


'''simple docstring'''
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				(      __a       )  ->     List[str]:



					'''simple docstring'''






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


__snake_case											=					logging.getLogger(__name__)






def       a				(      __a			, __a       )  ->     int:



					'''simple docstring'''






					if metric == "rouge2":
										UpperCamelCase__       :Any							 =			"""{val_avg_rouge2:.4f}-{step_count}"""
					elif metric == "bleu":
										UpperCamelCase__       :Tuple							 =			"""{val_avg_bleu:.4f}-{step_count}"""
					elif metric == "em":
										UpperCamelCase__       :Dict							 =			"""{val_avg_em:.4f}-{step_count}"""
					elif metric == "loss":
										UpperCamelCase__       :Optional[int]							 =			"""{val_avg_loss:.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__       :List[Any]							 =			ModelCheckpoint(
					    dirpath=lowercase__			, filename=lowercase__			, monitor=f'''val_{metric}'''			, mode='''max'''			, save_top_k=1			, every_n_epochs=1			, )

					return checkpoint_callback






def       a				(      __a			, __a       )  ->     Optional[Any]:



					'''simple docstring'''






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







class        lowercase     (							pl.Callback ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			{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   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_=True							):

							'''simple docstring'''


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

							if not save_generations:
												return

							if "preds" in metrics:
												UpperCamelCase__       :List[Any]							 =			"""\n""".join(metrics['''preds''']							)
												generations_file.open('''w+'''							).write(__A							)




		@rank_zero_only
		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							try:
												UpperCamelCase__       :Optional[int]							 =			pl_module.model.model.num_parameters()
							except AttributeError:
												UpperCamelCase__       :Any							 =			pl_module.model.num_parameters()

							UpperCamelCase__       :int							 =			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   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''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   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''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")



'''simple docstring'''
import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class        lowercase     (							unittest.TestCase ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			logging.get_logger()

							# the current default level is logging.WARNING
							UpperCamelCase__       :List[Any]							 =			logging.get_verbosity()

							logging.set_verbosity_error()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							logging.set_verbosity_warning()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							logging.set_verbosity_info()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							logging.set_verbosity_debug()
							self.assertEqual(logger.getEffectiveLevel()   ,       logging.get_verbosity()							)

							# restore to the original level
							logging.set_verbosity(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			logging.get_verbosity()

							UpperCamelCase__       :Union[str, Any]							 =			logging.get_logger('''transformers.models.bart.tokenization_bart'''							)
							UpperCamelCase__       :Optional[Any]							 =			'''Testing 1, 2, 3'''

							# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
							if level_origin <= logging.WARNING:
												with CaptureLogger(UpperCamelCase_							) as cl:
																	logger.warning(UpperCamelCase_							)
												self.assertEqual(cl.out   ,       msg + '''\n'''							)

							# this is setting the level for all of `transformers.*` loggers
							logging.set_verbosity_error()

							# should not be able to log warnings
							with CaptureLogger(UpperCamelCase_							) as cl:
												logger.warning(UpperCamelCase_							)
							self.assertEqual(cl.out   ,       ''''''							)

							# should be able to log warnings again
							logging.set_verbosity_warning()
							with CaptureLogger(UpperCamelCase_							) as cl:
												logger.warning(UpperCamelCase_							)
							self.assertEqual(cl.out   ,       msg + '''\n'''							)

							# restore to the original level
							logging.set_verbosity(UpperCamelCase_							)




		@mockenv(TRANSFORMERS_VERBOSITY='''error'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							transformers.utils.logging._reset_library_root_logger()
							# this action activates the env var
							UpperCamelCase__       :Tuple							 =			logging.get_logger('''transformers.models.bart.tokenization_bart'''							)

							UpperCamelCase__       :Any							 =			os.getenv('''TRANSFORMERS_VERBOSITY'''   ,       UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			logging.log_levels[env_level_str]

							UpperCamelCase__       :int							 =			logging.get_verbosity()
							self.assertEqual(
							    UpperCamelCase_   ,       UpperCamelCase_   ,       F'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''   ,       )

							# restore to the original level
							UpperCamelCase__       :Union[str, Any]							 =			''''''
							transformers.utils.logging._reset_library_root_logger()




		@mockenv(TRANSFORMERS_VERBOSITY='''super-error'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							transformers.utils.logging._reset_library_root_logger()
							UpperCamelCase__       :Dict							 =			logging.logging.getLogger()
							with CaptureLogger(UpperCamelCase_							) as cl:
												# this action activates the env var
												logging.get_logger('''transformers.models.bart.tokenization_bart'''							)
							self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''   ,       cl.out							)

							# no need to restore as nothing was changed




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							transformers.utils.logging._reset_library_root_logger()

							UpperCamelCase__       :Optional[int]							 =			logging.get_logger('''transformers.models.bart.tokenization_bart'''							)
							UpperCamelCase__       :int							 =			'''Testing 1, 2, 3'''

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''							):
												# nothing should be logged as env var disables this method
												with CaptureLogger(UpperCamelCase_							) as cl:
																	logger.warning_advice(UpperCamelCase_							)
												self.assertEqual(cl.out   ,       ''''''							)

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''							):
												# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
												with CaptureLogger(UpperCamelCase_							) as cl:
																	logger.warning_advice(UpperCamelCase_							)
												self.assertEqual(cl.out   ,       msg + '''\n'''							)



def       a				(      )  ->     str:



					'''simple docstring'''






					disable_progress_bar()
					assert are_progress_bars_disabled()

					enable_progress_bar()
					assert not are_progress_bars_disabled()