
import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
      from transformers.models.rag.configuration_rag import RagConfig
      from transformers.models.rag.tokenization_rag import RagTokenizer



@require_faiss
@require_torch
class 						UpperCamelCase__    (			SCREAMING_SNAKE_CASE_       ):
   def  lowerCAmelCase     (self					:   Union[str, Any]  ):
    __a					:    Union[str, Any]													=    tempfile.mkdtemp()
    __a					:    List[Any]													=    8

    # DPR tok
    __a					:    str													=    [
        '''[UNK]''',
        '''[CLS]''',
        '''[SEP]''',
        '''[PAD]''',
        '''[MASK]''',
        '''want''',
        '''##want''',
        '''##ed''',
        '''wa''',
        '''un''',
        '''runn''',
        '''##ing''',
        ''',''',
        '''low''',
        '''lowest''',
    ]
    __a					:    Optional[Any]													=    os.path.join(self.tmpdirname   ,							'''dpr_tokenizer'''  )
    os.makedirs(__SCREAMING_SNAKE_CASE   ,							exist_ok=__SCREAMING_SNAKE_CASE  )
    __a					:    Union[str, Any]													=    os.path.join(__SCREAMING_SNAKE_CASE   ,							DPR_VOCAB_FILES_NAMES['''vocab_file''']  )
    with open(self.vocab_file   ,							'''w'''   ,							encoding='''utf-8'''  ) as vocab_writer:
     vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )

    # BART tok
    __a					:    int													=    [
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''\u0120''',
        '''\u0120l''',
        '''\u0120n''',
        '''\u0120lo''',
        '''\u0120low''',
        '''er''',
        '''\u0120lowest''',
        '''\u0120newer''',
        '''\u0120wider''',
        '''<unk>''',
    ]
    __a					:    str													=    dict(zip(__SCREAMING_SNAKE_CASE   ,							range(len(__SCREAMING_SNAKE_CASE  )  )  )  )
    __a					:    Union[str, Any]													=    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
    __a					:    Optional[int]													=    {'''unk_token''': '''<unk>'''}

    __a					:    Dict													=    os.path.join(self.tmpdirname   ,							'''bart_tokenizer'''  )
    os.makedirs(__SCREAMING_SNAKE_CASE   ,							exist_ok=__SCREAMING_SNAKE_CASE  )
    __a					:    List[Any]													=    os.path.join(__SCREAMING_SNAKE_CASE   ,							BART_VOCAB_FILES_NAMES['''vocab_file''']  )
    __a					:    str													=    os.path.join(__SCREAMING_SNAKE_CASE   ,							BART_VOCAB_FILES_NAMES['''merges_file''']  )
    with open(self.vocab_file   ,							'''w'''   ,							encoding='''utf-8'''  ) as fp:
     fp.write(json.dumps(__SCREAMING_SNAKE_CASE  ) + '''\n'''  )
    with open(self.merges_file   ,							'''w'''   ,							encoding='''utf-8'''  ) as fp:
     fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE  )  )
   def  lowerCAmelCase     (self					:   Union[str, Any]  ):
    return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,							'''dpr_tokenizer'''  )  )
   def  lowerCAmelCase     (self					:   List[Any]  ):
    return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,							'''bart_tokenizer'''  )  )
   def  lowerCAmelCase     (self					:   Optional[Any]  ):
    shutil.rmtree(self.tmpdirname  )
   @require_tokenizers
   def  lowerCAmelCase     (self					:   List[str]  ):
    __a					:    int													=    os.path.join(self.tmpdirname   ,							'''rag_tokenizer'''  )
    __a					:    str													=    RagConfig(question_encoder=DPRConfig().to_dict()   ,							generator=BartConfig().to_dict()  )
    __a					:    int													=    RagTokenizer(question_encoder=self.get_dpr_tokenizer()   ,							generator=self.get_bart_tokenizer()  )
    rag_config.save_pretrained(__SCREAMING_SNAKE_CASE  )
    rag_tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE  )
    __a					:    Optional[int]													=    RagTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   ,							config=__SCREAMING_SNAKE_CASE  )
    self.assertIsInstance(new_rag_tokenizer.question_encoder   ,							__SCREAMING_SNAKE_CASE  )
    self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()   ,							rag_tokenizer.question_encoder.get_vocab()  )
    self.assertIsInstance(new_rag_tokenizer.generator   ,							__SCREAMING_SNAKE_CASE  )
    self.assertEqual(new_rag_tokenizer.generator.get_vocab()   ,							rag_tokenizer.generator.get_vocab()  )
   @slow
   def  lowerCAmelCase     (self					:   Optional[int]  ):
    __a					:    List[Any]													=    RagTokenizer.from_pretrained('''facebook/rag-token-nq'''  )
    __a					:    List[Any]													=    [
        '''who got the first nobel prize in physics''',
        '''when is the next deadpool movie being released''',
        '''which mode is used for short wave broadcast service''',
        '''who is the owner of reading football club''',
        '''when is the next scandal episode coming out''',
        '''when is the last time the philadelphia won the superbowl''',
        '''what is the most current adobe flash player version''',
        '''how many episodes are there in dragon ball z''',
        '''what is the first step in the evolution of the eye''',
        '''where is gall bladder situated in human body''',
        '''what is the main mineral in lithium batteries''',
        '''who is the president of usa right now''',
        '''where do the greasers live in the outsiders''',
        '''panda is a national animal of which country''',
        '''what is the name of manchester united stadium''',
    ]
    __a					:    Dict													=    tokenizer(__SCREAMING_SNAKE_CASE  )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )







