






'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					"beit"






			def __init__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      List[Any]=8192	,    __SCREAMING_SNAKE_CASE   :      str=768	,    __SCREAMING_SNAKE_CASE   :      List[str]=12	,    __SCREAMING_SNAKE_CASE   :      str=12	,    __SCREAMING_SNAKE_CASE   :      Dict=3072	,    __SCREAMING_SNAKE_CASE   :      List[Any]="gelu"	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=0.0	,    __SCREAMING_SNAKE_CASE   :      Any=0.0	,    __SCREAMING_SNAKE_CASE   :      Tuple=0.02	,    __SCREAMING_SNAKE_CASE   :      List[str]=1e-12	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=224	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=16	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=3	,    __SCREAMING_SNAKE_CASE   :      Dict=False	,    __SCREAMING_SNAKE_CASE   :      int=False	,    __SCREAMING_SNAKE_CASE   :      Any=False	,    __SCREAMING_SNAKE_CASE   :      int=False	,    __SCREAMING_SNAKE_CASE   :      List[str]=0.1	,    __SCREAMING_SNAKE_CASE   :      Any=0.1	,    __SCREAMING_SNAKE_CASE   :      Any=True	,    __SCREAMING_SNAKE_CASE   :      List[str]=[3, 5, 7, 11]	,    __SCREAMING_SNAKE_CASE   :      Tuple=[1, 2, 3, 6]	,    __SCREAMING_SNAKE_CASE   :      str=True	,    __SCREAMING_SNAKE_CASE   :      List[Any]=0.4	,    __SCREAMING_SNAKE_CASE   :      int=256	,    __SCREAMING_SNAKE_CASE   :      Dict=1	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=False	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=255	,    **__SCREAMING_SNAKE_CASE   :      Optional[int]	,    )      ->      str:
							super().__init__(**__SCREAMING_SNAKE_CASE       )

							a_		:	Dict									=   vocab_size
							a_		:	Tuple									=   hidden_size
							a_		:	Any									=   num_hidden_layers
							a_		:	Dict									=   num_attention_heads
							a_		:	Union[str, Any]									=   intermediate_size
							a_		:	Union[str, Any]									=   hidden_act
							a_		:	Union[str, Any]									=   hidden_dropout_prob
							a_		:	Optional[Any]									=   attention_probs_dropout_prob
							a_		:	Dict									=   initializer_range
							a_		:	Optional[int]									=   layer_norm_eps

							a_		:	Any									=   image_size
							a_		:	List[str]									=   patch_size
							a_		:	List[Any]									=   num_channels
							a_		:	Optional[Any]									=   use_mask_token
							a_		:	List[Any]									=   use_absolute_position_embeddings
							a_		:	Dict									=   use_relative_position_bias
							a_		:	Any									=   use_shared_relative_position_bias
							a_		:	int									=   layer_scale_init_value
							a_		:	List[Any]									=   drop_path_rate
							a_		:	List[Any]									=   use_mean_pooling
							# decode head attributes (semantic segmentation)
							a_		:	Union[str, Any]									=   out_indices
							a_		:	Dict									=   pool_scales
							# auxiliary head attributes (semantic segmentation)
							a_		:	Union[str, Any]									=   use_auxiliary_head
							a_		:	Optional[Any]									=   auxiliary_loss_weight
							a_		:	Optional[int]									=   auxiliary_channels
							a_		:	int									=   auxiliary_num_convs
							a_		:	str									=   auxiliary_concat_input
							a_		:	str									=   semantic_loss_ignore_index





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					version.parse("1.11"							)






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]       )







			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      float:
							return 1e-4












'''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,
)

__lowerCAmelCase									=   logging.getLogger(__name__)

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





def 		_UpperCAmelCase (				):
				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_		:	Tuple									=   parser.parse_args()

				return args





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Dict="cpu"   ):
				a_		:	str									=   model_dict[model_name].from_pretrained(__A   ).to(__A   )
				a_		:	str									=   tokenizer_dict[model_name].from_pretrained(__A   )

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

				return huggingface_model, tokenizer





def 		_UpperCAmelCase (				__A				: int    ,		__A				: Dict    ,		__A				: str    ,		__A				: Optional[Any]    ,		__A				: Any   ):
				model.eval()

				a_		:	Union[str, Any]									=   None
				a_		:	str									=   torch.jit.script(BARTBeamSearchGenerator(__A   )   )

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

								a_		:	Dict									=   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_		:	Any									=   remove_dup_initializers(os.path.abspath(__A   )   )

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

								a_		:	int									=   onnxruntime.InferenceSession(__A   )
								a_		:	Optional[Any]									=   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 		_UpperCAmelCase (				):
				a_		:	int									=   parse_args()
				a_		:	List[str]									=   5
				a_		:	List[str]									=   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_		:	Any									=   torch.device(args.device   )

				a_					,      a_		:	Optional[Any]									=   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_		:	Dict									=   args.max_length

				if args.num_beams:
								a_		:	str									=   args.num_beams

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

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


if __name__ == "__main__":
		main()




