'''simple docstring'''





import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import BertGenerationDecoder, BertGenerationEncoder




class 	__A				:



    def __init__(self   :   Union[str, Any]       ,      __a   :   Any       ,      __a   :   List[Any]=13       ,      __a   :   Optional[Any]=7       ,      __a   :   List[Any]=True       ,      __a   :   Optional[Any]=True       ,      __a   :   Any=99       ,      __a   :   List[Any]=32       ,      __a   :   Optional[Any]=5       ,      __a   :   Optional[int]=4       ,      __a   :   Tuple=37       ,      __a   :   int="gelu"       ,      __a   :   Union[str, Any]=0.1       ,      __a   :   str=0.1       ,      __a   :   str=50       ,      __a   :   List[Any]=0.02       ,      __a   :   str=True       ,      __a   :   Tuple=None       ,      ):
         UpperCAmelCase_	   =       parent
         UpperCAmelCase_	   =       batch_size
         UpperCAmelCase_	   =       seq_length
         UpperCAmelCase_	   =       is_training
         UpperCAmelCase_	   =       use_input_mask
         UpperCAmelCase_	   =       vocab_size
         UpperCAmelCase_	   =       hidden_size
         UpperCAmelCase_	   =       num_hidden_layers
         UpperCAmelCase_	   =       num_attention_heads
         UpperCAmelCase_	   =       intermediate_size
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       hidden_dropout_prob
         UpperCAmelCase_	   =       attention_probs_dropout_prob
         UpperCAmelCase_	   =       max_position_embeddings
         UpperCAmelCase_	   =       initializer_range
         UpperCAmelCase_	   =       use_labels
         UpperCAmelCase_	   =       scope



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size			)

         UpperCAmelCase_	   =       None
         if self.use_input_mask:
              UpperCAmelCase_	   =       random_attention_mask([self.batch_size, self.seq_length]			)

         if self.use_labels:
              UpperCAmelCase_	   =       ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size			)

         UpperCAmelCase_	   =       self.get_config()

         return config, input_ids, input_mask, token_labels



    def  _lowercase						(self   :   List[Any]			):
         return BertGenerationConfig(
             vocab_size=self.vocab_size       ,      hidden_size=self.hidden_size       ,      num_hidden_layers=self.num_hidden_layers       ,      num_attention_heads=self.num_attention_heads       ,      intermediate_size=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      hidden_dropout_prob=self.hidden_dropout_prob       ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      is_decoder=__a       ,      initializer_range=self.initializer_range       ,      )



    def  _lowercase						(self   :   Any			):
         (
             (
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			
         )	   =       self.prepare_config_and_inputs()

         UpperCAmelCase_	   =       True
         UpperCAmelCase_	   =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
         UpperCAmelCase_	   =       ids_tensor([self.batch_size, self.seq_length]       ,      vocab_size=2			)

         return (
             config,
             input_ids,
             input_mask,
             token_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[int]       ,      __a   :   Any       ,      __a   :   Any       ,      __a   :   Tuple       ,      **__a   :   Any       ,      ):
         UpperCAmelCase_	   =       BertGenerationEncoder(config=__a			)
         model.to(__a			)
         model.eval()
         UpperCAmelCase_	   =       model(__a       ,      attention_mask=__a			)
         UpperCAmelCase_	   =       model(__a			)
         self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)			)



    def  _lowercase						(self   :   Dict       ,      __a   :   Tuple       ,      __a   :   Optional[int]       ,      __a   :   Any       ,      __a   :   Optional[Any]       ,      __a   :   Optional[int]       ,      __a   :   int       ,      **__a   :   List[str]       ,      ):
         UpperCAmelCase_	   =       True
         UpperCAmelCase_	   =       BertGenerationEncoder(config=__a			)
         model.to(__a			)
         model.eval()
         UpperCAmelCase_	   =       model(
             __a       ,      attention_mask=__a       ,      encoder_hidden_states=__a       ,      encoder_attention_mask=__a       ,      )
         UpperCAmelCase_	   =       model(
             __a       ,      attention_mask=__a       ,      encoder_hidden_states=__a       ,      )
         self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)			)



    def  _lowercase						(self   :   Any       ,      __a   :   Any       ,      __a   :   List[Any]       ,      __a   :   Union[str, Any]       ,      __a   :   Union[str, Any]       ,      __a   :   List[str]       ,      __a   :   Any       ,      **__a   :   Optional[Any]       ,      ):
         UpperCAmelCase_	   =       True
         UpperCAmelCase_	   =       True
         UpperCAmelCase_	   =       BertGenerationDecoder(config=__a			).to(__a			).eval()

