


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_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = filter(lambda _UpperCamelCase	: p.requires_grad							,      model.parameters()	)
			__lowercase			       = sum([np.prod(p.size()	) for p in model_parameters]	)
			return params


a			:			Tuple												=					logging.getLogger(__name__)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			if metric == "rouge2":
						__lowercase			       = '''{val_avg_rouge2:.4f}-{step_count}'''
			elif metric == "bleu":
						__lowercase			       = '''{val_avg_bleu:.4f}-{step_count}'''
			elif metric == "em":
						__lowercase			       = '''{val_avg_em:.4f}-{step_count}'''
			elif metric == "loss":
						__lowercase			       = '''{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.'''	)

			__lowercase			       = ModelCheckpoint(
			    dirpath=_UpperCamelCase							,      filename=_UpperCamelCase							,      monitor=F'val_{metric}'							,      mode='''max'''							,      save_top_k=1							,      every_n_epochs=1							,      )

			return checkpoint_callback


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







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





class 						lowerCamelCase_							(					pl.Callback					):



					'''simple docstring'''







					def A   (					self			,							snake_case_			,							snake_case_    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
								pl_module.logger.log_metrics(snake_case_    )






					@rank_zero_only
					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_=True    ) ->      None:







								'''simple docstring'''

								logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'    )
								__lowercase			       = 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
								__lowercase			       = Path(pl_module.hparams.output_dir    )
								if type_path == "test":
											__lowercase			       = od / '''test_results.txt'''
											__lowercase			       = 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.
											__lowercase			       = od / F'{type_path}_results/{trainer.global_step:05d}.txt'
											__lowercase			       = od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
											results_file.parent.mkdir(exist_ok=snake_case_    )
											generations_file.parent.mkdir(exist_ok=snake_case_    )
								with open(snake_case_			,							'''a+'''    ) as writer:
											for key in sorted(snake_case_    ):
														if key in ["log", "progress_bar", "preds"]:
																	continue
														__lowercase			       = metrics[key]
														if isinstance(snake_case_			,							torch.Tensor    ):
																	__lowercase			       = val.item()
														__lowercase			       = F'{key}: {val:.6f}\n'
														writer.write(snake_case_    )

								if not save_generations:
											return

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






					@rank_zero_only
					def A   (					self			,							snake_case_			,							snake_case_    ) ->      Dict:







								'''simple docstring'''

								try:
											__lowercase			       = pl_module.model.model.num_parameters()
								except AttributeError:
											__lowercase			       = pl_module.model.num_parameters()

								__lowercase			       = count_trainable_parameters(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 A   (					self			,							snake_case_			,							snake_case_    ) ->      List[Any]:







								'''simple docstring'''

								save_json(pl_module.metrics			,							pl_module.metrics_save_path    )
								return self._write_logs(snake_case_			,							snake_case_			,							'''test'''    )






					@rank_zero_only
					def A   (					self			,							snake_case_			,							snake_case_    ) ->      List[str]:







								'''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")






import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='''%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s''',
    datefmt='''%Y-%m-%d %H:%M:%S''',
    level=os.environ.get('''LOGLEVEL''', '''INFO''').upper(),
    stream=sys.stdout,
)

a			:			Union[str, Any]												=					logging.getLogger(__name__)

a			:			Any												=					{'''facebook/bart-base''': BartForConditionalGeneration}
a			:			int												=					{'''facebook/bart-base''': BartTokenizer}


def 		lowercase_	(  ):


			'''simple docstring'''







			__lowercase			       = argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''	)
			parser.add_argument(
			    '''--validation_file'''							,      type=_UpperCamelCase							,      default=_UpperCamelCase							,      help='''A csv or a json file containing the validation data.'''	)
			parser.add_argument(
			    '''--max_length'''							,      type=_UpperCamelCase							,      default=5							,      help='''The maximum total input sequence length after tokenization.'''							,      )
			parser.add_argument(
			    '''--num_beams'''							,      type=_UpperCamelCase							,      default=_UpperCamelCase							,      help=(
			        '''Number of beams to use for evaluation. This argument will be '''
			        '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
			    )							,      )
			parser.add_argument(
			    '''--model_name_or_path'''							,      type=_UpperCamelCase							,      help='''Path to pretrained model or model identifier from huggingface.co/models.'''							,      required=_UpperCamelCase							,      )
			parser.add_argument(
			    '''--config_name'''							,      type=_UpperCamelCase							,      default=_UpperCamelCase							,      help='''Pretrained config name or path if not the same as model_name'''							,      )
			parser.add_argument(
			    '''--device'''							,      type=_UpperCamelCase							,      default='''cpu'''							,      help='''Device where the model will be run'''							,      )
			parser.add_argument('''--output_file_path'''							,      type=_UpperCamelCase							,      default=_UpperCamelCase							,      help='''Where to store the final ONNX file.'''	)

