





'''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,
)

lowercase__		:     List[Any]			=							logging.getLogger(__name__)

lowercase__		:     List[str]			=							{'facebook/bart-base': BartForConditionalGeneration}
lowercase__		:     List[str]			=							{'facebook/bart-base': BartTokenizer}







def    a__							(							)						->						Tuple:





     """simple docstring"""

     _UpperCamelCase							       =			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.''' )

     _UpperCamelCase							       =			parser.parse_args()

     return args







def    a__							(							lowercase   :	List[str],   lowercase   :	str="cpu" )						->						List[str]:





     """simple docstring"""

     _UpperCamelCase							       =			model_dict[model_name].from_pretrained(lowercase ).to(lowercase )
     _UpperCamelCase							       =			tokenizer_dict[model_name].from_pretrained(lowercase )

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

     return huggingface_model, tokenizer







def    a__							(							lowercase   :	Any,   lowercase   :	List[Any],   lowercase   :	Tuple,   lowercase   :	Union[str, Any],   lowercase   :	Optional[Any] )						->						Union[str, Any]:





     """simple docstring"""

     model.eval()

     _UpperCamelCase							       =			None
     _UpperCamelCase							       =			torch.jit.script(BARTBeamSearchGenerator(lowercase ) )

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

          _UpperCamelCase							       =			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 ) )

          _UpperCamelCase							       =			remove_dup_initializers(os.path.abspath(lowercase ) )

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

          _UpperCamelCase							       =			onnxruntime.InferenceSession(lowercase )
          _UpperCamelCase							       =			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__							(							)						->						int:





     """simple docstring"""

     _UpperCamelCase							       =			parse_args()
     _UpperCamelCase							       =			5
     _UpperCamelCase							       =			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()

     _UpperCamelCase							       =			torch.device(args.device )

     _UpperCamelCase       ,							_UpperCamelCase							       =			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:
          _UpperCamelCase							       =			args.max_length

     if args.num_beams:
          _UpperCamelCase							       =			args.num_beams

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

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


if __name__ == "__main__":
   main()










'''simple docstring'''

import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase__		:     int			=							logging.get_logger(__name__)

lowercase__		:     Any			=							'T5Config'







def    a__							(							lowercase   :	jnp.array,   lowercase   :	int,   lowercase   :	int )						->						jnp.ndarray:





     """simple docstring"""

     _UpperCamelCase							       =			jnp.zeros_like(lowercase )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 0].set(lowercase )

     _UpperCamelCase							       =			jnp.where(shifted_input_ids == -100,   lowercase,   lowercase )
     return shifted_input_ids
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'mt5'
       _snake_case      :	Union[str, Any]									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Tuple									=       'mt5'
       _snake_case      :	int									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[int]									=       'mt5'
       _snake_case      :	Optional[Any]									=       MTaConfig



