
'''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     =       logging.getLogger(__name__)

SCREAMING_SNAKE_CASE     =       {"""facebook/bart-base""": BartForConditionalGeneration}
SCREAMING_SNAKE_CASE     =       {"""facebook/bart-base""": BartTokenizer}







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

  UpperCAmelCase__      :					Any				       =							parser.parse_args()

  return args







def snake_case_     (					lowercase__   ,       lowercase__="cpu"		):
  UpperCAmelCase__      :					List[str]				       =							model_dict[model_name].from_pretrained(lowercase__		).to(lowercase__		)
  UpperCAmelCase__      :					Optional[int]				       =							tokenizer_dict[model_name].from_pretrained(lowercase__		)

  if model_name in ["facebook/bart-base"]:
    UpperCAmelCase__      :					Union[str, Any]				       =							0
    UpperCAmelCase__      :					Dict				       =							None
    UpperCAmelCase__      :					Tuple				       =							0

  return huggingface_model, tokenizer







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__   ,       lowercase__   ,       lowercase__		):
  model.eval()

  UpperCAmelCase__      :					Tuple				       =							None
  UpperCAmelCase__      :					Tuple				       =							torch.jit.script(BARTBeamSearchGenerator(lowercase__		)		)

  with torch.no_grad():
    UpperCAmelCase__      :					Optional[int]				       =							"My friends are cool but they eat too many carbs."
    UpperCAmelCase__      :					str				       =							tokenizer([ARTICLE_TO_SUMMARIZE]   ,       max_length=1_0_2_4   ,       return_tensors="pt"		).to(model.device		)

    UpperCAmelCase__      :					Dict				       =							model.generate(
        inputs["input_ids"]   ,       attention_mask=inputs["attention_mask"]   ,       num_beams=lowercase__   ,       max_length=lowercase__   ,       early_stopping=lowercase__   ,       decoder_start_token_id=model.config.decoder_start_token_id   ,       )

    torch.onnx.export(
        lowercase__   ,       (
            inputs["input_ids"],
            inputs["attention_mask"],
            num_beams,
            max_length,
            model.config.decoder_start_token_id,
        )   ,       lowercase__   ,       opset_version=1_4   ,       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=lowercase__   ,       )

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

    UpperCAmelCase__      :					str				       =							remove_dup_initializers(os.path.abspath(lowercase__		)		)

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

    UpperCAmelCase__      :					str				       =							onnxruntime.InferenceSession(lowercase__		)
    UpperCAmelCase__      :					List[str]				       =							ort_sess.run(
        lowercase__   ,       {
            "input_ids": inputs["input_ids"].cpu().numpy(),
            "attention_mask": inputs["attention_mask"].cpu().numpy(),
            "num_beams": np.array(lowercase__		),
            "max_length": np.array(lowercase__		),
            "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_     (					):
  UpperCAmelCase__      :					Union[str, Any]				       =							parse_args()
  UpperCAmelCase__      :					int				       =							5
  UpperCAmelCase__      :					int				       =							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__      :					str				       =							torch.device(args.device		)

  UpperCAmelCase__       ,			UpperCAmelCase__      :					str				       =							load_model_tokenizer(args.model_name_or_path   ,       lowercase__		)

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

  model.to(lowercase__		)

  if args.max_length:
    UpperCAmelCase__      :					Tuple				       =							args.max_length

  if args.num_beams:
    UpperCAmelCase__      :					str				       =							args.num_beams

  if args.output_file_path:
    UpperCAmelCase__      :					Any				       =							args.output_file_path
  else:
    UpperCAmelCase__      :					str				       =							"BART.onnx"

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


if __name__ == "__main__":
      main()





'''simple docstring'''





import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax

      from transformers.models.big_bird.modeling_flax_big_bird import (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
          FlaxBigBirdModel,
      )






