



import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin







class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  BlenderbotSmallTokenizer
			UpperCamelCase           =  False



			def 					_lowerCamelCase (		self     :						List[str])     ->					int:



						"""simple docstring"""






						super().setUp()

						_UpperCAmelCase	 =     ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
						_UpperCAmelCase	 =     dict(zip(__A				,	range(len(__A))))

						_UpperCAmelCase	 =     ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
						_UpperCAmelCase	 =     {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

						_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 					_lowerCamelCase (		self     :						str				,	**A     :						Optional[Any])     ->					Dict:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname				,	**__A)



			def 					_lowerCamelCase (		self     :						str				,	A     :						List[str])     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     'adapt act apte'
						_UpperCAmelCase	 =     'adapt act apte'
						return input_text, output_text



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     BlenderbotSmallTokenizer(self.vocab_file				,	self.merges_file				,	**self.special_tokens_map)
						_UpperCAmelCase	 =     'adapt act apte'
						_UpperCAmelCase	 =     ['adapt', 'act', 'ap@@', 'te']
						_UpperCAmelCase	 =     tokenizer.tokenize(__A)
						self.assertListEqual(__A				,	__A)

						_UpperCAmelCase	 =     [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

						_UpperCAmelCase	 =     [0, 1, 2, 3, 4, 5]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__A)				,	__A)



			def 					_lowerCamelCase (		self     :						Any)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M')
						assert tok('sam').input_ids == [13_84]
						_UpperCAmelCase	 =     'I am a small frog.'
						_UpperCAmelCase	 =     tok([src_text]				,	padding=__A				,	truncation=__A)['input_ids']
						_UpperCAmelCase	 =     tok.batch_decode(__A				,	skip_special_tokens=__A				,	clean_up_tokenization_spaces=__A)[0]
						assert src_text != decoded  # I wish it did!
						assert decoded == "i am a small frog ."



			def 					_lowerCamelCase (		self     :						Any)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M')
						_UpperCAmelCase	 =     'I am a small frog .'
						_UpperCAmelCase	 =     '.'
						_UpperCAmelCase	 =     tok(__A)['input_ids']
						_UpperCAmelCase	 =     tok(__A)['input_ids']

						assert encoded[-1] == encoded_dot[0]





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			(  _UpperCAmelCase       :     List[Any]					)       ->				int:







			'''simple docstring'''
			_UpperCAmelCase	 =     filter(lambda _UpperCAmelCase					: p.requires_grad							,     model.parameters()					)
			_UpperCAmelCase	 =     sum([np.prod(p.size()					) for p in model_parameters]					)
			return params


UpperCAmelCase__     = logging.getLogger(__name__)





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				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=_UpperCAmelCase							,     filename=_UpperCAmelCase							,     monitor=F"val_{metric}"							,     mode='max'							,     save_top_k=1							,     every_n_epochs=1							,     )

			return checkpoint_callback





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     int					)       ->				str:







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







class     __lowerCAmelCase					(    pl.Callback		):



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						List[Any]				,	A     :						int)     ->					Dict:



						"""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 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						pl.Trainer				,	A     :						pl.LightningModule				,	A     :						str				,	A     :						int=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 					_lowerCamelCase (		self     :						str				,	A     :						Optional[int]				,	A     :						List[str])     ->					Optional[Any]:



						"""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 					_lowerCamelCase (		self     :						Dict				,	A     :						pl.Trainer				,	A     :						pl.LightningModule)     ->					int:



						"""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     :						Tuple				,	A     :						pl.Trainer				,	A     :						str)     ->					Dict:



						"""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")