         # first forward pass
         UpperCAmelCase_	   =       model(
             __a       ,      attention_mask=__a       ,      encoder_hidden_states=__a       ,      encoder_attention_mask=__a       ,      use_cache=__a       ,      )
         UpperCAmelCase_	   =       outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         UpperCAmelCase_	   =       ids_tensor((self.batch_size, 3)       ,      config.vocab_size			)
         UpperCAmelCase_	   =       ids_tensor((self.batch_size, 3)       ,      vocab_size=2			)

         # append to next input_ids and
         UpperCAmelCase_	   =       torch.cat([input_ids, next_tokens]       ,      dim=-1			)
         UpperCAmelCase_	   =       torch.cat([input_mask, next_mask]       ,      dim=-1			)

         UpperCAmelCase_	   =       model(
             __a       ,      attention_mask=__a       ,      encoder_hidden_states=__a       ,      encoder_attention_mask=__a       ,      output_hidden_states=__a       ,      )["hidden_states"][0]
         UpperCAmelCase_	   =       model(
             __a       ,      attention_mask=__a       ,      encoder_hidden_states=__a       ,      encoder_attention_mask=__a       ,      past_key_values=__a       ,      output_hidden_states=__a       ,      )["hidden_states"][0]

         # select random slice
         UpperCAmelCase_	   =       ids_tensor((1,)       ,      output_from_past.shape[-1]			).item()
         UpperCAmelCase_	   =       output_from_no_past[:, -3:, random_slice_idx].detach()
         UpperCAmelCase_	   =       output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]			)

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(__a       ,      __a       ,      atol=1E-3			)			)



    def  _lowercase						(self   :   Any       ,      __a   :   List[str]       ,      __a   :   Any       ,      __a   :   List[str]       ,      __a   :   Optional[int]       ,      *__a   :   Optional[Any]       ,      ):
         UpperCAmelCase_	   =       BertGenerationDecoder(__a			)
         model.to(__a			)
         model.eval()
         UpperCAmelCase_	   =       model(__a       ,      attention_mask=__a       ,      labels=__a			)
         self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.vocab_size)			)






    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.prepare_config_and_inputs()
         UpperCAmelCase_	   =       {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict




@require_torch
class 	__A				(			UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						int	       =  (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
    a__	:						Any	       =  (BertGenerationDecoder,) if is_torch_available() else ()
    a__	:						List[Any]	       =  (
        {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
        if is_torch_available()
        else {}
    )



    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_	   =       BertGenerationEncoderTester(self			)
         UpperCAmelCase_	   =       ConfigTester(self       ,      config_class=__a       ,      hidden_size=37			)



    def  _lowercase						(self   :   int			):
         self.config_tester.run_common_tests()



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a			)



    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs()
         UpperCAmelCase_	   =       "bert"
         self.model_tester.create_and_check_model(__a       ,      __a       ,      __a       ,      __a			)



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*__a			)



    def  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*__a			)



    def  _lowercase						(self   :   int			):
         # This regression test was failing with PyTorch < 1.3
         (
             (
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			(
             UpperCAmelCase_
         )   ,			
         )	   =       self.model_tester.prepare_config_and_inputs_for_decoder()

         UpperCAmelCase_	   =       None

         self.model_tester.create_and_check_model_as_decoder(
             __a       ,      __a       ,      __a       ,      __a       ,      __a       ,      __a       ,      )



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_for_causal_lm(*__a			)






    @slow
    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"			)
         self.assertIsNotNone(__a			)




@require_torch
class 	__A				(			unittest.TestCase     ):



