"""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__   :      Tuple							=logging.getLogger(__name__)

SCREAMING_SNAKE_CASE__   :      List[Any]							={'facebook/bart-base': BartForConditionalGeneration}
SCREAMING_SNAKE_CASE__   :      Union[str, Any]							={'facebook/bart-base': BartTokenizer}



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

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

    return args



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_="cpu" )      ->Optional[int]:
    _lowerCamelCase	: Tuple       =       model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
    _lowerCamelCase	: Optional[int]       =       tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_ )

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

    return huggingface_model, tokenizer



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->str:
    model.eval()

    _lowerCamelCase	: int       =       None
    _lowerCamelCase	: List[Any]       =       torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE_ ) )

    with torch.no_grad():
        _lowerCamelCase	: Optional[Any]       =       '''My friends are cool but they eat too many carbs.'''
        _lowerCamelCase	: Tuple       =       tokenizer([ARTICLE_TO_SUMMARIZE]						,	max_length=1024						,	return_tensors='''pt''' ).to(model.device )

        _lowerCamelCase	: Optional[int]       =       model.generate(
            inputs['''input_ids''']						,	attention_mask=inputs['''attention_mask''']						,	num_beams=SCREAMING_SNAKE_CASE_						,	max_length=SCREAMING_SNAKE_CASE_						,	early_stopping=SCREAMING_SNAKE_CASE_						,	decoder_start_token_id=model.config.decoder_start_token_id						,	)

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

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

        _lowerCamelCase	: Union[str, Any]       =       remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE_ ) )

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

        _lowerCamelCase	: str       =       onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE_ )
        _lowerCamelCase	: Tuple       =       ort_sess.run(
            SCREAMING_SNAKE_CASE_						,	{
                '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                '''num_beams''': np.array(SCREAMING_SNAKE_CASE_ ),
                '''max_length''': np.array(SCREAMING_SNAKE_CASE_ ),
                '''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					(       )      ->Dict:
    _lowerCamelCase	: str       =       parse_args()
    _lowerCamelCase	: int       =       5
    _lowerCamelCase	: 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()

    _lowerCamelCase	: Optional[Any]       =       torch.device(args.device )

    _lowerCamelCase,					_lowerCamelCase	: Optional[int]       =       load_model_tokenizer(args.model_name_or_path						,	SCREAMING_SNAKE_CASE_ )

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

    model.to(SCREAMING_SNAKE_CASE_ )

    if args.max_length:
        _lowerCamelCase	: Optional[Any]       =       args.max_length

    if args.num_beams:
        _lowerCamelCase	: Optional[int]       =       args.num_beams

    if args.output_file_path:
        _lowerCamelCase	: Tuple       =       args.output_file_path
    else:
        _lowerCamelCase	: int       =       '''BART.onnx'''

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


if __name__ == "__main__":
  main()








'''simple docstring'''


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    OpenAIGPTConfig,
	    OpenAIGPTDoubleHeadsModel,
	    OpenAIGPTForSequenceClassification,
	    OpenAIGPTLMHeadModel,
	    OpenAIGPTModel,
	)
class 						a_				:




	def __init__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]=1_3       ,		__lowerCAmelCase     :  Tuple=7       ,		__lowerCAmelCase     :  Optional[int]=True       ,		__lowerCAmelCase     :  Any=True       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  Union[str, Any]=9_9       ,		__lowerCAmelCase     :  Any=3_2       ,		__lowerCAmelCase     :  Tuple=5       ,		__lowerCAmelCase     :  int=4       ,		__lowerCAmelCase     :  List[str]=3_7       ,		__lowerCAmelCase     :  List[Any]="gelu"       ,		__lowerCAmelCase     :  Union[str, Any]=0.1       ,		__lowerCAmelCase     :  int=0.1       ,		__lowerCAmelCase     :  Any=5_1_2       ,		__lowerCAmelCase     :  Dict=1_6       ,		__lowerCAmelCase     :  Dict=2       ,		__lowerCAmelCase     :  Union[str, Any]=0.02       ,		__lowerCAmelCase     :  Optional[Any]=3       ,		__lowerCAmelCase     :  str=4       ,		__lowerCAmelCase     :  List[str]=None       ,		):
							__snake_case						 =   parent
							__snake_case						 =   batch_size
							__snake_case						 =   seq_length
							__snake_case						 =   is_training
							__snake_case						 =   use_token_type_ids
							__snake_case						 =   use_labels
							__snake_case						 =   vocab_size
							__snake_case						 =   hidden_size
							__snake_case						 =   num_hidden_layers
							__snake_case						 =   num_attention_heads
							__snake_case						 =   intermediate_size
							__snake_case						 =   hidden_act
							__snake_case						 =   hidden_dropout_prob
							__snake_case						 =   attention_probs_dropout_prob
							__snake_case						 =   max_position_embeddings
							__snake_case						 =   type_vocab_size
							__snake_case						 =   type_sequence_label_size
							__snake_case						 =   initializer_range
							__snake_case						 =   num_labels
							__snake_case						 =   num_choices
							__snake_case						 =   scope
							__snake_case						 =   self.vocab_size - 1




