

from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

lowerCamelCase						=		_symbol_database.Default()


lowerCamelCase						=		_descriptor_pool.Default().AddSerializedFile(
    B'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)

lowerCamelCase						=		globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
		lowerCamelCase						=		None
		lowerCamelCase						=		B'''H\003'''
		# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
		# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
		# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
		# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
		# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
		lowerCamelCase						=		45
		lowerCamelCase						=		1581
		lowerCamelCase						=		1517
		lowerCamelCase						=		1570
		lowerCamelCase						=		1584
		lowerCamelCase						=		1793
		lowerCamelCase						=		1795
		lowerCamelCase						=		1916
		lowerCamelCase						=		1864
		lowerCamelCase						=		1905
		lowerCamelCase						=		1919
		lowerCamelCase						=		2429
		lowerCamelCase						=		2208
		lowerCamelCase						=		2418
		lowerCamelCase						=		2323
		lowerCamelCase						=		2407
# @@protoc_insertion_point(module_scope)








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_   (	_a	):




		"""simple docstring"""






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


lowerCamelCase						=		logging.getLogger(__name__)






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		if metric == "rouge2":
				lowerCAmelCase__       :      Optional[int]   					= '''{val_avg_rouge2:.4f}-{step_count}'''
		elif metric == "bleu":
				lowerCAmelCase__       :      Optional[int]   					= '''{val_avg_bleu:.4f}-{step_count}'''
		elif metric == "em":
				lowerCAmelCase__       :      List[Any]   					= '''{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.'''	)

		lowerCAmelCase__       :      Dict   					= ModelCheckpoint(
		    dirpath=_a     ,       filename=_a     ,       monitor=f'val_{metric}'     ,       mode='''max'''     ,       save_top_k=3     ,       every_n_epochs=1     ,       )
		return checkpoint_callback






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






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





class  _a							(    pl.Callback):




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Any		)->	Optional[int]:
			lowerCAmelCase__       :      Dict   					= {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
			pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE		)




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

			if not save_generations:
					return

			if "preds" in metrics:
					lowerCAmelCase__       :      Dict   					= '''\n'''.join(metrics['''preds''']		)
					generations_file.open('''w+'''		).write(_SCREAMING_SNAKE_CASE		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Optional[int]:
			try:
					lowerCAmelCase__       :      Tuple   					= pl_module.model.model.num_parameters()
			except AttributeError:
					lowerCAmelCase__       :      Optional[Any]   					= pl_module.model.num_parameters()

			lowerCAmelCase__       :      Dict   					= count_trainable_parameters(_SCREAMING_SNAKE_CASE		)
			# 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						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		pl.Trainer		, _SCREAMING_SNAKE_CASE						:		pl.LightningModule		)->	Optional[Any]:
			save_json(pl_module.metrics		, pl_module.metrics_save_path		)
			return self._write_logs(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, '''test'''		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		pl.Trainer		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	List[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")




