'''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_      	=	logging.getLogger(__name__)

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


def    lowerCamelCase       (			)			->	Optional[Any]:


							'''simple docstring'''






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

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

							return args


def    lowerCamelCase       (			UpperCAmelCase__				:  Dict				,						UpperCAmelCase__				:  Optional[int]="cpu"		)			->	int:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Tuple						    = model_dict[model_name].from_pretrained(UpperCAmelCase__		).to(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Any						    = tokenizer_dict[model_name].from_pretrained(UpperCAmelCase__		)

							if model_name in ["facebook/bart-base"]:
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 0
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = None
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = 0

							return huggingface_model, tokenizer


def    lowerCamelCase       (			UpperCAmelCase__				:  Tuple				,						UpperCAmelCase__				:  Dict				,						UpperCAmelCase__				:  List[Any]				,						UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  Tuple		)			->	Optional[int]:


							'''simple docstring'''






							model.eval()

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = None
							SCREAMING_SNAKE_CASE__     :Tuple						    = torch.jit.script(BARTBeamSearchGenerator(UpperCAmelCase__		)		)

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

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

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

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

														SCREAMING_SNAKE_CASE__     :List[Any]						    = remove_dup_initializers(os.path.abspath(UpperCAmelCase__		)		)

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

														SCREAMING_SNAKE_CASE__     :str						    = onnxruntime.InferenceSession(UpperCAmelCase__		)
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = ort_sess.run(
														    UpperCAmelCase__				,						{
														        'input_ids': inputs['input_ids'].cpu().numpy(),
														        'attention_mask': inputs['attention_mask'].cpu().numpy(),
														        'num_beams': np.array(UpperCAmelCase__		),
														        'max_length': np.array(UpperCAmelCase__		),
														        '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    lowerCamelCase       (			)			->	List[str]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[Any]						    = parse_args()
							SCREAMING_SNAKE_CASE__     :Any						    = 5
							SCREAMING_SNAKE_CASE__     :Optional[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()

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

							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = load_model_tokenizer(args.model_name_or_path				,						UpperCAmelCase__		)

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

							model.to(UpperCAmelCase__		)

							if args.max_length:
														SCREAMING_SNAKE_CASE__     :Dict						    = args.max_length

							if args.num_beams:
														SCREAMING_SNAKE_CASE__     :List[Any]						    = args.num_beams

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

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


if __name__ == "__main__":
		main()







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_      	=	{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	['''BlipImageProcessor''']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BlipModel''',
		    '''BlipPreTrainedModel''',
		    '''BlipForConditionalGeneration''',
		    '''BlipForQuestionAnswering''',
		    '''BlipVisionModel''',
		    '''BlipTextModel''',
		    '''BlipForImageTextRetrieval''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFBlipModel''',
		    '''TFBlipPreTrainedModel''',
		    '''TFBlipForConditionalGeneration''',
		    '''TFBlipForQuestionAnswering''',
		    '''TFBlipVisionModel''',
		    '''TFBlipTextModel''',
		    '''TFBlipForImageTextRetrieval''',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






