
"""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,
)

UpperCamelCase     :    str       =			logging.getLogger(__name__)

UpperCamelCase     :    Optional[int]       =			{"facebook/bart-base": BartForConditionalGeneration}
UpperCamelCase     :    Any       =			{"facebook/bart-base": BartTokenizer}






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

	__UpperCamelCase      =	parser.parse_args()

	return args






def 							A						(    snake_case				:Tuple    , snake_case				:List[Any]="cpu"		)				->     int:
	__UpperCamelCase      =	model_dict[model_name].from_pretrained(snake_case		).to(snake_case		)
	__UpperCamelCase      =	tokenizer_dict[model_name].from_pretrained(snake_case		)

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

	return huggingface_model, tokenizer






def 							A						(    snake_case				:List[str]    , snake_case				:Union[str, Any]    , snake_case				:Optional[int]    , snake_case				:Union[str, Any]    , snake_case				:List[str]		)				->     Any:
	model.eval()

	__UpperCamelCase      =	None
	__UpperCamelCase      =	torch.jit.script(BARTBeamSearchGenerator(snake_case		)		)

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

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

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

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

		__UpperCamelCase      =	remove_dup_initializers(os.path.abspath(snake_case		)		)

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

		__UpperCamelCase      =	onnxruntime.InferenceSession(snake_case		)
		__UpperCamelCase      =	ort_sess.run(
		    snake_case    , {
		        'input_ids': inputs['input_ids'].cpu().numpy(),
		        'attention_mask': inputs['attention_mask'].cpu().numpy(),
		        'num_beams': np.array(snake_case		),
		        'max_length': np.array(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 							A						(    )				->     Any:
	__UpperCamelCase      =	parse_args()
	__UpperCamelCase      =	5
	__UpperCamelCase      =	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      =	torch.device(args.device		)

	__UpperCamelCase  ,     __UpperCamelCase      =	load_model_tokenizer(args.model_name_or_path    , 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(snake_case		)

	if args.max_length:
		__UpperCamelCase      =	args.max_length

	if args.num_beams:
		__UpperCamelCase      =	args.num_beams

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

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


if __name__ == "__main__":
						main()





"""simple docstring"""

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands






def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	ArgumentParser('Transformers CLI tool'    , usage='transformers-cli <command> [<args>]'		)
 __UpperCamelCase      =	parser.add_subparsers(help='transformers-cli command helpers'		)

 # Register commands
 ConvertCommand.register_subcommand(snake_case		)
 DownloadCommand.register_subcommand(snake_case		)
 EnvironmentCommand.register_subcommand(snake_case		)
 RunCommand.register_subcommand(snake_case		)
 ServeCommand.register_subcommand(snake_case		)
 UserCommands.register_subcommand(snake_case		)
 AddNewModelCommand.register_subcommand(snake_case		)
 AddNewModelLikeCommand.register_subcommand(snake_case		)
 LfsCommands.register_subcommand(snake_case		)
 PTtoTFCommand.register_subcommand(snake_case		)

 # Let's go
 __UpperCamelCase      =	parser.parse_args()

 if not hasattr(snake_case    , 'func'		):
  parser.print_help()
  exit(1		)

 # Run
 __UpperCamelCase      =	args.func(snake_case		)
 service.run()


if __name__ == "__main__":
      main()



