




'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileViTImageProcessor






class     _a						(			unittest.TestCase				):
       def __init__(			self		:			Dict     ,       lowercase		:			int     ,       lowercase		:			Optional[int]=7     ,       lowercase		:			List[Any]=3     ,       lowercase		:			Dict=18     ,       lowercase		:			Dict=30     ,       lowercase		:			Optional[int]=400     ,       lowercase		:			int=True     ,       lowercase		:			str=None     ,       lowercase		:			Dict=True     ,       lowercase		:			Tuple=None     ,       lowercase		:			List[str]=True     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	size if size is not None else {"shortest_edge": 20}
           UpperCAmelCase     =	crop_size if crop_size is not None else {"height": 18, "width": 18}
           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	num_channels
           UpperCAmelCase     =	image_size
           UpperCAmelCase     =	min_resolution
           UpperCAmelCase     =	max_resolution
           UpperCAmelCase     =	do_resize
           UpperCAmelCase     =	size
           UpperCAmelCase     =	do_center_crop
           UpperCAmelCase     =	crop_size
           UpperCAmelCase     =	do_flip_channel_order







       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_flip_channel_order": self.do_flip_channel_order,
           }






@require_torch
@require_vision
class     _a						(			_UpperCamelCase							,							unittest.TestCase				):
       __a       :  List[str]            =      MobileViTImageProcessor if is_vision_available() else None
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	MobileViTImageProcessingTester(self       )
       @property
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.image_processing_class(**self.image_processor_dict       )
           self.assertTrue(hasattr(lowercase     ,       '''do_resize'''       )       )
           self.assertTrue(hasattr(lowercase     ,       '''size'''       )       )
           self.assertTrue(hasattr(lowercase     ,       '''do_center_crop'''       )       )
           self.assertTrue(hasattr(lowercase     ,       '''center_crop'''       )       )
           self.assertTrue(hasattr(lowercase     ,       '''do_flip_channel_order'''       )       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.image_processing_class.from_dict(self.image_processor_dict       )
           self.assertEqual(image_processor.size     ,       {'''shortest_edge''': 20}       )
           self.assertEqual(image_processor.crop_size     ,       {'''height''': 18, '''width''': 18}       )

           UpperCAmelCase     =	self.image_processing_class.from_dict(self.image_processor_dict     ,       size=42     ,       crop_size=84       )
           self.assertEqual(image_processor.size     ,       {'''shortest_edge''': 42}       )
           self.assertEqual(image_processor.crop_size     ,       {'''height''': 84, '''width''': 84}       )
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.image_processing_class(**self.image_processor_dict       )
           # create random PIL images
           UpperCAmelCase     =	prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=lowercase       )
           for image in image_inputs:
               self.assertIsInstance(lowercase     ,       Image.Image       )

           # Test not batched input
           UpperCAmelCase     =	image_processing(image_inputs[0]     ,       return_tensors='''pt'''       ).pixel_values
           self.assertEqual(
               encoded_images.shape     ,       (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )     ,       )

           # Test batched
           UpperCAmelCase     =	image_processing(lowercase     ,       return_tensors='''pt'''       ).pixel_values
           self.assertEqual(
               encoded_images.shape     ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )     ,       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.image_processing_class(**self.image_processor_dict       )
           # create random numpy tensors
           UpperCAmelCase     =	prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=lowercase     ,       numpify=lowercase       )
           for image in image_inputs:
               self.assertIsInstance(lowercase     ,       np.ndarray       )

           # Test not batched input
           UpperCAmelCase     =	image_processing(image_inputs[0]     ,       return_tensors='''pt'''       ).pixel_values
           self.assertEqual(
               encoded_images.shape     ,       (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )     ,       )

           # Test batched
           UpperCAmelCase     =	image_processing(lowercase     ,       return_tensors='''pt'''       ).pixel_values
           self.assertEqual(
               encoded_images.shape     ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )     ,       )







       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.image_processing_class(**self.image_processor_dict       )
           # create random PyTorch tensors
           UpperCAmelCase     =	prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=lowercase     ,       torchify=lowercase       )
           for image in image_inputs:
               self.assertIsInstance(lowercase     ,       torch.Tensor       )

           # Test not batched input
           UpperCAmelCase     =	image_processing(image_inputs[0]     ,       return_tensors='''pt'''       ).pixel_values
           self.assertEqual(
               encoded_images.shape     ,       (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )     ,       )

           # Test batched
           UpperCAmelCase     =	image_processing(lowercase     ,       return_tensors='''pt'''       ).pixel_values
           self.assertEqual(
               encoded_images.shape     ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )     ,       )










'''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									=logging.getLogger(__name__)

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





def 		snake_case_							():
    UpperCAmelCase     =	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.'''  )

    UpperCAmelCase     =	parser.parse_args()

    return args





def 		snake_case_							(_a			:							Tuple					,     _a			:							str="cpu"  ):
    UpperCAmelCase     =	model_dict[model_name].from_pretrained(_a  ).to(_a  )
    UpperCAmelCase     =	tokenizer_dict[model_name].from_pretrained(_a  )

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

    return huggingface_model, tokenizer





def 		snake_case_							(_a			:							Optional[int]					,     _a			:							List[str]					,     _a			:							str					,     _a			:							Optional[Any]					,     _a			:							str  ):
    model.eval()

    UpperCAmelCase     =	None
    UpperCAmelCase     =	torch.jit.script(BARTBeamSearchGenerator(_a  )  )

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

        UpperCAmelCase     =	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=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=_a					,     )

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

        UpperCAmelCase     =	remove_dup_initializers(os.path.abspath(_a  )  )

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

        UpperCAmelCase     =	onnxruntime.InferenceSession(_a  )
        UpperCAmelCase     =	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 		snake_case_							():
    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					,     _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:
        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(_a					,     _a					,     _a					,     _a					,     _a  )


if __name__ == "__main__":
   main()