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size      )

							__snake_case						 =   None
							if self.use_token_type_ids:
													__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.type_vocab_size      )

							__snake_case						 =   None
							__snake_case						 =   None
							__snake_case						 =   None
							if self.use_labels:
													__snake_case						 =   ids_tensor([self.batch_size]       ,		self.type_sequence_label_size      )
													__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.num_labels      )
													__snake_case						 =   ids_tensor([self.batch_size]       ,		self.num_choices      )

							__snake_case						 =   OpenAIGPTConfig(
							    vocab_size=self.vocab_size       ,		n_embd=self.hidden_size       ,		n_layer=self.num_hidden_layers       ,		n_head=self.num_attention_heads       ,		n_positions=self.max_position_embeddings       ,		pad_token_id=self.pad_token_id       ,		)

							__snake_case						 =   ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,		2      )

							return (
							    config,
							    input_ids,
							    head_mask,
							    token_type_ids,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							)




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Union[str, Any]       ,		*__lowerCAmelCase     :  List[str]      ):
							__snake_case						 =   OpenAIGPTModel(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		head_mask=__lowerCAmelCase      )
							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase      )
							__snake_case						 =   model(__lowerCAmelCase      )

							self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)      )




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict       ,		*__lowerCAmelCase     :  str      ):
							__snake_case						 =   OpenAIGPTLMHeadModel(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.loss.shape       ,		()      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[Any]       ,		*__lowerCAmelCase     :  Tuple      ):
							__snake_case						 =   OpenAIGPTDoubleHeadsModel(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.loss.shape       ,		()      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)      )




	def   lowercase__   (       self     :  Optional[int]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  int       ,		*__lowerCAmelCase     :  int      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   OpenAIGPTForSequenceClassification(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   ids_tensor([self.batch_size]       ,		self.type_sequence_label_size      )
							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)      )




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   self.prepare_config_and_inputs()
							(
							    (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       
							)						 =   config_and_inputs
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'token_type_ids': token_type_ids,
							    'head_mask': head_mask,
							}

							return config, inputs_dict





@require_torch
class 						a_				(			UpperCAmelCase__     ,						UpperCAmelCase__     ,						UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     List[str]			  =      (
	    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
	    if is_torch_available()
	    else ()
	)
	lowercase_       :     Union[str, Any]			  =      (
	    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
	lowercase_       :     str			  =      (
	    {
	        '''feature-extraction''': OpenAIGPTModel,
	        '''text-classification''': OpenAIGPTForSequenceClassification,
	        '''text-generation''': OpenAIGPTLMHeadModel,
	        '''zero-shot''': OpenAIGPTForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Any      ):
							if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
													# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
													# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
													# tiny config could not be created.
													return True

							return False




	def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  List[str]=False      ):
							__snake_case						 =   super()._prepare_for_class(__lowerCAmelCase       ,		__lowerCAmelCase       ,		return_labels=__lowerCAmelCase      )

							if return_labels:
													if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																			__snake_case						 =   torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)       ,		dtype=torch.long       ,		device=__lowerCAmelCase       ,		)
																			__snake_case						 =   inputs_dict['labels']
																			__snake_case						 =   inputs_dict['labels']
																			__snake_case						 =   torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.num_choices)       ,		dtype=torch.long       ,		device=__lowerCAmelCase       ,		)
																			__snake_case						 =   torch.zeros(
																			    self.model_tester.batch_size       ,		dtype=torch.long       ,		device=__lowerCAmelCase      )
							return inputs_dict




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   OpenAIGPTModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__lowerCAmelCase       ,		n_embd=3_7      )




	def   lowercase__   (       self     :  int      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_double_lm_head_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__lowerCAmelCase      )




	@slow
	def   lowercase__   (       self     :  str      ):
							for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__snake_case						 =   OpenAIGPTModel.from_pretrained(__lowerCAmelCase      )
													self.assertIsNotNone(__lowerCAmelCase      )





@require_torch
class 						a_				(			unittest.TestCase						):




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'      )
							model.to(__lowerCAmelCase      )
							__snake_case						 =   torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]       ,		dtype=torch.long       ,		device=__lowerCAmelCase      )  # the president is
							__snake_case						 =   [
							    4_8_1,
							    4_7_3_5,
							    5_4_4,
							    2_4_6,
							    9_6_3,
							    8_7_0,
							    7_6_2,
							    2_3_9,
							    2_4_4,
							    4_0_4_7_7,
							    2_4_4,
							    2_4_9,
							    7_1_9,
							    8_8_1,
							    4_8_7,
							    5_4_4,
							    2_4_0,
							    2_4_4,
							    6_0_3,
							    4_8_1,
							]  # the president is a very good man. " \n " i\'m sure he is, " said the

							__snake_case						 =   model.generate(__lowerCAmelCase       ,		do_sample=__lowerCAmelCase      )
							self.assertListEqual(output_ids[0].tolist()       ,		__lowerCAmelCase      )

