





'''simple docstring'''







import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


lowerCAmelCase_					:    int      	=			TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('', '|', '|'),
    datarow=DataRow('', '|', '|'),
    padding=1,
    with_header_hide=None,
)


lowerCAmelCase_					:    Tuple      	=			[]
lowerCAmelCase_					:    Union[str, Any]      	=			[]

lowerCAmelCase_					:    Union[str, Any]      	=			{'type': 'section', 'text': {'type': 'plain_text', 'text': 'No failed tests! 🤗', 'emoji': True}}

lowerCAmelCase_					:    List[str]      	=			[
    {
        'type': 'header',
        'text': {
            'type': 'plain_text',
            'text': f"""🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results""",
            'emoji': True,
        },
    }
]

lowerCAmelCase_					:    Optional[int]      	=			0
for log in Path().glob('*.log'):
					lowerCAmelCase_					:    Any      	=			0
					with open(log, 'r') as f:
										for line in f:
															lowerCAmelCase_					:    Optional[int]      	=			json.loads(line)
															if line.get('nodeid', '') != "":
																				lowerCAmelCase_					:    Any      	=			line['nodeid']
																				if line.get('duration', None) is not None:
																									lowerCAmelCase_					:    str      	=			f"""{line['duration']:.4f}"""
																									if line.get('outcome', '') == "failed":
																														section_num_failed += 1
																														failed.append([test, duration, log.name.split('_')[0]])
																														total_num_failed += 1
					group_info.append([str(log), section_num_failed, failed])
					lowerCAmelCase_					:    Union[str, Any]      	=			[]
					log.unlink()

lowerCAmelCase_					:    Any      	=			''
lowerCAmelCase_					:    Any      	=			[]
if total_num_failed > 0:
					for name, num_failed, failed_tests in group_info:
										if num_failed > 0:
															if num_failed == 1:
																				message += f"*{name[1:]}: {num_failed} failed test*\n"
															else:
																				message += f"*{name[1:]}: {num_failed} failed tests*\n"
															lowerCAmelCase_					:    Dict      	=			[]
															lowerCAmelCase_					:    Any      	=			{}
															for test in failed_tests:
																				lowerCAmelCase_					:    Optional[Any]      	=			test[0].split('::')
																				lowerCAmelCase_					:    Union[str, Any]      	=			data[0].split('/')[-1]
																				if data[0] not in filesafailed:
																									lowerCAmelCase_					:    Optional[int]      	=			[data[1:]]
																				else:
																									filesafailed[data[0]] += [data[1:]]
																				failed_table.append(data)

															lowerCAmelCase_					:    Optional[Any]      	=			[test[0] for test in failed_table]
															lowerCAmelCase_					:    List[Any]      	=			list(set(files))
															# Count number of instances in failed_tests
															lowerCAmelCase_					:    Dict      	=			[]
															for file in individual_files:
																				table.append([file, len(filesafailed[file])])

															lowerCAmelCase_					:    int      	=			tabulate(
															    table,
															    headers=['Test Location', 'Num Failed'],
															    tablefmt=hf_table_format,
															    stralign='right',
															)
															message += f"\n```\n{failed_table}\n```"
															all_filesafailed.append(filesafailed)
					if len(message) > 30_00:
										lowerCAmelCase_					:    int      	=			'Too many failed tests, please see the full report in the Action results.'
										lowerCAmelCase_					:    Dict      	=			len(err) + 10
										lowerCAmelCase_					:    List[str]      	=			message[: 30_00 - offset] + f"""\n...\n```\n{err}"""
					print(f"""### {message}""")
else:
					lowerCAmelCase_					:    List[Any]      	=			'No failed tests! 🤗'
					print(f"""## {message}""")
					payload.append(no_error_payload)

if os.environ.get('TEST_TYPE', '') != "":
					from slack_sdk import WebClient

					lowerCAmelCase_					:    Optional[Any]      	=			WebClient(token=os.environ['SLACK_API_TOKEN'])
					if message != "No failed tests! 🤗":
										lowerCAmelCase_					:    int      	=			{
										    'type': 'section',
										    'text': {
										        'type': 'mrkdwn',
										        'text': message,
										    },
										}
										payload.append(md_report)
										lowerCAmelCase_					:    Optional[Any]      	=			{
										    'type': 'section',
										    'text': {
										        'type': 'mrkdwn',
										        'text': '*For more details:*',
										    },
										    'accessory': {
										        'type': 'button',
										        'text': {
										            'type': 'plain_text',
										            'text': 'Check Action results',
										            'emoji': True,
										        },
										        'url': f"""https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
										    },
										}
										payload.append(action_button)
										lowerCAmelCase_					:    List[str]      	=			{
										    'type': 'context',
										    'elements': [
										        {
										            'type': 'plain_text',
										            'text': f"""Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}""",
										        }
										    ],
										}
										payload.append(date_report)
					lowerCAmelCase_					:    Tuple      	=			client.chat_postMessage(channel='#accelerate-ci-daily', text=message, blocks=payload)
					lowerCAmelCase_					:    Dict      	=			response.data['ts']
					for failed_file in all_filesafailed:
										for test_location, test_failures in failed_file.items():
															# Keep only the first instance of the test name
															lowerCAmelCase_					:    List[str]      	=			''
															for i, row in enumerate(test_failures):
																				if row[0] != test_class:
																									lowerCAmelCase_					:    Dict      	=			row[0]
																				else:
																									lowerCAmelCase_					:    Any      	=			''

															lowerCAmelCase_					:    Dict      	=			{
															    'type': 'section',
															    'text': {
															        'type': 'mrkdwn',
															        'text': f"""Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```""",
															    },
															}

															client.chat_postMessage(
															    channel='#accelerate-ci-daily',
															    thread_ts=ts,
															    blocks=[payload],
															)







'''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  _lowerCamelCase				( lowercase						:      Dict						)	->	Any:
				_a								= filter(lambda lowercase						: p.requires_grad    ,							model.parameters()						)
				_a								= sum([np.prod(p.size()						) for p in model_parameters]						)
				return params


lowerCAmelCase_					:    int      	=			logging.getLogger(__name__)






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Any						)	->	Any:
				if metric == "rouge2":
								_a								= "{val_avg_rouge2:.4f}-{step_count}"
				elif metric == "bleu":
								_a								= "{val_avg_bleu:.4f}-{step_count}"
				elif metric == "em":
								_a								= "{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."						)

				_a								= ModelCheckpoint(
				    dirpath=lowercase    ,							filename=lowercase    ,							monitor=F'val_{metric}'    ,							mode="max"    ,							save_top_k=3    ,							every_n_epochs=1    ,							)
				return checkpoint_callback






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      Optional[int]						)	->	Union[str, Any]:
				return EarlyStopping(
				    monitor=F'val_{metric}'    ,							mode="min" if "loss" in metric else "max"    ,							patience=lowercase    ,							verbose=lowercase    ,							)



class 				__SCREAMING_SNAKE_CASE  (pl.Callback      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					List[Any]					):
								_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   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					,				__a     :					str					,				__a     :					Tuple=True					):
								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   UpperCamelCase__   (  self     :					int					,				__a     :					List[Any]					,				__a     :					Union[str, Any]					):
								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   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					):
								save_json(pl_module.metrics					,				pl_module.metrics_save_path					)
								return self._write_logs(__a					,				__a					,				"test"					)




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					Any					,				__a     :					pl.Trainer					,				__a     :					int					):
								save_json(pl_module.metrics					,				pl_module.metrics_save_path					)
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")
