




import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Optional[Any]		   =							copy.deepcopy(UpperCAmelCase       )
					__magic_name__					:							int		   =							sorted(UpperCAmelCase       )
					__magic_name__					:							Tuple		   =							[bisect.bisect_right(UpperCAmelCase,	UpperCAmelCase       ) for y in x]
					return quantized







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase=0       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							np.arange(start=3,	stop=UpperCAmelCase,	step=4       ).tolist() if k > 0 else [10]
					__magic_name__					:							str		   =							_quantize(UpperCAmelCase,	UpperCAmelCase       )
					# count number of elements per group
					__magic_name__					:							Dict		   =							np.unique(UpperCAmelCase,	return_counts=UpperCAmelCase       )[1]
					__magic_name__					:							List[str]		   =							[0] + bins + [np.inf]
					logger.info('''Using {} as bins for aspect lengths quantization'''.format(UpperCAmelCase       )       )
					logger.info('''Count of instances per bin: {}'''.format(UpperCAmelCase       )       )
					return groups





class 		A__				(   __SCREAMING_SNAKE_CASE ):






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   Union[str, Any]:




						"""simple docstring"""






						if not isinstance(lowerCamelCase				,     lowerCamelCase						):
											raise ValueError(
											    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(lowerCamelCase						)						)
						__magic_name__					:							int		   =							sampler
						__magic_name__					:							Optional[Any]		   =							group_ids
						__magic_name__					:							Tuple		   =							batch_size






	def __iter__(	self						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							defaultdict(lowerCamelCase						)
						__magic_name__					:							List[Any]		   =							defaultdict(lowerCamelCase						)

						__magic_name__					:							Optional[Any]		   =							0
						for idx in self.sampler:
											__magic_name__					:							List[Any]		   =							self.group_ids[idx]
											buffer_per_group[group_id].append(lowerCamelCase						)
											samples_per_group[group_id].append(lowerCamelCase						)
											if len(buffer_per_group[group_id]						) == self.batch_size:
																yield buffer_per_group[group_id]  # TODO
																num_batches += 1
																del buffer_per_group[group_id]
											assert len(buffer_per_group[group_id]						) < self.batch_size

						# now we have run out of elements that satisfy
						# the group criteria, let's return the remaining
						# elements so that the size of the sampler is
						# deterministic
						__magic_name__					:							Optional[int]		   =							len(self						)
						__magic_name__					:							Union[str, Any]		   =							expected_num_batches - num_batches
						if num_remaining > 0:
											# for the remaining batches, group the batches by similar lengths
											__magic_name__					:							int		   =							[]
											for group_id, idxs in sorted(buffer_per_group.items()				,     key=lambda lowerCamelCase						: x[0]						):
																batch_idx.extend(lowerCamelCase						)
																if len(lowerCamelCase						) >= self.batch_size:
																					yield batch_idx[: self.batch_size]
																					__magic_name__					:							Tuple		   =							batch_idx[self.batch_size :]
																					num_remaining -= 1
											if len(lowerCamelCase						) > 0:
																yield batch_idx
																num_remaining -= 1
						assert num_remaining == 0






	def __len__(	self						)      ->   Optional[Any]:




						"""simple docstring"""






						return (len(self.sampler						) + self.batch_size - 1) // self.batch_size









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 import save_json

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      filter(lambda __UpperCamelCase: p.requires_grad		,					model.parameters())
     a      =      sum([np.prod(p.size()) for p in model_parameters])
     return params


lowercase__      :			Optional[int]           =					logging.getLogger(__name__)




class 			a__       (					pl.Callback      ):







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    str:



          '''simple docstring'''



          a      =      {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 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A  ,		A=True )  ->    None:



          '''simple docstring'''



          logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
          a      =      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      =      Path(pl_module.hparams.output_dir )
          if type_path == "test":
               a      =      od / "test_results.txt"
               a      =      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      =      od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
               a      =      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
                    a      =      metrics[key]
                    if isinstance(A  ,		torch.Tensor ):
                         a      =      val.item()
                    a      =      F'''{key}: {val:.6f}\n'''
                    writer.write(A )

          if not save_generations:
               return

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







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Any:



          '''simple docstring'''



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

          a      =      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 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Optional[int]:



          '''simple docstring'''



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







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    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")







def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase=1		,					__UpperCamelCase=False)			->     Optional[int]:
     if metric == "rouge2":
          a      =      "{val_avg_rouge2:.4f}-{step_count}"
     elif metric == "bleu":
          a      =      "{val_avg_bleu:.4f}-{step_count}"
     elif metric == "loss":
          a      =      "{val_avg_loss:.4f}-{step_count}"
     else:
          raise NotImplementedError(
              f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
              " this function.")

     a      =      ModelCheckpoint(
         dirpath=__UpperCamelCase		,					filename=__UpperCamelCase		,					monitor=f'''val_{metric}'''		,					mode="min" if "loss" in metric else "max"		,					save_top_k=__UpperCamelCase		,					)
     return checkpoint_callback





def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     Union[str, Any]:
     return EarlyStopping(
         monitor=f'''val_{metric}'''		,					mode="min" if "loss" in metric else "max"		,					patience=__UpperCamelCase		,					verbose=__UpperCamelCase		,					)

