

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[Any]:
	__lowerCamelCase    :    Dict 	=			filter(lambda lowerCamelCase__      : p.requires_grad     ,			model.parameters()      )
	__lowerCamelCase    :    List[str] 	=			sum([np.prod(p.size()      ) for p in model_parameters]      )
	return params


a		=logging.getLogger(__name__)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	if metric == "rouge2":
		__lowerCamelCase    :    Any 	=			'{val_avg_rouge2:.4f}-{step_count}'
	elif metric == "bleu":
		__lowerCamelCase    :    Union[str, Any] 	=			'{val_avg_bleu:.4f}-{step_count}'
	elif metric == "em":
		__lowerCamelCase    :    Optional[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    :    List[str] 	=			ModelCheckpoint(
	    dirpath=lowerCamelCase__     ,			filename=lowerCamelCase__     ,			monitor=F"val_{metric}"     ,			mode='max'     ,			save_top_k=3     ,			every_n_epochs=1     ,			)
	return checkpoint_callback






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Dict:
	return EarlyStopping(
	    monitor=F"val_{metric}"     ,			mode='min' if 'loss' in metric else 'max'     ,			patience=lowerCamelCase__     ,			verbose=lowerCamelCase__     ,			)






class 	A_		(	pl.Callback    ):
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
								__lowerCamelCase    :    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(SCREAMING_SNAKE_CASE__)
							@rank_zero_only
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      pl.Trainer       ,SCREAMING_SNAKE_CASE__		:      pl.LightningModule       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=True):
								logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****")
								__lowerCamelCase    :    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    :    int 	=			Path(pl_module.hparams.output_dir)
								if type_path == "test":
									__lowerCamelCase    :    int 	=			od / 'test_results.txt'
									__lowerCamelCase    :    Optional[int] 	=			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    :    List[str] 	=			od / F"{type_path}_results/{trainer.global_step:05d}.txt"
									__lowerCamelCase    :    Tuple 	=			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    :    Dict 	=			metrics[key]
										if isinstance(SCREAMING_SNAKE_CASE__       ,torch.Tensor):
											__lowerCamelCase    :    List[Any] 	=			val.item()
										__lowerCamelCase    :    Tuple 	=			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    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Dict):
								try:
									__lowerCamelCase    :    Any 	=			pl_module.model.model.num_parameters()
								except AttributeError:
									__lowerCamelCase    :    Tuple 	=			pl_module.model.num_parameters()

								__lowerCamelCase    :    Optional[int] 	=			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    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      pl.Trainer       ,SCREAMING_SNAKE_CASE__		:      pl.LightningModule):
								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    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      pl.Trainer       ,SCREAMING_SNAKE_CASE__		:      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")







from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
					from transformers import (
					    AutoConfig,
					    BertConfig,
					    GPTaConfig,
					    TaConfig,
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					)
					from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
					from transformers import (
					    AutoModel,
					    AutoModelForCausalLM,
					    AutoModelForMaskedLM,
					    AutoModelForPreTraining,
					    AutoModelForQuestionAnswering,
					    AutoModelForSeqaSeqLM,
					    AutoModelForSequenceClassification,
					    AutoModelWithLMHead,
					    BertForMaskedLM,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    BertModel,
					    GPTaLMHeadModel,
					    RobertaForMaskedLM,
					    TaForConditionalGeneration,
					)






@is_pt_tf_cross_test
class 	A_		(	unittest.TestCase    ):
							@slow
							def    lowerCAmelCase					(		self		:      List[str]):
								# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["bert-base-uncased"]:
									__lowerCamelCase    :    Optional[int] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Tuple 	=			TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    str 	=			AutoModel.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["bert-base-uncased"]:
									__lowerCamelCase    :    str 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Tuple 	=			TFAutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    str 	=			AutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    Union[str, Any] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Union[str, Any] 	=			TFAutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			TFAutoModelForCausalLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    List[Any] 	=			AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			AutoModelForCausalLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      List[Any]):
								for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    List[str] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    List[str] 	=			TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[Any] 	=			AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      Tuple):
								for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    Optional[Any] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[int] 	=			TFAutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			TFAutoModelForMaskedLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    List[Any] 	=			AutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			AutoModelForMaskedLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      str):
								for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    Dict 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[Any] 	=			TFAutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			TFAutoModelForSeqaSeqLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Any 	=			AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			AutoModelForSeqaSeqLM.from_pretrained(
									    SCREAMING_SNAKE_CASE__       ,output_loading_info=SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["bert-base-uncased"]:
									__lowerCamelCase    :    List[Any] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[int] 	=			TFAutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    int 	=			AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      Any):
								# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["bert-base-uncased"]:
									__lowerCamelCase    :    List[Any] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    str 	=			TFAutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[int] 	=			AutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    List[Any] 	=			TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
								self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								self.assertEqual(model.num_parameters()       ,1_4_4_1_0)
								self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE__)       ,1_4_4_1_0)

								__lowerCamelCase    :    Union[str, Any] 	=			AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
								self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								self.assertEqual(model.num_parameters()       ,1_4_4_1_0)
								self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE__)       ,1_4_4_1_0)


							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    str 	=			TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_pt=SCREAMING_SNAKE_CASE__)
								self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								self.assertEqual(model.num_parameters()       ,1_4_4_1_0)
								self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE__)       ,1_4_4_1_0)

								__lowerCamelCase    :    Optional[int] 	=			AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE__       ,from_tf=SCREAMING_SNAKE_CASE__)
								self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								self.assertEqual(model.num_parameters()       ,1_4_4_1_0)
								self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE__)       ,1_4_4_1_0)




