






"""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     :		Dict											=					logging.getLogger(__name__)

_UpperCamelCase     :		int											=					{"facebook/bart-base": BartForConditionalGeneration}
_UpperCamelCase     :		List[str]											=					{"facebook/bart-base": BartTokenizer}





def        a_  (   ):





       '''simple docstring'''

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

       lowercase__    :	Optional[Any]     					=			parser.parse_args()

       return args





def        a_  (   _lowerCAmelCase						:				List[str]     ,				_lowerCAmelCase						:				Optional[Any]="cpu"  ):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			model_dict[model_name].from_pretrained(__lowerCAmelCase  ).to(__lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			tokenizer_dict[model_name].from_pretrained(__lowerCAmelCase  )

       if model_name in ["facebook/bart-base"]:
              lowercase__    :	Optional[Any]     					=			0
              lowercase__    :	Optional[Any]     					=			None
              lowercase__    :	Optional[int]     					=			0

       return huggingface_model, tokenizer





def        a_  (   _lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				int  ):





       '''simple docstring'''

       model.eval()

       lowercase__    :	Dict     					=			None
       lowercase__    :	str     					=			torch.jit.script(BARTBeamSearchGenerator(__lowerCAmelCase  )  )

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

              lowercase__    :	int     					=			model.generate(
                  inputs['input_ids']     ,				attention_mask=inputs['attention_mask']     ,				num_beams=__lowerCAmelCase     ,				max_length=__lowerCAmelCase     ,				early_stopping=__lowerCAmelCase     ,				decoder_start_token_id=model.config.decoder_start_token_id     ,				)

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

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

              lowercase__    :	Optional[int]     					=			remove_dup_initializers(os.path.abspath(__lowerCAmelCase  )  )

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

              lowercase__    :	Dict     					=			onnxruntime.InferenceSession(__lowerCAmelCase  )
              lowercase__    :	str     					=			ort_sess.run(
                  __lowerCAmelCase     ,				{
                      'input_ids': inputs['input_ids'].cpu().numpy(),
                      'attention_mask': inputs['attention_mask'].cpu().numpy(),
                      'num_beams': np.array(__lowerCAmelCase  ),
                      'max_length': np.array(__lowerCAmelCase  ),
                      '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        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Union[str, Any]     					=			parse_args()
       lowercase__    :	List[Any]     					=			5
       lowercase__    :	Dict     					=			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__    :	Tuple     					=			torch.device(args.device  )

       lowercase__    :	List[str]     					=			load_model_tokenizer(args.model_name_or_path     ,				__lowerCAmelCase  )

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

       model.to(__lowerCAmelCase  )

       if args.max_length:
              lowercase__    :	Dict     					=			args.max_length

       if args.num_beams:
              lowercase__    :	Union[str, Any]     					=			args.num_beams

       if args.output_file_path:
              lowercase__    :	List[Any]     					=			args.output_file_path
       else:
              lowercase__    :	Optional[Any]     					=			"""BART.onnx"""

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


if __name__ == "__main__":
       main()


"""simple docstring"""





import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
           DistilBertForMaskedLM,
           DistilBertForMultipleChoice,
           DistilBertForQuestionAnswering,
           DistilBertForSequenceClassification,
           DistilBertForTokenClassification,
           DistilBertModel,
       )


class   UpperCAmelCase_			(					_a):


      def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=True	,    a=True	,    a=False	,    a=True	,    a=9_9	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=1_6	,    a=2	,    a=0.02	,    a=3	,    a=4	,    a=None	,    ) ->      Any:
             lowercase__    :	Tuple     					=			parent
             lowercase__    :	List[Any]     					=			batch_size
             lowercase__    :	List[Any]     					=			seq_length
             lowercase__    :	List[Any]     					=			is_training
             lowercase__    :	Optional[Any]     					=			use_input_mask
             lowercase__    :	Optional[int]     					=			use_token_type_ids
             lowercase__    :	int     					=			use_labels
             lowercase__    :	Tuple     					=			vocab_size
             lowercase__    :	int     					=			hidden_size
             lowercase__    :	Any     					=			num_hidden_layers
             lowercase__    :	List[str]     					=			num_attention_heads
             lowercase__    :	Optional[Any]     					=			intermediate_size
             lowercase__    :	Optional[Any]     					=			hidden_act
             lowercase__    :	List[str]     					=			hidden_dropout_prob
             lowercase__    :	List[Any]     					=			attention_probs_dropout_prob
             lowercase__    :	List[Any]     					=			max_position_embeddings
             lowercase__    :	List[str]     					=			type_vocab_size
             lowercase__    :	Tuple     					=			type_sequence_label_size
             lowercase__    :	List[Any]     					=			initializer_range
             lowercase__    :	str     					=			num_labels
             lowercase__    :	Tuple     					=			num_choices
             lowercase__    :	str     					=			scope


