


'''simple docstring'''






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,
)

__UpperCAmelCase       							=				logging.getLogger(__name__)

__UpperCAmelCase       							=				{'''facebook/bart-base''': BartForConditionalGeneration}
__UpperCAmelCase       							=				{'''facebook/bart-base''': BartTokenizer}


def    _snake_case  (	)		->       Any:
							lowerCAmelCase__			  =      argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''			)
							parser.add_argument(
							    '''--validation_file'''					,       type=A					,       default=A					,       help='''A csv or a json file containing the validation data.'''			)
							parser.add_argument(
							    '''--max_length'''					,       type=A					,       default=5					,       help='''The maximum total input sequence length after tokenization.'''					,       )
							parser.add_argument(
							    '''--num_beams'''					,       type=A					,       default=A					,       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=A					,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,       required=A					,       )
							parser.add_argument(
							    '''--config_name'''					,       type=A					,       default=A					,       help='''Pretrained config name or path if not the same as model_name'''					,       )
							parser.add_argument(
							    '''--device'''					,       type=A					,       default='''cpu'''					,       help='''Device where the model will be run'''					,       )
							parser.add_argument('''--output_file_path'''					,       type=A					,       default=A					,       help='''Where to store the final ONNX file.'''			)

							lowerCAmelCase__			  =      parser.parse_args()

							return args


def    _snake_case  (	A					,       A="cpu"			)		->       int:
							lowerCAmelCase__			  =      model_dict[model_name].from_pretrained(A			).to(A			)
							lowerCAmelCase__			  =      tokenizer_dict[model_name].from_pretrained(A			)

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

							return huggingface_model, tokenizer


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       Union[str, Any]:
							model.eval()

							lowerCAmelCase__			  =      None
							lowerCAmelCase__			  =      torch.jit.script(BARTBeamSearchGenerator(A			)			)

							with torch.no_grad():
														lowerCAmelCase__			  =      '''My friends are cool but they eat too many carbs.'''
														lowerCAmelCase__			  =      tokenizer([ARTICLE_TO_SUMMARIZE]					,       max_length=1024					,       return_tensors='''pt'''			).to(model.device			)

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

														torch.onnx.export(
														    A					,       (
														        inputs['''input_ids'''],
														        inputs['''attention_mask'''],
														        num_beams,
														        max_length,
														        model.config.decoder_start_token_id,
														    )					,       A					,       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=A					,       )

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

														lowerCAmelCase__			  =      remove_dup_initializers(os.path.abspath(A			)			)

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

														lowerCAmelCase__			  =      onnxruntime.InferenceSession(A			)
														lowerCAmelCase__			  =      ort_sess.run(
														    A					,       {
														        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
														        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
														        '''num_beams''': np.array(A			),
														        '''max_length''': np.array(A			),
														        '''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    _snake_case  (	)		->       List[str]:
							lowerCAmelCase__			  =      parse_args()
							lowerCAmelCase__			  =      5
							lowerCAmelCase__			  =      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()

							lowerCAmelCase__			  =      torch.device(args.device			)

							lowerCAmelCase__  ,   lowerCAmelCase__			  =      load_model_tokenizer(args.model_name_or_path					,       A			)

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

							model.to(A			)

							if args.max_length:
														lowerCAmelCase__			  =      args.max_length

							if args.num_beams:
														lowerCAmelCase__			  =      args.num_beams

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

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


if __name__ == "__main__":
		main()



'''simple docstring'''


import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm
      import torch_xla.debug.metrics as met







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     def __init__(   self			,     *lowerCamelCase			,     lowerCamelCase=None			,     lowerCamelCase=None			,     **lowerCamelCase		):


          '''simple docstring'''





          super().__init__(*lowerCamelCase			,     **lowerCamelCase		)
          __A	:       Tuple      =							eval_examples
          __A	:       List[str]      =							post_process_function


     def lowerCAmelCase__  (   self			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase = "eval"		):


          '''simple docstring'''





          __A	:       Any      =							self.eval_dataset if eval_dataset is None else eval_dataset
          __A	:       Dict      =							self.get_eval_dataloader(lowerCamelCase		)
          __A	:       str      =							self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          __A	:       Any      =							self.compute_metrics
          __A	:       Optional[int]      =							None
          __A	:       Tuple      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          __A	:       Dict      =							time.time()
          try:
               __A	:       List[str]      =							eval_loop(
                   lowerCamelCase			,     description="Evaluation"			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=lowerCamelCase			,     metric_key_prefix=lowerCamelCase			,     )
          finally:
               __A	:       List[str]      =							compute_metrics
          __A	:       List[Any]      =							self.args.eval_batch_size * self.args.world_size
          if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
               start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
          output.metrics.update(
              speed_metrics(
                  lowerCamelCase			,     lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size		)			,     )		)
          if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
               # Only the main node write the results by default
               __A	:       List[Any]      =							self.post_process_function(lowerCamelCase			,     lowerCamelCase			,     output.predictions		)
               __A	:       List[str]      =							self.compute_metrics(lowerCamelCase		)

               # Prefix all keys with metric_key_prefix + '_'
               for key in list(metrics.keys()		):
                    if not key.startswith(f"{metric_key_prefix}_"		):
                         __A	:       Union[str, Any]      =							metrics.pop(lowerCamelCase		)
               metrics.update(output.metrics		)
          else:
               __A	:       Any      =							output.metrics

          if self.args.should_log:
               # Only the main node log the results by default
               self.log(lowerCamelCase		)

          if self.args.tpu_metrics_debug or self.args.debug:
               # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
               xm.master_print(met.metrics_report()		)

          __A	:       str      =							self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     lowerCamelCase		)
          return metrics


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase=None			,     lowerCamelCase = "test"		):


          '''simple docstring'''





          __A	:       Tuple      =							self.get_test_dataloader(lowerCamelCase		)

          # Temporarily disable metric computation, we will do it in the loop here.
          __A	:       Union[str, Any]      =							self.compute_metrics
          __A	:       Union[str, Any]      =							None
          __A	:       Optional[Any]      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          __A	:       Tuple      =							time.time()
          try:
               __A	:       List[str]      =							eval_loop(
                   lowerCamelCase			,     description="Prediction"			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=lowerCamelCase			,     metric_key_prefix=lowerCamelCase			,     )
          finally:
               __A	:       int      =							compute_metrics
          __A	:       List[Any]      =							self.args.eval_batch_size * self.args.world_size
          if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
               start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
          output.metrics.update(
              speed_metrics(
                  lowerCamelCase			,     lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size		)			,     )		)

          if self.post_process_function is None or self.compute_metrics is None:
               return output

          __A	:       Dict      =							self.post_process_function(lowerCamelCase			,     lowerCamelCase			,     output.predictions			,     "predict"		)
          __A	:       Tuple      =							self.compute_metrics(lowerCamelCase		)

          # Prefix all keys with metric_key_prefix + '_'
          for key in list(metrics.keys()		):
               if not key.startswith(f"{metric_key_prefix}_"		):
                    __A	:       Any      =							metrics.pop(lowerCamelCase		)
          metrics.update(output.metrics		)
          return PredictionOutput(predictions=predictions.predictions			,     label_ids=predictions.label_ids			,     metrics=lowerCamelCase		)

