



import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

UpperCAmelCase__     = logging.getLogger(__name__)







@dataclass
class     __lowerCAmelCase					:
			UpperCamelCase           =  field(
			    default=1_2_8       ,		metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={
			        '''help''': (
			            '''Whether to pad all samples to `max_seq_length`. '''
			            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
			            '''value if set.'''
			        )
			    }       ,		)







@dataclass
class     __lowerCAmelCase					:
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''}		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={'''help''': '''Train language if it is different from the evaluation language.'''}		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}       ,		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}       ,		)
			UpperCamelCase           =  field(
			    default='''main'''       ,		metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}       ,		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=__lowerCAmelCase       ,		metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}       ,		)





def 							A			(  )       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
			_UpperCAmelCase	 =     parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry('run_xnli'							,     _UpperCAmelCase					)

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,     datefmt='%m/%d/%Y %H:%M:%S'							,     handlers=[logging.StreamHandler(sys.stdout					)]							,     )

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			_UpperCAmelCase	 =     training_args.get_process_log_level()
			logger.setLevel(_UpperCAmelCase					)
			datasets.utils.logging.set_verbosity(_UpperCAmelCase					)
			transformers.utils.logging.set_verbosity(_UpperCAmelCase					)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
			    + F"distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}"					)
			logger.info(F"Training/evaluation parameters {training_args}"					)

			# Detecting last checkpoint.
			_UpperCAmelCase	 =     None
			if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
						_UpperCAmelCase	 =     get_last_checkpoint(training_args.output_dir					)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
									raise ValueError(
									    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
									    'Use --overwrite_output_dir to overcome.'					)
						elif last_checkpoint is not None:
									logger.info(
									    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
									    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'					)

									 # Set seed before initializing model.
			set_seed(training_args.seed					)

			# In distributed training, the load_dataset function guarantees that only one local process can concurrently
			# download the dataset.
			# Downloading and loading xnli dataset from the hub.
			if training_args.do_train:
						if model_args.train_language is None:
									_UpperCAmelCase	 =     load_dataset(
									    'xnli'							,     model_args.language							,     split='train'							,     cache_dir=model_args.cache_dir							,     use_auth_token=True if model_args.use_auth_token else None							,     )
						else:
									_UpperCAmelCase	 =     load_dataset(
									    'xnli'							,     model_args.train_language							,     split='train'							,     cache_dir=model_args.cache_dir							,     use_auth_token=True if model_args.use_auth_token else None							,     )
						_UpperCAmelCase	 =     train_dataset.features['''label'''].names

			if training_args.do_eval:
						_UpperCAmelCase	 =     load_dataset(
						    'xnli'							,     model_args.language							,     split='validation'							,     cache_dir=model_args.cache_dir							,     use_auth_token=True if model_args.use_auth_token else None							,     )
						_UpperCAmelCase	 =     eval_dataset.features['''label'''].names

			if training_args.do_predict:
						_UpperCAmelCase	 =     load_dataset(
						    'xnli'							,     model_args.language							,     split='test'							,     cache_dir=model_args.cache_dir							,     use_auth_token=True if model_args.use_auth_token else None							,     )
						_UpperCAmelCase	 =     predict_dataset.features['''label'''].names

			# Labels
			_UpperCAmelCase	 =     len(_UpperCAmelCase					)

			# Load pretrained model and tokenizer
			# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			_UpperCAmelCase	 =     AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,     num_labels=_UpperCAmelCase							,     idalabel={str(_UpperCAmelCase					): label for i, label in enumerate(_UpperCAmelCase					)}							,     labelaid={label: i for i, label in enumerate(_UpperCAmelCase					)}							,     finetuning_task='xnli'							,     cache_dir=model_args.cache_dir							,     revision=model_args.model_revision							,     use_auth_token=True if model_args.use_auth_token else None							,     )
			_UpperCAmelCase	 =     AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,     do_lower_case=model_args.do_lower_case							,     cache_dir=model_args.cache_dir							,     use_fast=model_args.use_fast_tokenizer							,     revision=model_args.model_revision							,     use_auth_token=True if model_args.use_auth_token else None							,     )
			_UpperCAmelCase	 =     AutoModelForSequenceClassification.from_pretrained(
			    model_args.model_name_or_path							,     from_tf=bool('.ckpt' in model_args.model_name_or_path					)							,     config=_UpperCAmelCase							,     cache_dir=model_args.cache_dir							,     revision=model_args.model_revision							,     use_auth_token=True if model_args.use_auth_token else None							,     ignore_mismatched_sizes=model_args.ignore_mismatched_sizes							,     )

			# Preprocessing the datasets
			# Padding strategy
			if data_args.pad_to_max_length:
						_UpperCAmelCase	 =     '''max_length'''
			else:
						# We will pad later, dynamically at batch creation, to the max sequence length in each batch
						_UpperCAmelCase	 =     False

			def preprocess_function(_UpperCAmelCase       :     Optional[int]					):
						# Tokenize the texts
						return tokenizer(
						    examples['premise']							,     examples['hypothesis']							,     padding=_UpperCAmelCase							,     max_length=data_args.max_seq_length							,     truncation=_UpperCAmelCase							,     )