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

             lowercase__    :	str     					=			None
             if self.use_input_mask:
                    lowercase__    :	Any     					=			random_attention_mask([self.batch_size, self.seq_length]							)

             lowercase__    :	Dict     					=			None
             lowercase__    :	Optional[Any]     					=			None
             lowercase__    :	int     					=			None
             if self.use_labels:
                    lowercase__    :	Union[str, Any]     					=			ids_tensor([self.batch_size]	,    self.type_sequence_label_size							)
                    lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels							)
                    lowercase__    :	Dict     					=			ids_tensor([self.batch_size]	,    self.num_choices							)

             lowercase__    :	List[Any]     					=			self.get_config()

             return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             return DistilBertConfig(
                 vocab_size=self.vocab_size	,    dim=self.hidden_size	,    n_layers=self.num_hidden_layers	,    n_heads=self.num_attention_heads	,    hidden_dim=self.intermediate_size	,    hidden_act=self.hidden_act	,    dropout=self.hidden_dropout_prob	,    attention_dropout=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    initializer_range=self.initializer_range	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Tuple     					=			DistilBertModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Any     					=			model(a	,    a							)
             lowercase__    :	str     					=			model(a							)
             self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Optional[int]     					=			DistilBertForMaskedLM(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Union[str, Any]     					=			model(a	,    attention_mask=a	,    labels=a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      int:
             lowercase__    :	Tuple     					=			DistilBertForQuestionAnswering(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Tuple     					=			model(
                 a	,    attention_mask=a	,    start_positions=a	,    end_positions=a							)
             self.parent.assertEqual(result.start_logits.shape	,    (self.batch_size, self.seq_length)							)
             self.parent.assertEqual(result.end_logits.shape	,    (self.batch_size, self.seq_length)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      List[str]:
             lowercase__    :	int     					=			self.num_labels
             lowercase__    :	Dict     					=			DistilBertForSequenceClassification(a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Optional[Any]     					=			model(a	,    attention_mask=a	,    labels=a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      Any:
             lowercase__    :	Any     					=			self.num_labels
             lowercase__    :	List[str]     					=			DistilBertForTokenClassification(config=a							)
             model.to(a							)
             model.eval()

             lowercase__    :	Any     					=			model(a	,    attention_mask=a	,    labels=a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.num_labels)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      Tuple:
             lowercase__    :	List[Any]     					=			self.num_choices
             lowercase__    :	Any     					=			DistilBertForMultipleChoice(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	str     					=			input_ids.unsqueeze(1							).expand(-1	,    self.num_choices	,    -1							).contiguous()
             lowercase__    :	Optional[int]     					=			input_mask.unsqueeze(1							).expand(-1	,    self.num_choices	,    -1							).contiguous()
             lowercase__    :	int     					=			model(
                 a	,    attention_mask=a	,    labels=a	,    )
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_choices)							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Union[str, Any]     					=			self.prepare_config_and_inputs()
             ((lowercase__)					,   (lowercase__)					,   (lowercase__)					,   (lowercase__)					,   (lowercase__)					,   (lowercase__))    :	List[str]     					=			config_and_inputs
             lowercase__    :	Optional[Any]     					=			{'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					List[str]										=						(
          (
              DistilBertModel,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
          )
          if is_torch_available()
          else None
      )
      lowerCamelCase__	:					str										=						(
          {
              "feature-extraction": DistilBertModel,
              "fill-mask": DistilBertForMaskedLM,
              "question-answering": DistilBertForQuestionAnswering,
              "text-classification": DistilBertForSequenceClassification,
              "token-classification": DistilBertForTokenClassification,
              "zero-shot": DistilBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCamelCase__	:					Optional[int]										=						True
      lowerCamelCase__	:					Any										=						True
      lowerCamelCase__	:					List[Any]										=						True
      lowerCamelCase__	:					Optional[Any]										=						True


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	str     					=			DistilBertModelTester(self							)
             lowercase__    :	int     					=			ConfigTester(self	,    config_class=a	,    dim=3_7							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             self.config_tester.run_common_tests()


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	int     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_model(*a							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	int     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_for_masked_lm(*a							)


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	str     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_for_question_answering(*a							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	List[str]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_for_sequence_classification(*a							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_for_token_classification(*a							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_for_multiple_choice(*a							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__    :	str     					=			DistilBertModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)


      @slow
      @require_torch_gpu
      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__					,   lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                    # BertForMultipleChoice behaves incorrectly in JIT environments.
                    if model_class == DistilBertForMultipleChoice:
                           return

                    lowercase__    :	Optional[int]     					=			True
                    lowercase__    :	Union[str, Any]     					=			model_class(config=a							)

                    lowercase__    :	int     					=			self._prepare_for_class(a	,    a							)
                    lowercase__    :	Tuple     					=			torch.jit.trace(
                        a	,    (inputs_dict['input_ids'].to('cpu'							), inputs_dict['attention_mask'].to('cpu'							))							)

                    with tempfile.TemporaryDirectory() as tmp:
                           torch.jit.save(a	,    os.path.join(a	,    'traced_model.pt'							)							)
                           lowercase__    :	Optional[int]     					=			torch.jit.load(os.path.join(a	,    'traced_model.pt'							)	,    map_location=a							)
                           loaded(inputs_dict['input_ids'].to(a							)	,    inputs_dict['attention_mask'].to(a							)							)





@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	int     					=			DistilBertModel.from_pretrained('distilbert-base-uncased'							)
             lowercase__    :	Union[str, Any]     					=			torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
             lowercase__    :	Optional[Any]     					=			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
             with torch.no_grad():
                    lowercase__    :	Optional[Any]     					=			model(a	,    attention_mask=a							)[0]
             lowercase__    :	Tuple     					=			torch.Size((1, 1_1, 7_6_8)							)
             self.assertEqual(output.shape	,    a							)
             lowercase__    :	List[Any]     					=			torch.tensor(
                 [[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]]							)

             self.assertTrue(torch.allclose(output[:, 1:4, 1:4]	,    a	,    atol=1e-4							)							)

