




"""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      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''sequence-classification'''




    def __init__(   self						:	str     ,			snake_case_						:	Any				)				->							Tuple:




     '''simple docstring'''

     if type(snake_case_				) == dict:
      A__	=    Namespace(**snake_case_				)
     A__	=    glue_output_modes[hparams.task]
     A__	=    glue_tasks_num_labels[hparams.task]

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




    def 							__magic_name__							(   self						:	List[str]     ,			**snake_case_						:	List[Any]				)				->							Any:




     '''simple docstring'''

     return self.model(**snake_case_				)




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[Any]				)				->							Optional[int]:




     '''simple docstring'''

     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(**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 							__magic_name__							(   self						:	Any				)				->							Dict:




     '''simple docstring'''

     A__	=    self.hparams
     A__	=    processors[args.task]()
     A__	=    processor.get_labels()

     for mode in ["train", "dev"]:
      A__	=    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				)
       A__	=    (
           processor.get_dev_examples(args.data_dir				)
           if mode == "dev"
           else processor.get_train_examples(args.data_dir				)
       )
       A__	=    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 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	str     ,			snake_case_						:	int     ,			snake_case_						:	bool = False				)				->							DataLoader:




     '''simple docstring'''

     A__	=    "dev" if mode == "test" else mode

     A__	=    self._feature_file(snake_case_				)
     logger.info("Loading features from cached file %s"     ,			snake_case_				)
     A__	=    torch.load(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(snake_case_     ,			snake_case_     ,			snake_case_     ,			snake_case_				)     ,			batch_size=snake_case_     ,			shuffle=snake_case_     ,			)




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	int     ,			snake_case_						:	int				)				->							Optional[int]:




     '''simple docstring'''

     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(**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 							__magic_name__							(   self						:	Union[str, Any]     ,			snake_case_						:	List[Any]				)				->							tuple:




     '''simple docstring'''

     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(snake_case_     ,			axis=1				)
     elif self.hparams.glue_output_mode == "regression":
      A__	=    np.squeeze(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     ,			snake_case_     ,			snake_case_				)}

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




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	list				)				->							dict:




     '''simple docstring'''

     A__,				A__,				A__	=    self._eval_end(snake_case_				)
     A__	=    ret["log"]
     return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	str				)				->							dict:




     '''simple docstring'''

     A__,				A__,				A__	=    self._eval_end(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 							__magic_name__							(   snake_case_						:	Union[str, Any]     ,			snake_case_						:	Dict				)				->							Optional[Any]:




     '''simple docstring'''

     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   _SCREAMING_SNAKE_CASE       (							)						->		Optional[Any]:
 A__	=    argparse.ArgumentParser()
 add_generic_args(lowercase_      ,			os.getcwd()						)
 A__	=    GLUETransformer.add_model_specific_args(lowercase_      ,			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(lowercase_						)
 A__	=    generic_train(lowercase_      ,			lowercase_						)

 # 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=lowercase_						)						)
  A__	=    model.load_from_checkpoint(checkpoints[-1]						)
  return trainer.test(lowercase_						)


if __name__ == "__main__":
      main()











"""simple docstring"""



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''dpt'''




    def __init__(   self						:	List[Any]     ,			snake_case_						:	Union[str, Any]=768     ,			snake_case_						:	Tuple=12     ,			snake_case_						:	Tuple=12     ,			snake_case_						:	List[Any]=3_072     ,			snake_case_						:	Dict="gelu"     ,			snake_case_						:	Tuple=0.0     ,			snake_case_						:	int=0.0     ,			snake_case_						:	Optional[int]=0.02     ,			snake_case_						:	Union[str, Any]=1e-12     ,			snake_case_						:	Tuple=384     ,			snake_case_						:	Tuple=16     ,			snake_case_						:	Optional[Any]=3     ,			snake_case_						:	Dict=False     ,			snake_case_						:	Any=True     ,			snake_case_						:	Any=[2, 5, 8, 11]     ,			snake_case_						:	Union[str, Any]="project"     ,			snake_case_						:	Union[str, Any]=[4, 2, 1, 0.5]     ,			snake_case_						:	List[str]=[96, 192, 384, 768]     ,			snake_case_						:	int=256     ,			snake_case_						:	Tuple=-1     ,			snake_case_						:	List[str]=False     ,			snake_case_						:	int=True     ,			snake_case_						:	List[Any]=0.4     ,			snake_case_						:	Optional[Any]=255     ,			snake_case_						:	List[str]=0.1     ,			snake_case_						:	List[str]=[1, 1_024, 24, 24]     ,			snake_case_						:	Union[str, Any]=[0, 1]     ,			snake_case_						:	Any=None     ,			**snake_case_						:	Optional[Any]     ,			)				->							Optional[int]:




     '''simple docstring'''

     super().__init__(**snake_case_				)

     A__	=    hidden_size
     A__	=    is_hybrid

     if self.is_hybrid:
      if backbone_config is None:
       logger.info("Initializing the config with a `BiT` backbone."				)
       A__	=    {
           "global_padding": "same",
           "layer_type": "bottleneck",
           "depths": [3, 4, 9],
           "out_features": ["stage1", "stage2", "stage3"],
           "embedding_dynamic_padding": True,
       }
       A__	=    BitConfig(**snake_case_				)
      elif isinstance(snake_case_     ,			snake_case_				):
       logger.info("Initializing the config with a `BiT` backbone."				)
       A__	=    BitConfig(**snake_case_				)
      elif isinstance(snake_case_     ,			snake_case_				):
       A__	=    backbone_config
      else:
       raise ValueError(
           F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""				)

      A__	=    backbone_featmap_shape
      A__	=    neck_ignore_stages

      if readout_type != "project":
       raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."				)
     else:
      A__	=    None
      A__	=    None
      A__	=    []

     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    intermediate_size
     A__	=    hidden_act
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    initializer_range
     A__	=    layer_norm_eps
     A__	=    image_size
     A__	=    patch_size
     A__	=    num_channels
     A__	=    qkv_bias
     A__	=    backbone_out_indices
     if readout_type not in ["ignore", "add", "project"]:
      raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"				)
     A__	=    readout_type
     A__	=    reassemble_factors
     A__	=    neck_hidden_sizes
     A__	=    fusion_hidden_size
     A__	=    head_in_index
     A__	=    use_batch_norm_in_fusion_residual
     # auxiliary head attributes (semantic segmentation)
     A__	=    use_auxiliary_head
     A__	=    auxiliary_loss_weight
     A__	=    semantic_loss_ignore_index
     A__	=    semantic_classifier_dropout




    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    copy.deepcopy(self.__dict__				)

     if output["backbone_config"] is not None:
      A__	=    self.backbone_config.to_dict()

     A__	=    self.__class__.model_type
     return output





