





"""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			      =	logging.getLogger(__name__)

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

def      __A   (      )		->		str:
				__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			:    Any      							=	parser.parse_args()

				return args

def      __A   (      a_    :int ,							a_    :List[Any]="cpu")		->		List[Any]:
				__a			:    Optional[int]      							=	model_dict[model_name].from_pretrained(a_).to(a_)
				__a			:    Tuple      							=	tokenizer_dict[model_name].from_pretrained(a_)

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

				return huggingface_model, tokenizer

def      __A   (      a_    :Optional[Any] ,							a_    :int ,							a_    :Tuple ,							a_    :List[str] ,							a_    :int)		->		Optional[int]:
				model.eval()

				__a			:    Dict      							=	None
				__a			:    List[str]      							=	torch.jit.script(BARTBeamSearchGenerator(a_))

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

								__a			:    List[str]      							=	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			:    Dict      							=	remove_dup_initializers(os.path.abspath(a_))

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

								__a			:    Tuple      							=	onnxruntime.InferenceSession(a_)
								__a			:    Tuple      							=	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      __A   (      )		->		Any:
				__a			:    Tuple      							=	parse_args()
				__a			:    Tuple      							=	5
				__a			:    Union[str, Any]      							=	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			:    Optional[int]      							=	torch.device(args.device)

				__a							,    __a			:    Tuple      							=	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			:    int      							=	args.max_length

				if args.num_beams:
								__a			:    List[str]      							=	args.num_beams

				if args.output_file_path:
								__a			:    Any      							=	args.output_file_path
				else:
								__a			:    Tuple      							=	'''BART.onnx'''

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


if __name__ == "__main__":
		main()





"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """openai-gpt"""
			lowerCamelCase__ =     {
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}
			def __init__(    self    , lowercase=40478    , lowercase=512    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=0.1    , lowercase=1E-5    , lowercase=0.02    , lowercase="cls_index"    , lowercase=True    , lowercase=None    , lowercase=True    , lowercase=0.1    , **lowercase    , ):
							_lowerCamelCase	:     Tuple       =       vocab_size
							_lowerCamelCase	:     List[str]       =       n_positions
							_lowerCamelCase	:     Tuple       =       n_embd
							_lowerCamelCase	:     Optional[int]       =       n_layer
							_lowerCamelCase	:     List[Any]       =       n_head
							_lowerCamelCase	:     Dict       =       afn
							_lowerCamelCase	:     Union[str, Any]       =       resid_pdrop
							_lowerCamelCase	:     Dict       =       embd_pdrop
							_lowerCamelCase	:     List[Any]       =       attn_pdrop
							_lowerCamelCase	:     Any       =       layer_norm_epsilon
							_lowerCamelCase	:     List[Any]       =       initializer_range
							_lowerCamelCase	:     List[Any]       =       summary_type
							_lowerCamelCase	:     List[str]       =       summary_use_proj
							_lowerCamelCase	:     Tuple       =       summary_activation
							_lowerCamelCase	:     int       =       summary_first_dropout
							_lowerCamelCase	:     str       =       summary_proj_to_labels
							super().__init__(**lowercase    )