

import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402


UpperCAmelCase_							:					List[Any]		 =   get_tests_dir('''fixtures/dummy-config.json''')






class      lowerCamelCase_      (	unittest.TestCase	):




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			str		    =			0




	def        lowerCAmelCase_       (     self						:     List[str]							):
		self.assertIsNotNone(transformers.models.auto.__spec__							)
		self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto"""							)							)




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			List[str]		    =			AutoConfig.from_pretrained("""bert-base-uncased"""							)
		self.assertIsInstance(__A					,      __A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			Optional[int]		    =			AutoConfig.from_pretrained(__A							)
		self.assertIsInstance(__A					,      __A							)




	def        lowerCAmelCase_       (     self						:     Dict							):
		__A :			Optional[Any]		    =			AutoConfig.from_pretrained(__A							)
		self.assertIsInstance(__A					,      __A							)




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Union[str, Any]		    =			AutoConfig.for_model("""roberta"""							)
		self.assertIsInstance(__A					,      __A							)




	def        lowerCAmelCase_       (     self						:     int							):
		with tempfile.TemporaryDirectory() as tmp_dir:
			# This model name contains bert and roberta, but roberta ends up being picked.
			__A :			Dict		    =			os.path.join(__A					,      """fake-roberta"""							)
			os.makedirs(__A					,      exist_ok=__A							)
			with open(os.path.join(__A					,      """config.json"""							)					,      """w"""							) as f:
				f.write(json.dumps({}							)							)
			__A :			Optional[Any]		    =			AutoConfig.from_pretrained(__A							)
			self.assertEqual(type(__A							)					,      __A							)




	def        lowerCAmelCase_       (     self						:     Dict							):
		try:
			AutoConfig.register("""custom"""					,      __A							)
			# Wrong model type will raise an error
			with self.assertRaises(__A							):
				AutoConfig.register("""model"""					,      __A							)
			# Trying to register something existing in the Transformers library will raise an error
			with self.assertRaises(__A							):
				AutoConfig.register("""bert"""					,      __A							)

			# Now that the config is registered, it can be used as any other config with the auto-API
			__A :			Any		    =			CustomConfig()
			with tempfile.TemporaryDirectory() as tmp_dir:
				config.save_pretrained(__A							)
				__A :			List[str]		    =			AutoConfig.from_pretrained(__A							)
				self.assertIsInstance(__A					,      __A							)

		finally:
			if "custom" in CONFIG_MAPPING._extra_content:
				del CONFIG_MAPPING._extra_content["custom"]




	def        lowerCAmelCase_       (     self						:     Tuple							):
		with self.assertRaisesRegex(
		    __A					,      """bert-base is not a local folder and is not a valid model identifier"""							):
			__A :			int		    =			AutoConfig.from_pretrained("""bert-base"""							)




	def        lowerCAmelCase_       (     self						:     Tuple							):
		with self.assertRaisesRegex(
		    __A					,      R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""							):
			__A :			Dict		    =			AutoConfig.from_pretrained(__A					,      revision="""aaaaaa"""							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		with self.assertRaisesRegex(
		    __A					,      """hf-internal-testing/no-config-test-repo does not appear to have a file named config.json."""					,      ):
			__A :			Dict		    =			AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo"""							)




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		# If remote code is not set, we will time out when asking whether to load the model.
		with self.assertRaises(__A							):
			__A :			List[str]		    =			AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""							)
		# If remote code is disabled, we can't load this config.
		with self.assertRaises(__A							):
			__A :			Optional[Any]		    =			AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,      trust_remote_code=__A							)

		__A :			str		    =			AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,      trust_remote_code=__A							)
		self.assertEqual(config.__class__.__name__					,      """NewModelConfig"""							)

		# Test config can be reloaded.
		with tempfile.TemporaryDirectory() as tmp_dir:
			config.save_pretrained(__A							)
			__A :			str		    =			AutoConfig.from_pretrained(__A					,      trust_remote_code=__A							)
		self.assertEqual(reloaded_config.__class__.__name__					,      """NewModelConfig"""							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):






		class      lowerCamelCase_      (	_lowercase	):
			_lowercase		:   Tuple     					=				'''new-model'''

		try:
			AutoConfig.register("""new-model"""					,      __A							)
			# If remote code is not set, the default is to use local
			__A :			Any		    =			AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""							)
			self.assertEqual(config.__class__.__name__					,      """NewModelConfigLocal"""							)

			# If remote code is disabled, we load the local one.
			__A :			Union[str, Any]		    =			AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,      trust_remote_code=__A							)
			self.assertEqual(config.__class__.__name__					,      """NewModelConfigLocal"""							)

			# If remote is enabled, we load from the Hub
			__A :			List[Any]		    =			AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""					,      trust_remote_code=__A							)
			self.assertEqual(config.__class__.__name__					,      """NewModelConfig"""							)

		finally:
			if "new-model" in CONFIG_MAPPING._extra_content:
				del CONFIG_MAPPING._extra_content["new-model"]









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			:			Union[str, Any]												=					logging.getLogger(__name__)

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


def 		lowercase_	(  ):


			'''simple docstring'''







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

			__lowercase			       = parser.parse_args()

			return args


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase="cpu"	):


			'''simple docstring'''







			__lowercase			       = model_dict[model_name].from_pretrained(_UpperCamelCase	).to(_UpperCamelCase	)
			__lowercase			       = tokenizer_dict[model_name].from_pretrained(_UpperCamelCase	)

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

			return huggingface_model, tokenizer


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			model.eval()

			__lowercase			       = None
			__lowercase			       = torch.jit.script(BARTBeamSearchGenerator(_UpperCamelCase	)	)

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

						__lowercase			       = model.generate(
						    inputs['''input_ids''']							,      attention_mask=inputs['''attention_mask''']							,      num_beams=_UpperCamelCase							,      max_length=_UpperCamelCase							,      early_stopping=_UpperCamelCase							,      decoder_start_token_id=model.config.decoder_start_token_id							,      )

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

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

						__lowercase			       = remove_dup_initializers(os.path.abspath(_UpperCamelCase	)	)

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

						__lowercase			       = onnxruntime.InferenceSession(_UpperCamelCase	)
						__lowercase			       = ort_sess.run(
						    _UpperCamelCase							,      {
						        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
						        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
						        '''num_beams''': np.array(_UpperCamelCase	),
						        '''max_length''': np.array(_UpperCamelCase	),
						        '''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_	(  ):


			'''simple docstring'''







			__lowercase			       = parse_args()
			__lowercase			       = 5
			__lowercase			       = 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			       = torch.device(args.device	)

			__lowercase       ,			__lowercase			       = load_model_tokenizer(args.model_name_or_path							,      _UpperCamelCase	)

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

			model.to(_UpperCamelCase	)

			if args.max_length:
						__lowercase			       = args.max_length

			if args.num_beams:
						__lowercase			       = args.num_beams

			if args.output_file_path:
						__lowercase			       = args.output_file_path
			else:
						__lowercase			       = '''BART.onnx'''

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


if __name__ == "__main__":
	main()