   @slow
   def  lowerCAmelCase     (self					:   List[Any]  ):
    __a					:    str													=    RagTokenizer.from_pretrained('''facebook/rag-sequence-nq'''  )
    __a					:    List[Any]													=    [
        '''who got the first nobel prize in physics''',
        '''when is the next deadpool movie being released''',
        '''which mode is used for short wave broadcast service''',
        '''who is the owner of reading football club''',
        '''when is the next scandal episode coming out''',
        '''when is the last time the philadelphia won the superbowl''',
        '''what is the most current adobe flash player version''',
        '''how many episodes are there in dragon ball z''',
        '''what is the first step in the evolution of the eye''',
        '''where is gall bladder situated in human body''',
        '''what is the main mineral in lithium batteries''',
        '''who is the president of usa right now''',
        '''where do the greasers live in the outsiders''',
        '''panda is a national animal of which country''',
        '''what is the name of manchester united stadium''',
    ]
    __a					:    Union[str, Any]													=    tokenizer(__SCREAMING_SNAKE_CASE  )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )








'''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,
)

__lowerCAmelCase									=   logging.getLogger(__name__)

__lowerCAmelCase									=   {'facebook/bart-base': BartForConditionalGeneration}
__lowerCAmelCase									=   {'facebook/bart-base': BartTokenizer}





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

				a_		:	Tuple									=   parser.parse_args()

				return args





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Dict="cpu"   ):
				a_		:	str									=   model_dict[model_name].from_pretrained(__A   ).to(__A   )
				a_		:	str									=   tokenizer_dict[model_name].from_pretrained(__A   )

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

				return huggingface_model, tokenizer





def 		_UpperCAmelCase (				__A				: int    ,		__A				: Dict    ,		__A				: str    ,		__A				: Optional[Any]    ,		__A				: Any   ):
				model.eval()

				a_		:	Union[str, Any]									=   None
				a_		:	str									=   torch.jit.script(BARTBeamSearchGenerator(__A   )   )

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

								a_		:	Dict									=   model.generate(
								    inputs['''input_ids''']    ,		attention_mask=inputs['''attention_mask''']    ,		num_beams=__A    ,		max_length=__A    ,		early_stopping=__A    ,		decoder_start_token_id=model.config.decoder_start_token_id    ,		)

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

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

								a_		:	Any									=   remove_dup_initializers(os.path.abspath(__A   )   )

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

								a_		:	int									=   onnxruntime.InferenceSession(__A   )
								a_		:	Optional[Any]									=   ort_sess.run(
								    __A    ,		{
								        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
								        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
								        '''num_beams''': np.array(__A   ),
								        '''max_length''': np.array(__A   ),
								        '''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 		_UpperCAmelCase (				):
				a_		:	int									=   parse_args()
				a_		:	List[str]									=   5
				a_		:	List[str]									=   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()

				a_		:	Any									=   torch.device(args.device   )

				a_					,      a_		:	Optional[Any]									=   load_model_tokenizer(args.model_name_or_path    ,		__A   )

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

				model.to(__A   )

				if args.max_length:
								a_		:	Dict									=   args.max_length

				if args.num_beams:
								a_		:	str									=   args.num_beams

				if args.output_file_path:
								a_		:	str									=   args.output_file_path
				else:
								a_		:	Union[str, Any]									=   '''BART.onnx'''

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


if __name__ == "__main__":
		main()




