


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 		snake_case_   (				lowercase__     :    List[Any]     ):






				'''simple docstring'''






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


__SCREAMING_SNAKE_CASE	:							List[str]           =  logging.getLogger(__name__)


class        __lowerCamelCase      (				pl.Callback ):


				"""simple docstring"""







				def        lowerCAmelCase__			(				self     :    Any	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    Optional[int]					):
								_lowerCAmelCase  						={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        lowerCAmelCase__			(				self     :    Optional[Any]	,							lowerCamelCase_     :    pl.Trainer	,							lowerCamelCase_     :    pl.LightningModule	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    List[Any]=True					):
								logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"					)
								_lowerCAmelCase  						=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
								_lowerCAmelCase  						=Path(pl_module.hparams.output_dir					)
								if type_path == "test":
												_lowerCAmelCase  						=od / """test_results.txt"""
												_lowerCAmelCase  						=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.
												_lowerCAmelCase  						=od / F"{type_path}_results/{trainer.global_step:05d}.txt"
												_lowerCAmelCase  						=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
																_lowerCAmelCase  						=metrics[key]
																if isinstance(lowerCamelCase_	,							torch.Tensor					):
																				_lowerCAmelCase  						=val.item()
																_lowerCAmelCase  						=F"{key}: {val:.6f}\n"
																writer.write(lowerCamelCase_					)

								if not save_generations:
												return

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






				@rank_zero_only
				def        lowerCAmelCase__			(				self     :    Union[str, Any]	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    Any					):
								try:
												_lowerCAmelCase  						=pl_module.model.model.num_parameters()
								except AttributeError:
												_lowerCAmelCase  						=pl_module.model.num_parameters()

								_lowerCAmelCase  						=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        lowerCAmelCase__			(				self     :    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        lowerCAmelCase__			(				self     :    Tuple	,							lowerCamelCase_     :    pl.Trainer	,							lowerCamelCase_     :    Optional[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")

def 		snake_case_   (				lowercase__     :    Dict       ,     lowercase__     :    Optional[Any]       ,     lowercase__     :    Any=1       ,     lowercase__     :    int=False     ):






				'''simple docstring'''






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

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





def 		snake_case_   (				lowercase__     :    Any       ,     lowercase__     :    List[str]     ):






				'''simple docstring'''






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





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    bool     ):






				'''simple docstring'''






				if not inplace:
								_lowerCAmelCase  						=ma + ma
				else:
								ma += ma

				return ma

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    List[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=-1 * len(lowercase__     )
				_lowerCAmelCase  						=list(range(len(tensor.shape[:zero_index]     )     )     )
				return tensor.permute(first_inds + [zero_index + i for i in inds]     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    int     ):






				'''simple docstring'''






				return t.reshape(t.shape[:-no_dims] + (-1,)     )

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int       ,     lowercase__     :    float = 1e-4     ):






				'''simple docstring'''






				_lowerCAmelCase  						=mask.expand(*value.shape     )
				return torch.sum(mask * value       ,     dim=lowercase__     ) / (eps + torch.sum(lowercase__       ,     dim=lowercase__     ))

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.types.Number = 2.3_1_2_5       ,     lowercase__     :    torch.types.Number = 2_1.6_8_7_5       ,     lowercase__     :    int = 64     ):






				'''simple docstring'''






				_lowerCAmelCase  						=torch.linspace(lowercase__       ,     lowercase__       ,     no_bins - 1       ,     device=pts.device     )
				_lowerCAmelCase  						=torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2       ,     dim=-1     )     )
				return torch.bucketize(lowercase__       ,     lowercase__     )

def 		snake_case_   (				lowercase__     :    Callable[[list], Any]       ,     lowercase__     :    List[dict]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=dicts[0]
				_lowerCAmelCase  						={}
				for k, v in first.items():
								_lowerCAmelCase  						=[d[k] for d in dicts]
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_multimap(lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=fn(lowercase__     )

				return new_dict

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor     ):






				'''simple docstring'''






				_lowerCAmelCase  						=v_bins.view(((1,) * len(x.shape     )) + (len(lowercase__     ),)     )
				_lowerCAmelCase  						=x[..., None] - reshaped_bins
				_lowerCAmelCase  						=torch.argmin(torch.abs(lowercase__     )       ,     dim=-1     )
				return nn.functional.one_hot(lowercase__       ,     num_classes=len(lowercase__     )     ).float()

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int = 0       ,     lowercase__     :    int = 0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[]
				for i, s in enumerate(data.shape[:no_batch_dims]     ):
								_lowerCAmelCase  						=torch.arange(lowercase__     )
								_lowerCAmelCase  						=r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
								ranges.append(lowercase__     )

				_lowerCAmelCase  						=[slice(lowercase__     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
				_lowerCAmelCase  						=inds
				ranges.extend(lowercase__     )
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowercase__     )]


__SCREAMING_SNAKE_CASE	:							str           =  TypeVar('''T''')

def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    Dict[Any, Union[dict, list, tuple, T]]       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				_lowerCAmelCase  						={}
				for k, v in dic.items():
								if isinstance(lowercase__       ,     lowercase__     ):
												_lowerCAmelCase  						=dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
								else:
												_lowerCAmelCase  						=tree_map(lowercase__       ,     lowercase__       ,     lowercase__     )

				return new_dict

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    T       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    dict       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    list       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    tuple       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				if isinstance(lowercase__       ,     lowercase__     ):
								return dict_map(lowercase__       ,     lowercase__       ,     lowercase__     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return [tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree]
				elif isinstance(lowercase__       ,     lowercase__     ):
								return tuple(tree_map(lowercase__       ,     lowercase__       ,     lowercase__     ) for x in tree     )
				elif isinstance(lowercase__       ,     lowercase__     ):
								return fn(lowercase__     )
				else:
								print(type(lowercase__     )     )
								raise ValueError("""Not supported"""     )





__SCREAMING_SNAKE_CASE	:							Any           =  partial(tree_map, leaf_type=torch.Tensor)