    @slow
    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"			)
         UpperCAmelCase_	   =       torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]			)
         with torch.no_grad():
              UpperCAmelCase_	   =       model(__a			)[0]
         UpperCAmelCase_	   =       torch.Size([1, 8, 1024]			)
         self.assertEqual(output.shape       ,      __a			)
         UpperCAmelCase_	   =       torch.tensor(
             [[[0.17_75, 0.00_83, -0.03_21], [1.60_02, 0.12_87, 0.39_12], [2.14_73, 0.57_91, 0.60_66]]]			)
         self.assertTrue(torch.allclose(output[:, :3, :3]       ,      __a       ,      atol=1E-4			)			)




@require_torch
class 	__A				(			unittest.TestCase     ):



    @slow
    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"			)
         UpperCAmelCase_	   =       torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]			)
         with torch.no_grad():
              UpperCAmelCase_	   =       model(__a			)[0]
         UpperCAmelCase_	   =       torch.Size([1, 8, 50358]			)
         self.assertEqual(output.shape       ,      __a			)
         UpperCAmelCase_	   =       torch.tensor(
             [[[-0.57_88, -2.59_94, -3.70_54], [0.04_38, 4.79_97, 1.87_95], [1.58_62, 6.64_09, 4.46_38]]]			)
         self.assertTrue(torch.allclose(output[:, :3, :3]       ,      __a       ,      atol=1E-4			)			)





'''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,
)

SCREAMING_SNAKE_CASE_:					Tuple  =logging.getLogger(__name__)

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





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




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

     UpperCAmelCase_	   =       parser.parse_args()

     return args





def      lowerCAmelCase_    ( snake_case_	:			Optional[Any] ,   snake_case_	:			int="cpu"      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       model_dict[model_name].from_pretrained(snake_case_      ).to(snake_case_      )
     UpperCAmelCase_	   =       tokenizer_dict[model_name].from_pretrained(snake_case_      )

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

     return huggingface_model, tokenizer





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			List[Any] ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			int ,   snake_case_	:			Dict      )   ->				Dict:


     '''simple docstring'''




     model.eval()

     UpperCAmelCase_	   =       None
     UpperCAmelCase_	   =       torch.jit.script(BARTBeamSearchGenerator(snake_case_      )      )

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

          UpperCAmelCase_	   =       model.generate(
              inputs["input_ids"] ,   attention_mask=inputs["attention_mask"] ,   num_beams=snake_case_ ,   max_length=snake_case_ ,   early_stopping=snake_case_ ,   decoder_start_token_id=model.config.decoder_start_token_id ,   )

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

          logger.info("Model exported to {}".format(snake_case_      )      )

          UpperCAmelCase_	   =       remove_dup_initializers(os.path.abspath(snake_case_      )      )

          logger.info("Deduplicated and optimized model written to {}".format(snake_case_      )      )

          UpperCAmelCase_	   =       onnxruntime.InferenceSession(snake_case_      )
          UpperCAmelCase_	   =       ort_sess.run(
              snake_case_ ,   {
                  "input_ids": inputs["input_ids"].cpu().numpy(),
                  "attention_mask": inputs["attention_mask"].cpu().numpy(),
                  "num_beams": np.array(snake_case_      ),
                  "max_length": np.array(snake_case_      ),
                  "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      lowerCAmelCase_    ( )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       parse_args()
     UpperCAmelCase_	   =       5
     UpperCAmelCase_	   =       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()

     UpperCAmelCase_	   =       torch.device(args.device      )

     UpperCAmelCase_   ,			UpperCAmelCase_	   =       load_model_tokenizer(args.model_name_or_path ,   snake_case_      )

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

     model.to(snake_case_      )

     if args.max_length:
          UpperCAmelCase_	   =       args.max_length

     if args.num_beams:
          UpperCAmelCase_	   =       args.num_beams

     if args.output_file_path:
          UpperCAmelCase_	   =       args.output_file_path
     else:
          UpperCAmelCase_	   =       "BART.onnx"

     logger.info("Exporting model to ONNX"      )
     export_and_validate_model(snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_      )


if __name__ == "__main__":
    main()




