

"""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,
)

a					:Dict    = logging.getLogger(__name__)

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




def  _lowercase					(    )       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      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."""							)

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

      return args




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase="cpu"							)       ->				Optional[Any]:
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	model_dict[model_name].from_pretrained(__lowerCAmelCase							).to(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	tokenizer_dict[model_name].from_pretrained(__lowerCAmelCase							)

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

      return huggingface_model, tokenizer




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Optional[Any]:
      model.eval()

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

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

            SCREAMING_SNAKE_CASE__   :      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							)							)

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

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

            SCREAMING_SNAKE_CASE__   :      Dict      		=	onnxruntime.InferenceSession(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      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  _lowercase					(    )       ->				Tuple:
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	parse_args()
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	5
      SCREAMING_SNAKE_CASE__   :      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()

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	torch.device(args.device							)

      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      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:
            SCREAMING_SNAKE_CASE__   :      Dict      		=	args.max_length

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

      if args.output_file_path:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	args.output_file_path
      else:
            SCREAMING_SNAKE_CASE__   :      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 unittest

import numpy as np

from transformers import RoFormerConfig, 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.numpy as jnp

  from transformers.models.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )

class      __a							(unittest.TestCase):






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a=13	,    _a=7	,    _a=True	,    _a=True	,    _a=True	,    _a=True	,    _a=99	,    _a=32	,    _a=5	,    _a=4	,    _a=37	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=512	,    _a=16	,    _a=2	,    _a=0.02	,    _a=4	,    )  ->      List[str]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	None
           if self.use_attention_mask:
                 SCREAMING_SNAKE_CASE__   :      int      		=	random_attention_mask([self.batch_size, self.seq_length]  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	None
           if self.use_token_type_ids:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	RoFormerConfig(
               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=_a	,    initializer_range=self.initializer_range	,    )

           return config, input_ids, token_type_ids, attention_mask





     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[str]      		=	config_and_inputs
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
           return config, inputs_dict





@require_flax
class      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = True

     _SCREAMING_SNAKE_CASE						:Optional[Any]       = (
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerModelTester(self  )





     @slow
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""	,    from_pt=_a  )
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model(np.ones((1, 1)  )  )
                 self.assertIsNotNone(_a  )





@require_flax
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	jnp.array([[0, 1, 2, 3, 4, 5]]  )
           SCREAMING_SNAKE_CASE__   :      str      		=	model(_a  )[0]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	50_000

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	(1, 6, vocab_size)
           self.assertEqual(output.shape	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	jnp.array(
               [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]  )

           self.assertTrue(jnp.allclose(output[:, :3, :3]	,    _a	,    atol=1E-4  )  )




