
"""simple docstring"""



import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any		)     ->					Optional[Any]:
  _snake_case					     =       filter(lambda __lowerCamelCase		: p.requires_grad ,					model.parameters()		)
  _snake_case					     =       sum([np.prod(p.size()		) for p in model_parameters]		)
  return params


UpperCAmelCase__          = logging.getLogger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       Dict		)     ->					str:
  if metric == "rouge2":
    _snake_case					     =       '''{val_avg_rouge2:.4f}-{step_count}'''
  elif metric == "bleu":
    _snake_case					     =       '''{val_avg_bleu:.4f}-{step_count}'''
  elif metric == "em":
    _snake_case					     =       '''{val_avg_em:.4f}-{step_count}'''
  else:
    raise NotImplementedError(
        f'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
        ''' function.'''		)

  _snake_case					     =       ModelCheckpoint(
      dirpath=__lowerCamelCase ,					filename=__lowerCamelCase ,					monitor=f'''val_{metric}''' ,					mode='''max''' ,					save_top_k=3 ,					every_n_epochs=1 ,					)
  return checkpoint_callback
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       List[str]		)     ->					List[str]:
  return EarlyStopping(
      monitor=f'''val_{metric}''' ,					mode='''min''' if '''loss''' in metric else '''max''' ,					patience=__lowerCamelCase ,					verbose=__lowerCamelCase ,					)

class       lowerCAmelCase__				(						pl.Callback	):







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			str			):
       _snake_case					     =       {f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
       pl_module.logger.log_metrics(_lowerCamelCase			)







     @rank_zero_only
     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			pl.Trainer    ,      _lowerCamelCase			:			pl.LightningModule    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Tuple=True			):
       logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''			)
       _snake_case					     =       trainer.callback_metrics
       trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}			)
       # Log results
       _snake_case					     =       Path(pl_module.hparams.output_dir			)
       if type_path == "test":
         _snake_case					     =       od / '''test_results.txt'''
         _snake_case					     =       od / '''test_generations.txt'''
       else:
         # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
         # If people want this it will be easy enough to add back.
         _snake_case					     =       od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
         _snake_case					     =       od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
         results_file.parent.mkdir(exist_ok=_lowerCamelCase			)
         generations_file.parent.mkdir(exist_ok=_lowerCamelCase			)
       with open(_lowerCamelCase    ,      '''a+'''			) as writer:
         for key in sorted(_lowerCamelCase			):
           if key in ["log", "progress_bar", "preds"]:
             continue
           _snake_case					     =       metrics[key]
           if isinstance(_lowerCamelCase    ,      torch.Tensor			):
             _snake_case					     =       val.item()
           _snake_case					     =       f'''{key}: {val:.6f}\n'''
           writer.write(_lowerCamelCase			)

       if not save_generations:
         return

       if "preds" in metrics:
         _snake_case					     =       '''\n'''.join(metrics['''preds''']			)
         generations_file.open('''w+'''			).write(_lowerCamelCase			)







     @rank_zero_only
     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			List[str]			):
       try:
         _snake_case					     =       pl_module.model.model.num_parameters()
       except AttributeError:
         _snake_case					     =       pl_module.model.num_parameters()

       _snake_case					     =       count_trainable_parameters(_lowerCamelCase			)
       # mp stands for million parameters
       trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6}			)







     @rank_zero_only
     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			pl.Trainer    ,      _lowerCamelCase			:			pl.LightningModule			):
       save_json(pl_module.metrics    ,      pl_module.metrics_save_path			)
       return self._write_logs(_lowerCamelCase    ,      _lowerCamelCase    ,      '''test'''			)







     @rank_zero_only
     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			pl.Trainer    ,      _lowerCamelCase			:			Any			):
       save_json(pl_module.metrics    ,      pl_module.metrics_save_path			)
       # Uncommenting this will save val generations
       # return self._write_logs(trainer, pl_module, "valid")





"""simple docstring"""



# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str		)     ->					List[Any]:
  return 1 / (1 + np.exp(-z		))
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       List[str]		)     ->					Optional[Any]:
  return (-y * np.log(__lowerCamelCase		) - (1 - y) * np.log(1 - h		)).mean()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict		)     ->					List[str]:
  _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
  return np.sum(y * scores - np.log(1 + np.exp(__lowerCamelCase		)		)		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str=7_00_00		)     ->					Optional[Any]:
  _snake_case					     =       np.zeros(x.shape[1]		)

  for iterations in range(__lowerCamelCase		):
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       np.dot(x.T ,					h - y		) / y.size
    _snake_case					     =       theta - alpha * gradient  # updating the weights
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       cost_function(__lowerCamelCase ,					__lowerCamelCase		)
    if iterations % 1_00 == 0:
      print(f'''loss: {j} \t'''		)  # printing the loss after every 100 iterations
  return theta


# In[68]:

if __name__ == "__main__":
 UpperCAmelCase__          = datasets.load_iris()
 UpperCAmelCase__          = iris.data[:, :2]
 UpperCAmelCase__          = (iris.target != 0) * 1

 UpperCAmelCase__          = 0.1
 UpperCAmelCase__          = logistic_reg(alpha, x, y, max_iterations=70000)
 print('theta: ', theta)  # printing the theta i.e our weights vector
 def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Union[str, Any]:
   return sigmoid_function(
       np.dot(__lowerCamelCase ,					__lowerCamelCase		)		)  # predicting the value of probability from the logistic regression algorithm

 plt.figure(figsize=(10, 6))
 plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
 plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = (x[:, 0].min(), x[:, 0].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = (x[:, 1].min(), x[:, 1].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 UpperCAmelCase__          = np.c_[xxa.ravel(), xxa.ravel()]
 UpperCAmelCase__          = predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

 plt.legend()

 plt.show()



