




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 		lowercase__   (		__A:	List[str]				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							filter(lambda __A				: p.requires_grad							,model.parameters()				)
					__magic_name__							:	List[str]    							=							sum([np.prod(p.size()				) for p in model_parameters]				)
					return params


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







def 		lowercase__   (		__A:	int							,__A:	Optional[int]				):





					'''simple docstring'''






					if metric == "rouge2":
										__magic_name__							:	Tuple    							=							'''{val_avg_rouge2:.4f}-{step_count}'''
					elif metric == "bleu":
										__magic_name__							:	Union[str, Any]    							=							'''{val_avg_bleu:.4f}-{step_count}'''
					elif metric == "em":
										__magic_name__							:	Union[str, Any]    							=							'''{val_avg_em:.4f}-{step_count}'''
					elif metric == "loss":
										__magic_name__							:	List[str]    							=							'''{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.'''				)

					__magic_name__							:	Optional[int]    							=							ModelCheckpoint(
					    dirpath=__A							,filename=__A							,monitor=F'''val_{metric}'''							,mode='''max'''							,save_top_k=1							,every_n_epochs=1							,)

					return checkpoint_callback







def 		lowercase__   (		__A:	List[str]							,__A:	Dict				):





					'''simple docstring'''






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


class    lowerCamelCase    (		pl.Callback						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   Optional[Any]  )						->   Tuple:
								__magic_name__							:	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(lowerCamelCase_  )

			@rank_zero_only
			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   pl.Trainer       ,						lowerCamelCase_			:   pl.LightningModule       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   List[str]=True  )						->   None:
								logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''  )
								__magic_name__							:	Optional[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
								__magic_name__							:	List[Any]    							=							Path(pl_module.hparams.output_dir  )
								if type_path == "test":
													__magic_name__							:	Union[str, Any]    							=							od / '''test_results.txt'''
													__magic_name__							:	List[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.
													__magic_name__							:	Optional[int]    							=							od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
													__magic_name__							:	Optional[int]    							=							od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
													results_file.parent.mkdir(exist_ok=lowerCamelCase_  )
													generations_file.parent.mkdir(exist_ok=lowerCamelCase_  )
								with open(lowerCamelCase_       ,						'''a+'''  ) as writer:
													for key in sorted(lowerCamelCase_  ):
																		if key in ["log", "progress_bar", "preds"]:
																							continue
																		__magic_name__							:	Optional[int]    							=							metrics[key]
																		if isinstance(lowerCamelCase_       ,						torch.Tensor  ):
																							__magic_name__							:	List[Any]    							=							val.item()
																		__magic_name__							:	Union[str, Any]    							=							F'''{key}: {val:.6f}\n'''
																		writer.write(lowerCamelCase_  )

								if not save_generations:
													return

								if "preds" in metrics:
													__magic_name__							:	Optional[Any]    							=							'''\n'''.join(metrics['''preds''']  )
													generations_file.open('''w+'''  ).write(lowerCamelCase_  )

			@rank_zero_only
			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   Tuple       ,						lowerCamelCase_			:   Dict  )						->   int:
								try:
													__magic_name__							:	Any    							=							pl_module.model.model.num_parameters()
								except AttributeError:
													__magic_name__							:	List[Any]    							=							pl_module.model.num_parameters()

								__magic_name__							:	Union[str, Any]    							=							count_trainable_parameters(lowerCamelCase_  )
								# 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			:   Optional[Any]       ,						lowerCamelCase_			:   pl.Trainer       ,						lowerCamelCase_			:   pl.LightningModule  )						->   List[str]:
								save_json(pl_module.metrics       ,						pl_module.metrics_save_path  )
								return self._write_logs(lowerCamelCase_       ,						lowerCamelCase_       ,						'''test'''  )


			@rank_zero_only
			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   pl.Trainer       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								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.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=BlenderbotSmallTokenizer
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   str:
								super().setUp()

								__magic_name__							:	Union[str, Any]    							=							['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
								__magic_name__							:	Tuple    							=							dict(zip(lowerCamelCase_       ,						range(len(lowerCamelCase_  )  )  )  )

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

								__magic_name__							:	str    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''vocab_file''']  )
								__magic_name__							:	List[str]    							=							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(lowerCamelCase_  ) + '''\n'''  )
								with open(self.merges_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write('''\n'''.join(lowerCamelCase_  )  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   List[Any]:
								kwargs.update(self.special_tokens_map  )
								return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							'''adapt act apte'''
								__magic_name__							:	Dict    							=							'''adapt act apte'''
								return input_text, output_text

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Any:
								__magic_name__							:	int    							=							BlenderbotSmallTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map  )
								__magic_name__							:	str    							=							'''adapt act apte'''
								__magic_name__							:	Any    							=							['''adapt''', '''act''', '''ap@@''', '''te''']
								__magic_name__							:	List[str]    							=							tokenizer.tokenize(lowerCamelCase_  )
								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

								__magic_name__							:	List[Any]    							=							[0, 1, 2, 3, 4, 5]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_  )       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								__magic_name__							:	Any    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								assert tok('''sam'''  ).input_ids == [1384]
								__magic_name__							:	Dict    							=							'''I am a small frog.'''
								__magic_name__							:	Tuple    							=							tok([src_text]       ,						padding=lowerCamelCase_       ,						truncation=lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Tuple    							=							tok.batch_decode(lowerCamelCase_       ,						skip_special_tokens=lowerCamelCase_       ,						clean_up_tokenization_spaces=lowerCamelCase_  )[0]
								assert src_text != decoded  # I wish it did!
								assert decoded == "i am a small frog ."


			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Dict:
								__magic_name__							:	Tuple    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								__magic_name__							:	Any    							=							'''I am a small frog .'''
								__magic_name__							:	List[str]    							=							'''.'''
								__magic_name__							:	Tuple    							=							tok(lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Optional[Any]    							=							tok(lowerCamelCase_  )['''input_ids''']

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



