do_center_crop:
														_lowerCAmelCase										=[self.center_crop(__A		,						__A				) for image in images]

								if do_rescale:
														_lowerCAmelCase										=[self.rescale(__A		,						__A				) for image in images]

								if do_normalize:
														_lowerCAmelCase										=[self.normalize(__A		,						__A		,						__A				) for image in images]

								_lowerCAmelCase										=[to_channel_dimension_format(__A		,						__A				) for image in images]

								_lowerCAmelCase										={'pixel_values': images}
								return BatchFeature(data=__A		,						tensor_type=__A				)






'''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


lowercase_     		= logging.getLogger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							'sequence-classification'






		def __init__(   self		,						__A				) ->     List[Any]:
								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 		UpperCamelCase__       (   self		,						**__A				) ->     Any:
								return self.model(**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     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 		UpperCamelCase__       (   self				) ->     Any:
								_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 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = False				) ->     DataLoader:
								_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 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     List[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						,				_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 		UpperCamelCase__       (   self		,						__A				) ->     tuple:
								_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 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=ret['log']
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_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 		UpperCamelCase__       (   __A		,						__A				) ->     Any:
								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(a__			,      os.getcwd()      )
						_lowerCAmelCase										=GLUETransformer.add_model_specific_args(a__			,      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(a__      )
						_lowerCAmelCase										=generic_train(a__			,      a__      )

						# 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=a__      )      )
												_lowerCAmelCase										=model.load_from_checkpoint(checkpoints[-1]      )
												return trainer.test(a__      )


if __name__ == "__main__":
							main()





