




'''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,
)

a    :							Dict										=    logging.getLogger(__name__)

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


def 						__magic_name__       (      )						->   Union[str, Any]:

					'''simple docstring'''

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

					snake_case_		     =							parser.parse_args()

					return args


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase="cpu"      )						->   Union[str, Any]:

					'''simple docstring'''

					snake_case_		     =							model_dict[model_name].from_pretrained(lowerCamelCase__      ).to(lowerCamelCase__      )
					snake_case_		     =							tokenizer_dict[model_name].from_pretrained(lowerCamelCase__      )

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

					return huggingface_model, tokenizer


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Any:

					'''simple docstring'''

					model.eval()

					snake_case_		     =							None
					snake_case_		     =							torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase__      )      )

					with torch.no_grad():
										snake_case_		     =							'''My friends are cool but they eat too many carbs.'''
										snake_case_		     =							tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='''pt'''      ).to(model.device      )

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

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

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

										snake_case_		     =							remove_dup_initializers(os.path.abspath(lowerCamelCase__      )      )

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

										snake_case_		     =							onnxruntime.InferenceSession(lowerCamelCase__      )
										snake_case_		     =							ort_sess.run(
										    lowerCamelCase__, {
										        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
										        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
										        '''num_beams''': np.array(lowerCamelCase__      ),
										        '''max_length''': np.array(lowerCamelCase__      ),
										        '''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 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					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, lowerCamelCase__      )

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

					model.to(lowerCamelCase__      )

					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(lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__      )


if __name__ == "__main__":
							main()


"""simple docstring"""




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowerCAmelCase     :				int    				=    logging.get_logger(__name__)  # pylint: disable=invalid-name

class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  Optional[int]	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=768  ):
							super().__init__(snake_case__  )
							lowerCAmelCase__   = proj_size

							lowerCAmelCase__   = CLIPVisionModel(snake_case__  )
							lowerCAmelCase__   = PaintByExampleMapper(snake_case__  )
							lowerCAmelCase__   = nn.LayerNorm(config.hidden_size  )
							lowerCAmelCase__   = nn.Linear(config.hidden_size	,    self.proj_size  )

							# uncondition for scaling
							lowerCAmelCase__   = nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  Optional[int]	,    snake_case__    :  Optional[Any]=False  ):
							lowerCAmelCase__   = self.model(pixel_values=snake_case__  )
							lowerCAmelCase__   = clip_output.pooler_output
							lowerCAmelCase__   = self.mapper(latent_states[:, None]  )
							lowerCAmelCase__   = self.final_layer_norm(snake_case__  )
							lowerCAmelCase__   = self.proj_out(snake_case__  )
							if return_uncond_vector:
									return latent_states, self.uncond_vector

							return latent_states

class    a_   (  nn.Module      ):




					def __init__(						self    :  int	,    snake_case__    :  Dict  ):
							super().__init__()
							lowerCAmelCase__   = (config.num_hidden_layers + 1) // 5
							lowerCAmelCase__   = config.hidden_size
							lowerCAmelCase__   = 1
							lowerCAmelCase__   = nn.ModuleList(
							    [
							        BasicTransformerBlock(snake_case__	,    snake_case__	,    snake_case__	,    activation_fn="""gelu"""	,    attention_bias=snake_case__  )
							        for _ in range(snake_case__  )
							    ]  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Optional[int]  ):
							for block in self.blocks:
									lowerCAmelCase__   = block(snake_case__  )

							return hidden_states