			if training_args.do_train:
						if data_args.max_train_samples is not None:
									_UpperCAmelCase	 =     min(len(_UpperCAmelCase					)							,     data_args.max_train_samples					)
									_UpperCAmelCase	 =     train_dataset.select(range(_UpperCAmelCase					)					)
						with training_args.main_process_first(desc='train dataset map pre-processing'					):
									_UpperCAmelCase	 =     train_dataset.map(
									    _UpperCAmelCase							,     batched=_UpperCAmelCase							,     load_from_cache_file=not data_args.overwrite_cache							,     desc='Running tokenizer on train dataset'							,     )
						# Log a few random samples from the training set:
						for index in random.sample(range(len(_UpperCAmelCase					)					)							,     3					):
									logger.info(F"Sample {index} of the training set: {train_dataset[index]}."					)

			if training_args.do_eval:
						if data_args.max_eval_samples is not None:
									_UpperCAmelCase	 =     min(len(_UpperCAmelCase					)							,     data_args.max_eval_samples					)
									_UpperCAmelCase	 =     eval_dataset.select(range(_UpperCAmelCase					)					)
						with training_args.main_process_first(desc='validation dataset map pre-processing'					):
									_UpperCAmelCase	 =     eval_dataset.map(
									    _UpperCAmelCase							,     batched=_UpperCAmelCase							,     load_from_cache_file=not data_args.overwrite_cache							,     desc='Running tokenizer on validation dataset'							,     )

			if training_args.do_predict:
						if data_args.max_predict_samples is not None:
									_UpperCAmelCase	 =     min(len(_UpperCAmelCase					)							,     data_args.max_predict_samples					)
									_UpperCAmelCase	 =     predict_dataset.select(range(_UpperCAmelCase					)					)
						with training_args.main_process_first(desc='prediction dataset map pre-processing'					):
									_UpperCAmelCase	 =     predict_dataset.map(
									    _UpperCAmelCase							,     batched=_UpperCAmelCase							,     load_from_cache_file=not data_args.overwrite_cache							,     desc='Running tokenizer on prediction dataset'							,     )

									 # Get the metric function
			_UpperCAmelCase	 =     evaluate.load('xnli'					)

			# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
			# predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(_UpperCAmelCase       :     EvalPrediction					):
						_UpperCAmelCase	 =     p.predictions[0] if isinstance(p.predictions							,     _UpperCAmelCase					) else p.predictions
						_UpperCAmelCase	 =     np.argmax(_UpperCAmelCase							,     axis=1					)
						return metric.compute(predictions=_UpperCAmelCase							,     references=p.label_ids					)

			# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
			if data_args.pad_to_max_length:
						_UpperCAmelCase	 =     default_data_collator
			elif training_args.fpaa:
						_UpperCAmelCase	 =     DataCollatorWithPadding(_UpperCAmelCase							,     pad_to_multiple_of=8					)
			else:
						_UpperCAmelCase	 =     None

			# Initialize our Trainer
			_UpperCAmelCase	 =     Trainer(
			    model=_UpperCAmelCase							,     args=_UpperCAmelCase							,     train_dataset=train_dataset if training_args.do_train else None							,     eval_dataset=eval_dataset if training_args.do_eval else None							,     compute_metrics=_UpperCAmelCase							,     tokenizer=_UpperCAmelCase							,     data_collator=_UpperCAmelCase							,     )

			# Training
			if training_args.do_train:
						_UpperCAmelCase	 =     None
						if training_args.resume_from_checkpoint is not None:
									_UpperCAmelCase	 =     training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									_UpperCAmelCase	 =     last_checkpoint
						_UpperCAmelCase	 =     trainer.train(resume_from_checkpoint=_UpperCAmelCase					)
						_UpperCAmelCase	 =     train_result.metrics
						_UpperCAmelCase	 =     (
						    data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase					)
						)
						_UpperCAmelCase	 =     min(_UpperCAmelCase							,     len(_UpperCAmelCase					)					)

						trainer.save_model()  # Saves the tokenizer too for easy upload

						trainer.log_metrics('train'							,     _UpperCAmelCase					)
						trainer.save_metrics('train'							,     _UpperCAmelCase					)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						logger.info('*** Evaluate ***'					)
						_UpperCAmelCase	 =     trainer.evaluate(eval_dataset=_UpperCAmelCase					)

						_UpperCAmelCase	 =     data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase					)
						_UpperCAmelCase	 =     min(_UpperCAmelCase							,     len(_UpperCAmelCase					)					)

						trainer.log_metrics('eval'							,     _UpperCAmelCase					)
						trainer.save_metrics('eval'							,     _UpperCAmelCase					)

			# Prediction
			if training_args.do_predict:
						logger.info('*** Predict ***'					)
						_UpperCAmelCase	 =     trainer.predict(_UpperCAmelCase							,     metric_key_prefix='predict'					)

						_UpperCAmelCase	 =     (
						    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_UpperCAmelCase					)
						)
						_UpperCAmelCase	 =     min(_UpperCAmelCase							,     len(_UpperCAmelCase					)					)

						trainer.log_metrics('predict'							,     _UpperCAmelCase					)
						trainer.save_metrics('predict'							,     _UpperCAmelCase					)

						_UpperCAmelCase	 =     np.argmax(_UpperCAmelCase							,     axis=1					)
						_UpperCAmelCase	 =     os.path.join(training_args.output_dir							,     'predictions.txt'					)
						if trainer.is_world_process_zero():
									with open(_UpperCAmelCase							,     'w'					) as writer:
												writer.write('index\tprediction\n'					)
												for index, item in enumerate(_UpperCAmelCase					):
															_UpperCAmelCase	 =     label_list[item]
															writer.write(F"{index}\t{item}\n"					)


if __name__ == "__main__":
			main()





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")
