



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


a_      :      Dict    =     logging.getLogger(__name__)
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    List[str]      						=					'''sequence-classification'''

 def __init__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, 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 						_UpperCAmelCase       (	self		,				**SCREAMING_SNAKE_CASE      ) ->			List[str]:
  return self.model(**SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	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 						_UpperCAmelCase       (	self      ) ->			Any:
  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 						_UpperCAmelCase       (	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 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Tuple:
  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 						_UpperCAmelCase       (	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 						_UpperCAmelCase       (	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 						_UpperCAmelCase       (	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 						_UpperCAmelCase       (	SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  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					(		):
 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()










from __future__ import annotations

from typing import Any
class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 0      ) ->			None:
  a__							,			a__												= row, column
  a__												= [[default_value for c in range(SCREAMING_SNAKE_CASE      )] for r in range(SCREAMING_SNAKE_CASE      )]

 def __str__(	self      ) ->			str:
  a__												= f"Matrix consist of {self.row} rows and {self.column} columns\n"

  # Make string identifier
  a__												= 0
  for row_vector in self.array:
   for obj in row_vector:
    a__												= max(SCREAMING_SNAKE_CASE		,				len(str(SCREAMING_SNAKE_CASE      )      )      )
  a__												= f"%{max_element_length}s"

  # Make string and return
  def single_line(SCREAMING_SNAKE_CASE      ) -> str:
   nonlocal string_format_identifier
   a__												= '''['''
   line += ", ".join(string_format_identifier % (obj,) for obj in row_vector      )
   line += "]"
   return line

  s += "\n".join(single_line(SCREAMING_SNAKE_CASE      ) for row_vector in self.array      )
  return s

 def __repr__(	self      ) ->			str:
  return str(self      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			bool:
  if not (isinstance(SCREAMING_SNAKE_CASE		,				(list, tuple)      ) and len(SCREAMING_SNAKE_CASE      ) == 2):
   return False
  elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
   return False
  else:
   return True

 def __getitem__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  assert self.validate_indicies(SCREAMING_SNAKE_CASE      )
  return self.array[loc[0]][loc[1]]

 def __setitem__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			None:
  assert self.validate_indicies(SCREAMING_SNAKE_CASE      )
  a__												= value

 def __add__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  assert isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  assert self.row == another.row and self.column == another.column

  # Add
  a__												= Matrix(self.row		,				self.column      )
  for r in range(self.row      ):
   for c in range(self.column      ):
    a__												= self[r, c] + another[r, c]
  return result

 def __neg__(	self      ) ->			Matrix:
  a__												= Matrix(self.row		,				self.column      )
  for r in range(self.row      ):
   for c in range(self.column      ):
    a__												= -self[r, c]
  return result

 def __sub__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  return self + (-another)

 def __mul__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:

  if isinstance(SCREAMING_SNAKE_CASE		,				(int, float)      ):  # Scalar multiplication
   a__												= Matrix(self.row		,				self.column      )
   for r in range(self.row      ):
    for c in range(self.column      ):
     a__												= self[r, c] * another
   return result
  elif isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):  # Matrix multiplication
   assert self.column == another.row
   a__												= Matrix(self.row		,				another.column      )
   for r in range(self.row      ):
    for c in range(another.column      ):
     for i in range(self.column      ):
      result[r, c] += self[r, i] * another[i, c]
   return result
  else:
   a__												= f"Unsupported type given for another ({type(SCREAMING_SNAKE_CASE      )})"
   raise TypeError(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Matrix:
  a__												= Matrix(self.column		,				self.row      )
  for r in range(self.row      ):
   for c in range(self.column      ):
    a__												= self[r, c]
  return result



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  assert isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) and isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  assert self.row == self.column == u.row == v.row  # u, v should be column vector
  assert u.column == v.column == 1  # u, v should be column vector

  # Calculate
  a__												= v.transpose()
  a__												= (v_t * self * u)[0, 0] + 1
  if numerator_factor == 0:
   return None  # It's not invertable
  return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":






     def     __a					(		):
      # a^(-1)
      a__												= Matrix(3							,  3							,  0       )
      for i in range(3       ):
       a__												= 1
      print(f"a^(-1) is {ainv}"       )
      # u, v
      a__												= Matrix(3							,  1							,  0       )
      a__							,			a__							,			a__												= 1, 2, -3
      a__												= Matrix(3							,  1							,  0       )
      a__							,			a__							,			a__												= 4, -2, 5
      print(f"u is {u}"       )
      print(f"v is {v}"       )
      print(f"uv^T is {u * v.transpose()}"       )
      # Sherman Morrison
      print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(__UpperCAmelCase							,  __UpperCAmelCase       )}"       )






     def     __a					(		):
      import doctest

      doctest.testmod()

     testa()





