






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	( lowercase			)       ->  List[str]:
		'''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 :			Tuple              =	logging.getLogger(__name__)


def 				A	( lowercase   ,	lowercase			)       ->  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 == "em":
				UpperCamelCase          =					'{val_avg_em:.4f}-{step_count}'
		elif metric == "loss":
				UpperCamelCase          =					'{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          =					ModelCheckpoint(
		    dirpath=lowercase   ,	filename=lowercase   ,	monitor=f'''val_{metric}'''   ,	mode='max'   ,	save_top_k=1   ,	every_n_epochs=1   ,	)

		return checkpoint_callback


def 				A	( lowercase   ,	lowercase			)       ->  int:
		'''simple docstring'''

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


class     lowercase (		pl.Callback       ):



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Optional[int]:
				"""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_	)  -> Union[str, 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")


import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Any     					=    CTRLTokenizer
		__lowercase	:	Any     					=    False
		__lowercase	:	Union[str, Any]     					=    False



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				UpperCamelCase          =					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
				UpperCamelCase          =					dict(zip(A_    ,				range(len(A_	)	)	)	)
				UpperCamelCase          =					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
				UpperCamelCase          =					{'unk_token': '<unk>'}

				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file']	)
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file']	)
				with open(self.vocab_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write(json.dumps(A_	) + '\n'	)
				with open(self.merges_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write('\n'.join(A_	)	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> List[Any]:
				"""simple docstring"""
				kwargs.update(self.special_tokens_map	)
				return CTRLTokenizer.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt react readapt apt'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					CTRLTokenizer(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map	)
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
				UpperCamelCase          =					tokenizer.tokenize(A_	)
				self.assertListEqual(A_    ,				A_	)

				UpperCamelCase          =					tokens + [tokenizer.unk_token]

				UpperCamelCase          =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)    ,				A_	)
