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	: Dict    		=  logging.getLogger(__name__)

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

						SCREAMING_SNAKE_CASE							:					Dict           =							parser.parse_args()

						return args
def    A			(					_lowercase	,						_lowercase="cpu"		):
						SCREAMING_SNAKE_CASE							:					Dict           =							model_dict[model_name].from_pretrained(_lowercase		).to(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tokenizer_dict[model_name].from_pretrained(_lowercase		)

						if model_name in ["facebook/bart-base"]:
												SCREAMING_SNAKE_CASE							:					Dict           =							0
												SCREAMING_SNAKE_CASE							:					List[str]           =							None
												SCREAMING_SNAKE_CASE							:					int           =							0

						return huggingface_model, tokenizer
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):
						model.eval()

						SCREAMING_SNAKE_CASE							:					Any           =							None
						SCREAMING_SNAKE_CASE							:					Dict           =							torch.jit.script(BARTBeamSearchGenerator(_lowercase		)		)

						with torch.no_grad():
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''My friends are cool but they eat too many carbs.'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							tokenizer([ARTICLE_TO_SUMMARIZE]	,						max_length=1_024	,						return_tensors='''pt'''		).to(model.device		)

												SCREAMING_SNAKE_CASE							:					str           =							model.generate(
												    inputs['''input_ids''']	,						attention_mask=inputs['''attention_mask''']	,						num_beams=_lowercase	,						max_length=_lowercase	,						early_stopping=_lowercase	,						decoder_start_token_id=model.config.decoder_start_token_id	,						)

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

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

												SCREAMING_SNAKE_CASE							:					str           =							remove_dup_initializers(os.path.abspath(_lowercase		)		)

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

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

						SCREAMING_SNAKE_CASE							:					int           =							torch.device(args.device		)

						SCREAMING_SNAKE_CASE							:					List[Any]           =							load_model_tokenizer(args.model_name_or_path	,						_lowercase		)

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

						model.to(_lowercase		)

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

						if args.num_beams:
												SCREAMING_SNAKE_CASE							:					Dict           =							args.num_beams

						if args.output_file_path:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							args.output_file_path
						else:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''BART.onnx'''

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


if __name__ == "__main__":
					main()






import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

__UpperCamelCase	: Dict    		=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCamelCase	: Tuple    		=  [file for file in filepaths if file != file.lower()]
if upper_files:
					print(f"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if ' ' in file]
if space_files:
					print(f"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if '-' in file]
if hyphen_files:
					print(f"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(f"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

__UpperCamelCase	: Optional[Any]    		=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)





