
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  A			(   lowercase					)  ->    Union[str, Any]:
		'''simple docstring'''

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


_UpperCAmelCase   :					Any    =			logging.getLogger(__name__)


class 							lowercase   (						pl.Callback  ):



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   Optional[Any]:
				"""simple docstring"""
				UpperCamelCase            =	{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 __UpperCamelCase					(					self							, A_							, A_							, A_							, A_=True			)    ->   None:
				"""simple docstring"""
				logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''			)
				UpperCamelCase            =	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
				UpperCamelCase            =	Path(pl_module.hparams.output_dir			)
				if type_path == "test":
						UpperCamelCase            =	od / 'test_results.txt'
						UpperCamelCase            =	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.
						UpperCamelCase            =	od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
						UpperCamelCase            =	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
								UpperCamelCase            =	metrics[key]
								if isinstance(A_							, torch.Tensor			):
										UpperCamelCase            =	val.item()
								UpperCamelCase            =	F'''{key}: {val:.6f}\n'''
								writer.write(A_			)

				if not save_generations:
						return

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



		@rank_zero_only
		def __UpperCamelCase					(					self							, A_							, A_			)    ->   int:
				"""simple docstring"""
				try:
						UpperCamelCase            =	pl_module.model.model.num_parameters()
				except AttributeError:
						UpperCamelCase            =	pl_module.model.num_parameters()

				UpperCamelCase            =	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 __UpperCamelCase					(					self							, A_							, A_			)    ->   Tuple:
				"""simple docstring"""
				save_json(pl_module.metrics							, pl_module.metrics_save_path			)
				return self._write_logs(A_							, A_							, 'test'			)



		@rank_zero_only
		def __UpperCamelCase					(					self							, A_							, A_			)    ->   List[Any]:
				"""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  A			(   lowercase       ,			lowercase       ,			lowercase=1       ,			lowercase=False					)  ->    Union[str, Any]:
		'''simple docstring'''

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

		UpperCamelCase            =	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  A			(   lowercase       ,			lowercase					)  ->    Optional[int]:
		'''simple docstring'''

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


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_UpperCAmelCase   :					str    =			100_000_000_000
_UpperCAmelCase   :					Optional[int]    =			10_000

_UpperCAmelCase  ,_UpperCAmelCase   :					Optional[int]    =			os.path.split(__file__)
_UpperCAmelCase   :					Optional[int]    =			os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


def  A			(   lowercase       ,			lowercase					)  ->    datasets.Dataset:
		'''simple docstring'''

		UpperCamelCase            =	pa.Table.from_pydict({'col': [0] * chunk_size}					)
		UpperCamelCase            =	pa.concat_tables([table] * (num_examples // chunk_size)					)
		return datasets.Dataset(lowercase       ,			fingerprint='table_100B'					)


@dataclass
class 							lowercase   :
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	np.random.default_rng(self.seed			)
				UpperCamelCase            =	rng.integers(low=self.low							, high=self.high							, size=self.size			).tolist()



		def __iter__(					self			)    ->   List[str]:
				"""simple docstring"""
				return iter(self._sampled_values			)



		def __len__(					self			)    ->   int:
				"""simple docstring"""
				return self.size






@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[0]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[-1]


@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[range(len(lowercase					) // 2       ,			len(lowercase					) // 2 + 1_024					)]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[RandIter(0       ,			len(lowercase					)       ,			1_024       ,			seed=42					)]


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	{'num examples': SPEED_TEST_N_EXAMPLES}
		UpperCamelCase            =	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
		print('generating dataset'					)
		UpperCamelCase            =	generate_100B_dataset(num_examples=lowercase       ,			chunk_size=lowercase					)
		print('Functions'					)
		for func in functions:
				print(func.__name__					)
				UpperCamelCase            =	func(lowercase					)

		with open(lowercase       ,			'wb'					) as f:
				f.write(json.dumps(lowercase					).encode('utf-8'					)					)


if __name__ == "__main__":  # useful to run the profiler
					benchmark_table_100B()
