
"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor




@require_vision
class 		a_						( unittest.TestCase	):







    def      _snake_case					(			self			:       str     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   tempfile.mkdtemp()

        _UpperCAmelCase          =   [
            """[UNK]""",
            """[CLS]""",
            """[SEP]""",
            """[PAD]""",
            """[MASK]""",
            """的""",
            """价""",
            """格""",
            """是""",
            """15""",
            """便""",
            """alex""",
            """##andra""",
            """，""",
            """。""",
            """-""",
            """t""",
            """shirt""",
        ]
        _UpperCAmelCase          =   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""vocab_file"""]     )
        with open(self.vocab_file     ,					"""w"""     ,					encoding="""utf-8"""     ) as vocab_writer:
            vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )

        _UpperCAmelCase          =   {
            """do_resize""": True,
            """size""": {"""height""": 2_24, """width""": 2_24},
            """do_center_crop""": True,
            """crop_size""": {"""height""": 18, """width""": 18},
            """do_normalize""": True,
            """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
            """do_convert_rgb""": True,
        }
        _UpperCAmelCase          =   os.path.join(self.tmpdirname     ,					__UpperCamelCase     )
        with open(self.image_processor_file     ,					"""w"""     ,					encoding="""utf-8"""     ) as fp:
            json.dump(__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     ,					**__UpperCamelCase			:       Any     )		->List[Any]:






        '''simple docstring'''






        return BertTokenizer.from_pretrained(self.tmpdirname     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       str     ,					**__UpperCamelCase			:       str     )		->Optional[int]:






        '''simple docstring'''






        return BertTokenizerFast.from_pretrained(self.tmpdirname     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       str     ,					**__UpperCamelCase			:       Any     )		->Tuple:






        '''simple docstring'''






        return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     )		->Optional[Any]:






        '''simple docstring'''






        shutil.rmtree(self.tmpdirname     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   [np.random.randint(2_55     ,					size=(3, 30, 4_00)     ,					dtype=np.uinta     )]

        _UpperCAmelCase          =   [Image.fromarray(np.moveaxis(__UpperCamelCase     ,					0     ,					-1     )     ) for x in image_inputs]

        return image_inputs







    def      _snake_case					(			self			:       Tuple     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_tokenizer()
        _UpperCAmelCase          =   self.get_rust_tokenizer()
        _UpperCAmelCase          =   self.get_image_processor()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )
        processor_slow.save_pretrained(self.tmpdirname     )
        _UpperCAmelCase          =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,					use_fast=__UpperCamelCase     )

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )
        processor_fast.save_pretrained(self.tmpdirname     )
        _UpperCAmelCase          =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname     )

        self.assertEqual(processor_slow.tokenizer.get_vocab()     ,					tokenizer_slow.get_vocab()     )
        self.assertEqual(processor_fast.tokenizer.get_vocab()     ,					tokenizer_fast.get_vocab()     )
        self.assertEqual(tokenizer_slow.get_vocab()     ,					tokenizer_fast.get_vocab()     )
        self.assertIsInstance(processor_slow.tokenizer     ,					__UpperCamelCase     )
        self.assertIsInstance(processor_fast.tokenizer     ,					__UpperCamelCase     )

        self.assertEqual(processor_slow.image_processor.to_json_string()     ,					image_processor.to_json_string()     )
        self.assertEqual(processor_fast.image_processor.to_json_string()     ,					image_processor.to_json_string()     )
        self.assertIsInstance(processor_slow.image_processor     ,					__UpperCamelCase     )
        self.assertIsInstance(processor_fast.image_processor     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       str     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     ,					image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

        _UpperCAmelCase          =   self.get_tokenizer(cls_token="""(CLS)"""     ,					sep_token="""(SEP)"""     )
        _UpperCAmelCase          =   self.get_image_processor(do_normalize=__UpperCamelCase     )

        _UpperCAmelCase          =   ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname     ,					cls_token="""(CLS)"""     ,					sep_token="""(SEP)"""     ,					do_normalize=__UpperCamelCase     )

        self.assertEqual(processor.tokenizer.get_vocab()     ,					tokenizer_add_kwargs.get_vocab()     )
        self.assertIsInstance(processor.tokenizer     ,					__UpperCamelCase     )

        self.assertEqual(processor.image_processor.to_json_string()     ,					image_processor_add_kwargs.to_json_string()     )
        self.assertIsInstance(processor.image_processor     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       str     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_image_processor()
        _UpperCAmelCase          =   self.get_tokenizer()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )

        _UpperCAmelCase          =   self.prepare_image_inputs()

        _UpperCAmelCase          =   image_processor(__UpperCamelCase     ,					return_tensors="""np"""     )
        _UpperCAmelCase          =   processor(images=__UpperCamelCase     ,					return_tensors="""np"""     )

        for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum()     ,					input_processor[key].sum()     ,					delta=1e-2     )







    def      _snake_case					(			self			:       Dict     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_image_processor()
        _UpperCAmelCase          =   self.get_tokenizer()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )

        _UpperCAmelCase          =   """Alexandra，T-shirt的价格是15便士。"""

        _UpperCAmelCase          =   processor(text=__UpperCamelCase     )

        _UpperCAmelCase          =   tokenizer(__UpperCamelCase     )

        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]     ,					encoded_processor[key]     )







    def      _snake_case					(			self			:       Any     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_image_processor()
        _UpperCAmelCase          =   self.get_tokenizer()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )

        _UpperCAmelCase          =   """Alexandra，T-shirt的价格是15便士。"""
        _UpperCAmelCase          =   self.prepare_image_inputs()

        _UpperCAmelCase          =   processor(text=__UpperCamelCase     ,					images=__UpperCamelCase     )

        self.assertListEqual(list(inputs.keys()     )     ,					["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]     )

        # test if it raises when no input is passed
        with pytest.raises(__UpperCamelCase     ):
            processor()







    def      _snake_case					(			self			:       Union[str, Any]     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_image_processor()
        _UpperCAmelCase          =   self.get_tokenizer()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )

        _UpperCAmelCase          =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCAmelCase          =   processor.batch_decode(__UpperCamelCase     )
        _UpperCAmelCase          =   tokenizer.batch_decode(__UpperCamelCase     )

        self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_image_processor()
        _UpperCAmelCase          =   self.get_tokenizer()

        _UpperCAmelCase          =   ChineseCLIPProcessor(tokenizer=__UpperCamelCase     ,					image_processor=__UpperCamelCase     )

        _UpperCAmelCase          =   """Alexandra，T-shirt的价格是15便士。"""
        _UpperCAmelCase          =   self.prepare_image_inputs()

        _UpperCAmelCase          =   processor(text=__UpperCamelCase     ,					images=__UpperCamelCase     )

        self.assertListEqual(list(inputs.keys()     )     ,					processor.model_input_names     )

"""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					:	List[str]							=		logging.getLogger(__name__)

a					:	int							=		{'''facebook/bart-base''': BartForConditionalGeneration}
a					:	Dict							=		{'''facebook/bart-base''': BartTokenizer}


def  _UpperCamelCase	(						)    ->  Optional[Any]:





    """simple docstring"""

    _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  _UpperCamelCase	(						_A					,      _A="cpu"      )    ->  Optional[Any]:





    """simple docstring"""

    _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  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  Optional[int]:





    """simple docstring"""

    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  _UpperCamelCase	(						)    ->  Union[str, Any]:





    """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					,      _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()