class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def __init__(					self		:  Tuple			,				snake_case__		:  List[str]			,				snake_case__		:  Union[str, Any]=2			,				snake_case__		:  int=56			,				snake_case__		:  Any=True			,				snake_case__		:  Union[str, Any]=True			,				snake_case__		:  Optional[int]=True			,				snake_case__		:  Any=True			,				snake_case__		:  Tuple=99			,				snake_case__		:  Any=32			,				snake_case__		:  List[Any]=2			,				snake_case__		:  Optional[Any]=2			,				snake_case__		:  Any=7			,				snake_case__		:  Union[str, Any]="gelu_new"			,				snake_case__		:  List[Any]=0.1			,				snake_case__		:  Optional[Any]=0.1			,				snake_case__		:  Optional[int]=5_12			,				snake_case__		:  Optional[int]=16			,				snake_case__		:  int=2			,				snake_case__		:  List[Any]=0.02			,				snake_case__		:  int=4			,				snake_case__		:  Optional[Any]="block_sparse"			,				snake_case__		:  Union[str, Any]=True			,				snake_case__		:  List[Any]=False			,				snake_case__		:  Optional[Any]=2			,				snake_case__		:  Optional[Any]=3			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							parent
   UpperCAmelCase__      :					Dict				       =							batch_size
   UpperCAmelCase__      :					Tuple				       =							seq_length
   UpperCAmelCase__      :					Optional[int]				       =							is_training
   UpperCAmelCase__      :					Optional[int]				       =							use_attention_mask
   UpperCAmelCase__      :					List[Any]				       =							use_token_type_ids
   UpperCAmelCase__      :					Dict				       =							use_labels
   UpperCAmelCase__      :					Tuple				       =							vocab_size
   UpperCAmelCase__      :					str				       =							hidden_size
   UpperCAmelCase__      :					List[str]				       =							num_hidden_layers
   UpperCAmelCase__      :					Union[str, Any]				       =							num_attention_heads
   UpperCAmelCase__      :					Optional[int]				       =							intermediate_size
   UpperCAmelCase__      :					Optional[int]				       =							hidden_act
   UpperCAmelCase__      :					Union[str, Any]				       =							hidden_dropout_prob
   UpperCAmelCase__      :					str				       =							attention_probs_dropout_prob
   UpperCAmelCase__      :					str				       =							max_position_embeddings
   UpperCAmelCase__      :					Any				       =							type_vocab_size
   UpperCAmelCase__      :					Dict				       =							type_sequence_label_size
   UpperCAmelCase__      :					Dict				       =							initializer_range
   UpperCAmelCase__      :					Any				       =							num_choices

   UpperCAmelCase__      :					List[str]				       =							rescale_embeddings
   UpperCAmelCase__      :					Union[str, Any]				       =							attention_type
   UpperCAmelCase__      :					Union[str, Any]				       =							use_bias
   UpperCAmelCase__      :					Union[str, Any]				       =							block_size
   UpperCAmelCase__      :					Any				       =							num_random_blocks







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size )

   UpperCAmelCase__      :					Optional[Any]				       =							None
   if self.use_attention_mask:
     UpperCAmelCase__      :					List[Any]				       =							random_attention_mask([self.batch_size, self.seq_length] )

   UpperCAmelCase__      :					Union[str, Any]				       =							None
   if self.use_token_type_ids:
     UpperCAmelCase__      :					Any				       =							ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size )

   UpperCAmelCase__      :					Union[str, Any]				       =							BigBirdConfig(
       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			,				type_vocab_size=self.type_vocab_size			,				is_decoder=snake_case__			,				initializer_range=self.initializer_range			,				attention_type=self.attention_type			,				block_size=self.block_size			,				num_random_blocks=self.num_random_blocks			,				use_bias=self.use_bias			,				rescale_embeddings=self.rescale_embeddings			,				)

   return config, input_ids, token_type_ids, attention_mask







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.prepare_config_and_inputs()
   UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Tuple				       =							config_and_inputs
   UpperCAmelCase__      :					str				       =							{
       "input_ids": input_ids,
       "token_type_ids": token_type_ids,
       "attention_mask": attention_mask,
   }
   return config, inputs_dict






@require_flax
class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Any				=							(
     (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdModel,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
     )
     if is_flax_available()
     else ()
 )

 lowercase_					:    Optional[Any]				=							False
 lowercase_					:    str				=							False







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							FlaxBigBirdModelTester(self )







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   super().test_from_pretrained_save_pretrained()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   super().test_from_pretrained_with_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   super().test_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   super().test_hidden_states_output()







 @slow
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     UpperCAmelCase__      :					List[Any]				       =							model_class_name.from_pretrained("google/bigbird-roberta-base" )
     self.assertIsNotNone(snake_case__ )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   if self.test_attn_probs:
     super().test_attention_outputs()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					str				       =							self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__ ):
       UpperCAmelCase__      :					Union[str, Any]				       =							self._prepare_for_class(snake_case__			,				snake_case__ )
       UpperCAmelCase__      :					List[Any]				       =							model_class(snake_case__ )

       @jax.jit
       def model_jitted(snake_case__		:  int			,				snake_case__		:  Optional[int]=None			,				**snake_case__		:  Optional[Any] ):
         return model(input_ids=snake_case__			,				attention_mask=snake_case__			,				**snake_case__ )

       with self.subTest("JIT Enabled" ):
         UpperCAmelCase__      :					List[Any]				       =							model_jitted(**snake_case__ ).to_tuple()

       with self.subTest("JIT Disabled" ):
         with jax.disable_jit():
           UpperCAmelCase__      :					Tuple				       =							model_jitted(**snake_case__ ).to_tuple()

       self.assertEqual(len(snake_case__ )			,				len(snake_case__ ) )
       for jitted_output, output in zip(snake_case__			,				snake_case__ ):
         self.assertEqual(jitted_output.shape			,				output.shape )







 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  int			,				snake_case__		:  Dict			,				snake_case__		:  Any			,				snake_case__		:  Union[str, Any]=1e-5			,				snake_case__		:  Any="outputs"			,				snake_case__		:  Optional[Any]=None ):



   '''simple docstring'''




   if name.startswith("outputs.attentions" ):
     return
   else:
     super().check_pt_flax_outputs(snake_case__			,				snake_case__			,				snake_case__			,				snake_case__			,				snake_case__			,				snake_case__ )



