




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 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Any			=						filter(lambda snake_case_     : p.requires_grad,model.parameters()     )
      _A							:     Any			=						sum([np.prod(p.size()     ) for p in model_parameters]     )
      return params


_snake_case										=       logging.getLogger(__name__)







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      if metric == "rouge2":
            _A							:     Any			=						"""{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            _A							:     Tuple			=						"""{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "em":
            _A							:     Tuple			=						"""{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."""     )

      _A							:     List[Any]			=						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 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      return EarlyStopping(
          monitor=f'''val_{metric}''',mode="""min""" if """loss""" in metric else """max""",patience=_snake_case,verbose=_snake_case,)







class 						lowercase							(   pl.Callback		):

     def 					a__	(  self		,     _a		,     _a				)						->							Any:
           _A							:     Union[str, Any]			=						{F'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups				)}
           pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__				)

     @rank_zero_only
     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=True				)						->							None:
           logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''				)
           _A							:     Tuple			=						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
           _A							:     Any			=						Path(pl_module.hparams.output_dir				)
           if type_path == "test":
                 _A							:     Optional[Any]			=						od / """test_results.txt"""
                 _A							:     Tuple			=						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.
                 _A							:     List[str]			=						od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
                 _A							:     int			=						od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
                 results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
                 generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
           with open(SCREAMING_SNAKE_CASE__		,     """a+"""				) as writer:
                 for key in sorted(SCREAMING_SNAKE_CASE__				):
                       if key in ["log", "progress_bar", "preds"]:
                             continue
                       _A							:     Any			=						metrics[key]
                       if isinstance(SCREAMING_SNAKE_CASE__		,     torch.Tensor				):
                             _A							:     Union[str, Any]			=						val.item()
                       _A							:     Optional[int]			=						F'''{key}: {val:.6f}\n'''
                       writer.write(SCREAMING_SNAKE_CASE__				)

           if not save_generations:
                 return

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

     @rank_zero_only
     def 					a__	(  self		,     _a		,     _a				)						->							Optional[Any]:




           try:
                 _A							:     Tuple			=						pl_module.model.model.num_parameters()
           except AttributeError:
                 _A							:     Union[str, Any]			=						pl_module.model.num_parameters()

           _A							:     int			=						count_trainable_parameters(SCREAMING_SNAKE_CASE__				)
           # 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		,     _a		,     _a				)						->							Tuple:
           save_json(pl_module.metrics		,     pl_module.metrics_save_path				)
           return self._write_logs(SCREAMING_SNAKE_CASE__		,     SCREAMING_SNAKE_CASE__		,     """test"""				)



     @rank_zero_only
     def 					a__	(  self		,     _a		,     _a				)						->							str:
           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"""




import mpmath  # for roots of unity
import numpy as np



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   list(poly_a or [0]    )[:]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   list(poly_b or [0]    )[:]

											# Remove leading zero coefficients
											while self.polyA[-1] == 0:
																		self.polyA.pop()
											SCREAMING_SNAKE_CASE__						:       int           =   len(self.polyA    )

											while self.polyB[-1] == 0:
																		self.polyB.pop()
											SCREAMING_SNAKE_CASE__						:       List[str]           =   len(self.polyB    )

											# Add 0 to make lengths equal a power of 2
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   int(
											    2 ** np.ceil(np.loga(len(self.polyA    ) + len(self.polyB    ) - 1    )    )    )

											while len(self.polyA    ) < self.c_max_length:
																		self.polyA.append(0    )
											while len(self.polyB    ) < self.c_max_length:
																		self.polyB.append(0    )
											# A complex root used for the fourier transform
											SCREAMING_SNAKE_CASE__						:       List[str]           =   complex(mpmath.root(x=1   ,       n=self.c_max_length   ,       k=1    )    )

											# The product
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.__multiply()



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   [[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
											# Corner case
											if len(SCREAMING_SNAKE_CASE__    ) <= 1:
																		return dft[0]
											#
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.c_max_length // 2
											while next_ncol > 0:
																		SCREAMING_SNAKE_CASE__						:       Any           =   [[] for i in range(SCREAMING_SNAKE_CASE__    )]
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.root**next_ncol

																		# First half of next step
																		SCREAMING_SNAKE_CASE__						:       str           =   1
																		for j in range(self.c_max_length // (next_ncol * 2)    ):
																									for i in range(SCREAMING_SNAKE_CASE__    ):
																																new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]    )
																									current_root *= root
																		# Second half of next step
																		SCREAMING_SNAKE_CASE__						:       int           =   1
																		for j in range(self.c_max_length // (next_ncol * 2)    ):
																									for i in range(SCREAMING_SNAKE_CASE__    ):
																																new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]    )
																									current_root *= root
																		# Update
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   new_dft
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   next_ncol // 2
											return dft[0]



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.__dft("""A"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.__dft("""B"""    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [[dft_a[i] * dft_b[i] for i in range(self.c_max_length    )]]
											del dft_a
											del dft_b

											# Corner Case
											if len(inverce_c[0]    ) <= 1:
																		return inverce_c[0]
											# Inverse DFT
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   2
											while next_ncol <= self.c_max_length:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   [[] for i in range(SCREAMING_SNAKE_CASE__    )]
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.root ** (next_ncol // 2)
																		SCREAMING_SNAKE_CASE__						:       Any           =   1
																		# First half of next step
																		for j in range(self.c_max_length // next_ncol    ):
																									for i in range(next_ncol // 2    ):
																																# Even positions
																																new_inverse_c[i].append(
																																    (
																																        inverce_c[i][j]
																																        + inverce_c[i][j + self.c_max_length // next_ncol]
																																    )
																																    / 2    )
																																# Odd positions
																																new_inverse_c[i + next_ncol // 2].append(
																																    (
																																        inverce_c[i][j]
																																        - inverce_c[i][j + self.c_max_length // next_ncol]
																																    )
																																    / (2 * current_root)    )
																									current_root *= root
																		# Update
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   new_inverse_c
																		next_ncol *= 2
											# Unpack
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [round(x[0].real   ,       8    ) + round(x[0].imag   ,       8    ) * 1j for x in inverce_c]

											# Remove leading 0's
											while inverce_c[-1] == 0:
																		inverce_c.pop()
											return inverce_c



				def __str__(self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   """A = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]    )    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """B = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]    )    )
											SCREAMING_SNAKE_CASE__						:       int           =   """A*B = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.product    )    )

											return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
					import doctest

					doctest.testmod()




