
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,
)

_lowerCamelCase       :				Optional[Any]       =			logging.getLogger(__name__)

_lowerCamelCase       :				Tuple       =			{'''facebook/bart-base''': BartForConditionalGeneration}
_lowerCamelCase       :				Any       =			{'''facebook/bart-base''': BartTokenizer}






def        a_		(       ) ->  Union[str, Any]:
			_snake_case		=			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.' )

			_snake_case		=			parser.parse_args()

			return args






def        a_		(       __lowercase   :	Union[str, Any]       ,					__lowercase   :	List[str]="cpu" ) ->  Any:
			_snake_case		=			model_dict[model_name].from_pretrained(__lowercase ).to(__lowercase )
			_snake_case		=			tokenizer_dict[model_name].from_pretrained(__lowercase )

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

			return huggingface_model, tokenizer






def        a_		(       __lowercase   :	Union[str, Any]       ,					__lowercase   :	int       ,					__lowercase   :	str       ,					__lowercase   :	Optional[Any]       ,					__lowercase   :	int ) ->  Any:
			model.eval()

			_snake_case		=			None
			_snake_case		=			torch.jit.script(BARTBeamSearchGenerator(__lowercase ) )

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

						_snake_case		=			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 ) )

						_snake_case		=			remove_dup_initializers(os.path.abspath(__lowercase ) )

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

						_snake_case		=			onnxruntime.InferenceSession(__lowercase )
						_snake_case		=			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_		(       ) ->  Union[str, Any]:
			_snake_case		=			parse_args()
			_snake_case		=			5
			_snake_case		=			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()

			_snake_case		=			torch.device(args.device )

			_snake_case	,					_snake_case		=			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:
						_snake_case		=			args.max_length

			if args.num_beams:
						_snake_case		=			args.num_beams

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

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


if __name__ == "__main__":
			main()

import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






def        a_		(       __lowercase   :	Dict ) ->  List[Any]:
			_snake_case		=			args.pruning_method
			_snake_case		=			args.threshold

			_snake_case		=			args.model_name_or_path.rstrip('/' )
			_snake_case		=			args.target_model_path

			print(f'''Load fine-pruned model from {model_name_or_path}''' )
			_snake_case		=			torch.load(os.path.join(__lowercase       ,					'pytorch_model.bin' ) )
			_snake_case		=			{}

			for name, tensor in model.items():
						if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						elif "classifier" in name or "qa_output" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						elif "bias" in name:
									_snake_case		=			tensor
									print(f'''Copied layer {name}''' )
						else:
									if pruning_method == "magnitude":
												_snake_case		=			MagnitudeBinarizer.apply(inputs=__lowercase       ,					threshold=__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "topK":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case		=			TopKBinarizer.apply(__lowercase       ,					__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "sigmoied_threshold":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case		=			ThresholdBinarizer.apply(__lowercase       ,					__lowercase       ,					__lowercase )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									elif pruning_method == "l0":
												if "mask_scores" in name:
															continue
												_snake_case		=			name[:-6]
												_snake_case		=			model[f'''{prefix_}mask_scores''']
												_snake_case	,					_snake_case		=			-0.1, 1.1
												_snake_case		=			torch.sigmoid(__lowercase )
												_snake_case		=			s * (r - l) + l
												_snake_case		=			s_bar.clamp(min=0.0       ,					max=1.0 )
												_snake_case		=			tensor * mask
												print(f'''Pruned layer {name}''' )
									else:
												raise ValueError('Unknown pruning method' )

			if target_model_path is None:
						_snake_case		=			os.path.join(
						    os.path.dirname(__lowercase )       ,					f'''bertarized_{os.path.basename(__lowercase )}''' )

			if not os.path.isdir(__lowercase ):
						shutil.copytree(__lowercase       ,					__lowercase )
						print(f'''\nCreated folder {target_model_path}''' )

			torch.save(__lowercase       ,					os.path.join(__lowercase       ,					'pytorch_model.bin' ) )
			print('\nPruned model saved! See you later!' )


if __name__ == "__main__":
			_lowerCamelCase       :				Dict       =			argparse.ArgumentParser()

			parser.add_argument(
			    '''--pruning_method''',
			    choices=['''l0''', '''magnitude''', '''topK''', '''sigmoied_threshold'''],
			    type=str,
			    required=True,
			    help=(
			        '''Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'''
			        ''' sigmoied_threshold = Soft movement pruning)'''
			    ),
			)
			parser.add_argument(
			    '''--threshold''',
			    type=float,
			    required=False,
			    help=(
			        '''For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
			        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
			        '''Not needed for `l0`'''
			    ),
			)
			parser.add_argument(
			    '''--model_name_or_path''',
			    type=str,
			    required=True,
			    help='''Folder containing the model that was previously fine-pruned''',
			)
			parser.add_argument(
			    '''--target_model_path''',
			    default=None,
			    type=str,
			    required=False,
			    help='''Folder containing the model that was previously fine-pruned''',
			)

			_lowerCamelCase       :				int       =			parser.parse_args()

			main(args)