






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 				snake_case  (     lowerCamelCase					):




 '''simple docstring'''







 __lowercase             =  filter(lambda lowerCamelCase					: p.requires_grad				,  model.parameters()					)
 __lowercase             =  sum([np.prod(p.size()					) for p in model_parameters]					)
 return params


__UpperCamelCase      :      Dict     						=						logging.getLogger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 if metric == "rouge2":
  __lowercase             =  """{val_avg_rouge2:.4f}-{step_count}"""
 elif metric == "bleu":
  __lowercase             =  """{val_avg_bleu:.4f}-{step_count}"""
 elif metric == "em":
  __lowercase             =  """{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."""					)

 __lowercase             =  ModelCheckpoint(
     dirpath=__snake_case				,  filename=__snake_case				,  monitor=F'val_{metric}'				,  mode="""max"""				,  save_top_k=3				,  every_n_epochs=1				,  )
 return checkpoint_callback





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 return EarlyStopping(
     monitor=F'val_{metric}'				,  mode="""min""" if """loss""" in metric else """max"""				,  patience=__snake_case				,  verbose=__snake_case				,  )




class        __UpperCamelCase    (     pl.Callback   ):







 def  _a   (  self      :							int       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Dict			)		-> Any:
  """simple docstring"""







  __lowercase             =  {F'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
  pl_module.logger.log_metrics(_A			)







 @rank_zero_only
 def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							pl.Trainer       ,				_lowerCAmelCase      :							pl.LightningModule       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Tuple=True			)		-> Dict:
  """simple docstring"""







  logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'			)
  __lowercase             =  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
  __lowercase             =  Path(pl_module.hparams.output_dir			)
  if type_path == "test":
   __lowercase             =  od / """test_results.txt"""
   __lowercase             =  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.
   __lowercase             =  od / F'{type_path}_results/{trainer.global_step:05d}.txt'
   __lowercase             =  od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
   results_file.parent.mkdir(exist_ok=_A			)
   generations_file.parent.mkdir(exist_ok=_A			)
  with open(_A       ,				"""a+"""			) as writer:
   for key in sorted(_A			):
    if key in ["log", "progress_bar", "preds"]:
     continue
    __lowercase             =  metrics[key]
    if isinstance(_A       ,				torch.Tensor			):
     __lowercase             =  val.item()
    __lowercase             =  F'{key}: {val:.6f}\n'
    writer.write(_A			)

  if not save_generations:
   return

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







 @rank_zero_only
 def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Tuple			)		-> Optional[int]:
  """simple docstring"""







  try:
   __lowercase             =  pl_module.model.model.num_parameters()
  except AttributeError:
   __lowercase             =  pl_module.model.num_parameters()

  __lowercase             =  count_trainable_parameters(_A			)
  # 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  _a   (  self      :							str       ,				_lowerCAmelCase      :							pl.Trainer       ,				_lowerCAmelCase      :							pl.LightningModule			)		-> List[Any]:
  """simple docstring"""







  save_json(pl_module.metrics       ,				pl_module.metrics_save_path			)
  return self._write_logs(_A       ,				_A       ,				"""test"""			)







 @rank_zero_only
 def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							pl.Trainer       ,				_lowerCAmelCase      :							str			)		-> Dict:
  """simple docstring"""







  save_json(pl_module.metrics       ,				pl_module.metrics_save_path			)
  # Uncommenting this will save val generations
  # return self._write_logs(trainer, pl_module, "valid")









from __future__ import annotations

from scipy.special import comb  # type: ignore


class        __UpperCamelCase    :







	def __init__(  self      :							int       ,				_lowerCAmelCase      :							list[tuple[float, float]]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  list_of_points
		# Degree determines the flexibility of the curve.
		# Degree = 1 will produce a straight line.
		__lowercase             =  len(_lowerCAmelCase			) - 1







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							float			)		-> list[float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."
		__lowercase             =  []
		for i in range(len(self.list_of_points			)			):
			# basis function for each i
			output_values.append(
			    comb(self.degree       ,				_lowerCAmelCase			) * ((1 - t) ** (self.degree - i)) * (t**i)			)
		# the basis must sum up to 1 for it to produce a valid Bezier curve.
		assert round(sum(_lowerCAmelCase			)       ,				5			) == 1
		return output_values







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							float			)		-> tuple[float, float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."

		__lowercase             =  self.basis_function(_lowerCAmelCase			)
		__lowercase             =  0.0
		__lowercase             =  0.0
		for i in range(len(self.list_of_points			)			):
			# For all points, sum up the product of i-th basis function and i-th point.
			x += basis_function[i] * self.list_of_points[i][0]
			y += basis_function[i] * self.list_of_points[i][1]
		return (x, y)







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							float = 0.01			)		-> Union[str, Any]:
		"""simple docstring"""







		from matplotlib import pyplot as plt  # type: ignore

		__lowercase             =  []  # x coordinates of points to plot
		__lowercase             =  []  # y coordinates of points to plot

		__lowercase             =  0.0
		while t <= 1:
			__lowercase             =  self.bezier_curve_function(_lowerCAmelCase			)
			to_plot_x.append(value[0]			)
			to_plot_y.append(value[1]			)
			t += step_size

		__lowercase             =  [i[0] for i in self.list_of_points]
		__lowercase             =  [i[1] for i in self.list_of_points]

		plt.plot(
		    _lowerCAmelCase       ,				_lowerCAmelCase       ,				color="""blue"""       ,				label="""Curve of Degree """ + str(self.degree			)       ,				)
		plt.scatter(_lowerCAmelCase       ,				_lowerCAmelCase       ,				color="""red"""       ,				label="""Control Points"""			)
		plt.legend()
		plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
						BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
						BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

