



'''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   :			Optional[Any]					  =    logging.getLogger(__name__)

a   :			Dict					  =    {"""facebook/bart-base""": BartForConditionalGeneration}
a   :			int					  =    {"""facebook/bart-base""": BartTokenizer}



def       __lowerCamelCase  (					)      ->			Any:
							UpperCAmelCase     :		Any							     =		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     :		int							     =		parser.parse_args()

							return args



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

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

							return huggingface_model, tokenizer



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			int:
							model.eval()

							UpperCAmelCase     :		Optional[Any]							     =		None
							UpperCAmelCase     :		Optional[Any]							     =		torch.jit.script(BARTBeamSearchGenerator(_lowercase		)		)

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

														UpperCAmelCase     :		Optional[int]							     =		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     :		Union[str, Any]							     =		remove_dup_initializers(os.path.abspath(_lowercase		)		)

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

														UpperCAmelCase     :		Union[str, Any]							     =		onnxruntime.InferenceSession(_lowercase		)
														UpperCAmelCase     :		Optional[Any]							     =		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       __lowerCamelCase  (					)      ->			Dict:
							UpperCAmelCase     :		Union[str, Any]							     =		parse_args()
							UpperCAmelCase     :		int							     =		5
							UpperCAmelCase     :		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()

							UpperCAmelCase     :		Dict							     =		torch.device(args.device		)

							UpperCAmelCase     ,     UpperCAmelCase     :		Tuple							     =		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     :		Union[str, Any]							     =		args.max_length

							if args.num_beams:
														UpperCAmelCase     :		str							     =		args.num_beams

							if args.output_file_path:
														UpperCAmelCase     :		Tuple							     =		args.output_file_path
							else:
														UpperCAmelCase     :		Any							     =		"""BART.onnx"""

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


if __name__ == "__main__":
							main()






'''simple docstring'''




import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
							from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
							from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
							from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


a   :			str					  =    DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

a   :			int					  =    """main"""
# Default branch name
a   :			Any					  =    """f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"""
# One particular commit (not the top of `main`)
a   :			str					  =    """aaaaaaa"""
# This commit does not exist, so we should 404.

a   :			int					  =    """d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"""
# Sha-1 of config.json on the top of `main`, for checking purposes
a   :			Any					  =    """4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"""



@contextlib.contextmanager
def       __lowerCamelCase  (					)      ->			List[str]:
							print("""Welcome!"""		)
							yield
							print("""Bye!"""		)



@contextlib.contextmanager
def       __lowerCamelCase  (					)      ->			Optional[int]:
							print("""Bonjour!"""		)
							yield
							print("""Au revoir!"""		)

class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			List[Any]:
								# If the spec is missing, importlib would not be able to import the module dynamically.
								assert transformers.__spec__ is not None
								assert importlib.util.find_spec("""transformers"""    ) is not None




class 			UpperCamelCase_ (							unittest.TestCase			):





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Tuple:
								with ContextManagers([]    ):
															print("""Transformers are awesome!"""    )
								# The print statement adds a new line at the end of the output
								self.assertEqual(mock_stdout.getvalue()		,		"""Transformers are awesome!\n"""    )





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Dict:
								with ContextManagers([context_en()]    ):
															print("""Transformers are awesome!"""    )
								# The output should be wrapped with an English welcome and goodbye
								self.assertEqual(mock_stdout.getvalue()		,		"""Welcome!\nTransformers are awesome!\nBye!\n"""    )





	@unittest.mock.patch("""sys.stdout"""		,		new_callable=io.StringIO    )
	def  _lowercase(  self		,		A    )			->			Union[str, Any]:
								with ContextManagers([context_fr(), context_en()]    ):
															print("""Transformers are awesome!"""    )
								# The output should be wrapped with an English and French welcome and goodbye
								self.assertEqual(mock_stdout.getvalue()		,		"""Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"""    )





	@require_torch
	def  _lowercase(  self    )			->			Optional[int]:
								self.assertEqual(find_labels(A    )		,		["""labels"""]    )
								self.assertEqual(find_labels(A    )		,		["""labels""", """next_sentence_label"""]    )
								self.assertEqual(find_labels(A    )		,		["""start_positions""", """end_positions"""]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		["""labels"""]    )





	@require_tf
	def  _lowercase(  self    )			->			int:
								self.assertEqual(find_labels(A    )		,		["""labels"""]    )
								self.assertEqual(find_labels(A    )		,		["""labels""", """next_sentence_label"""]    )
								self.assertEqual(find_labels(A    )		,		["""start_positions""", """end_positions"""]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		["""labels"""]    )






	@require_flax
	def  _lowercase(  self    )			->			Any:
								# Flax models don't have labels
								self.assertEqual(find_labels(A    )		,		[]    )
								self.assertEqual(find_labels(A    )		,		[]    )
								self.assertEqual(find_labels(A    )		,		[]    )

								class 			UpperCamelCase_ (							__magic_name__			):
									pass




								self.assertEqual(find_labels(A    )		,		[]    )