			__lowercase			       = parser.parse_args()

			return args


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase="cpu"	):


			'''simple docstring'''







			__lowercase			       = model_dict[model_name].from_pretrained(_UpperCamelCase	).to(_UpperCamelCase	)
			__lowercase			       = tokenizer_dict[model_name].from_pretrained(_UpperCamelCase	)

			if model_name in ["facebook/bart-base"]:
						__lowercase			       = 0
						__lowercase			       = None
						__lowercase			       = 0

			return huggingface_model, tokenizer


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			model.eval()

			__lowercase			       = None
			__lowercase			       = torch.jit.script(BARTBeamSearchGenerator(_UpperCamelCase	)	)

			with torch.no_grad():
						__lowercase			       = '''My friends are cool but they eat too many carbs.'''
						__lowercase			       = tokenizer([ARTICLE_TO_SUMMARIZE]							,      max_length=10_24							,      return_tensors='''pt'''	).to(model.device	)

						__lowercase			       = model.generate(
						    inputs['''input_ids''']							,      attention_mask=inputs['''attention_mask''']							,      num_beams=_UpperCamelCase							,      max_length=_UpperCamelCase							,      early_stopping=_UpperCamelCase							,      decoder_start_token_id=model.config.decoder_start_token_id							,      )

						torch.onnx.export(
						    _UpperCamelCase							,      (
						        inputs['''input_ids'''],
						        inputs['''attention_mask'''],
						        num_beams,
						        max_length,
						        model.config.decoder_start_token_id,
						    )							,      _UpperCamelCase							,      opset_version=14							,      input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id''']							,      output_names=['''output_ids''']							,      dynamic_axes={
						        '''input_ids''': {0: '''batch''', 1: '''seq'''},
						        '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
						    }							,      example_outputs=_UpperCamelCase							,      )

						logger.info('''Model exported to {}'''.format(_UpperCamelCase	)	)

						__lowercase			       = remove_dup_initializers(os.path.abspath(_UpperCamelCase	)	)

						logger.info('''Deduplicated and optimized model written to {}'''.format(_UpperCamelCase	)	)

						__lowercase			       = onnxruntime.InferenceSession(_UpperCamelCase	)
						__lowercase			       = ort_sess.run(
						    _UpperCamelCase							,      {
						        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
						        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
						        '''num_beams''': np.array(_UpperCamelCase	),
						        '''max_length''': np.array(_UpperCamelCase	),
						        '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id	),
						    }							,      )

						np.testing.assert_allclose(summary_ids.cpu().numpy()							,      ort_out[0]							,      rtol=1E-3							,      atol=1E-3	)

						logger.info('''Model outputs from torch and ONNX Runtime are similar.'''	)
						logger.info('''Success.'''	)


def 		lowercase_	(  ):


			'''simple docstring'''







			__lowercase			       = parse_args()
			__lowercase			       = 5
			__lowercase			       = 4

			# Make one log on every process with the configuration for debugging.
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''							,      datefmt='''%m/%d/%Y %H:%M:%S'''							,      level=logging.INFO							,      )

			logger.setLevel(logging.INFO	)
			transformers.utils.logging.set_verbosity_error()

			__lowercase			       = torch.device(args.device	)

			__lowercase       ,			__lowercase			       = load_model_tokenizer(args.model_name_or_path							,      _UpperCamelCase	)

			if model.config.decoder_start_token_id is None:
						raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''	)

			model.to(_UpperCamelCase	)

			if args.max_length:
						__lowercase			       = args.max_length

			if args.num_beams:
						__lowercase			       = args.num_beams

			if args.output_file_path:
						__lowercase			       = args.output_file_path
			else:
						__lowercase			       = '''BART.onnx'''

			logger.info('''Exporting model to ONNX'''	)
			export_and_validate_model(_UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	)


if __name__ == "__main__":
	main()


