


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      a	(							SCREAMING_SNAKE_CASE_      :		str       ):






							"""simple docstring"""





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


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


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Dict       ):






							"""simple docstring"""





							if metric == "rouge2":
														UpperCamelCase    :	str   	=		'''{val_avg_rouge2:.4f}-{step_count}'''
							elif metric == "bleu":
														UpperCamelCase    :	Optional[Any]   	=		'''{val_avg_bleu:.4f}-{step_count}'''
							elif metric == "em":
														UpperCamelCase    :	int   	=		'''{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.'''       )

							UpperCamelCase    :	Tuple   	=		ModelCheckpoint(
							    dirpath=SCREAMING_SNAKE_CASE_							,			filename=SCREAMING_SNAKE_CASE_							,			monitor=F"""val_{metric}"""							,			mode='''max'''							,			save_top_k=3							,			every_n_epochs=1							,			)
							return checkpoint_callback


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		List[Any]       ):






							"""simple docstring"""





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



class    UpperCAmelCase_	(							pl.Callback):
							'''simple docstring'''






							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														UpperCamelCase    :	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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=True						):




														"""simple docstring"""




														logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""						)
														UpperCamelCase    :	Union[str, Any]   	=		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    :	List[str]   	=		Path(pl_module.hparams.output_dir						)
														if type_path == "test":
																					UpperCamelCase    :	Union[str, Any]   	=		od / '''test_results.txt'''
																					UpperCamelCase    :	Any   	=		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    :	Optional[int]   	=		od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
																					UpperCamelCase    :	Tuple   	=		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
																												UpperCamelCase    :	Any   	=		metrics[key]
																												if isinstance(__SCREAMING_SNAKE_CASE ,					torch.Tensor						):
																																			UpperCamelCase    :	Any   	=		val.item()
																												UpperCamelCase    :	Dict   	=		f"""{key}: {val:.6f}\n"""
																												writer.write(__SCREAMING_SNAKE_CASE						)

														if not save_generations:
																					return

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






							@rank_zero_only
							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														try:
																					UpperCamelCase    :	Optional[int]   	=		pl_module.model.model.num_parameters()
														except AttributeError:
																					UpperCamelCase    :	Dict   	=		pl_module.model.num_parameters()

														UpperCamelCase    :	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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




														"""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")





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase : Optional[int] 			=				500000

__UpperCAmelCase			,    __UpperCAmelCase : Any 			=				os.path.split(__file__)
__UpperCAmelCase : int 			=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		dataset.map(**SCREAMING_SNAKE_CASE_       )


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		dataset.filter(**SCREAMING_SNAKE_CASE_       )


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCamelCase    :	Dict   	=		datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
              UpperCamelCase    :	List[str]   	=		generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE_							,			'''dataset.arrow'''       )							,			SCREAMING_SNAKE_CASE_							,			num_examples=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Union[str, Any]   	=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''							,			use_fast=SCREAMING_SNAKE_CASE_       )

              def tokenize(SCREAMING_SNAKE_CASE_      :		Dict       ):
                     return tokenizer(examples['''text''']       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''numpy'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''pandas'''       ):
                     UpperCamelCase    :	int   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''torch'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Dict   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''tensorflow'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			function=SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Dict   	=		filter(SCREAMING_SNAKE_CASE_       )

              # Activate later when tokenizer support batched inputs
              # with dataset.formatted_as(type='numpy'):
              #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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

