


"""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,
)

_snake_case					:		Dict      =    logging.getLogger(__name__)

_snake_case					:		List[Any]      =    {"facebook/bart-base": BartForConditionalGeneration}
_snake_case					:		List[str]      =    {"facebook/bart-base": BartTokenizer}







def     lowerCAmelCase_					( ):
							__snake_case		:      Tuple										=       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		:      Optional[int]										=       parser.parse_args()

							return args







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase="cpu"     ):
							__snake_case		:      str										=       model_dict[model_name].from_pretrained(__lowerCamelCase     ).to(__lowerCamelCase     )
							__snake_case		:      int										=       tokenizer_dict[model_name].from_pretrained(__lowerCamelCase     )

							if model_name in ["facebook/bart-base"]:
														__snake_case		:      Any										=       0
														__snake_case		:      Any										=       None
														__snake_case		:      List[Any]										=       0

							return huggingface_model, tokenizer







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							model.eval()

							__snake_case		:      Dict										=       None
							__snake_case		:      Dict										=       torch.jit.script(BARTBeamSearchGenerator(__lowerCamelCase     )     )

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

														__snake_case		:      Optional[int]										=       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=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=__lowerCamelCase							,     )

														logger.info("Model exported to {}".format(__lowerCamelCase     )     )

														__snake_case		:      Union[str, Any]										=       remove_dup_initializers(os.path.abspath(__lowerCamelCase     )     )

														logger.info("Deduplicated and optimized model written to {}".format(__lowerCamelCase     )     )

														__snake_case		:      Dict										=       onnxruntime.InferenceSession(__lowerCamelCase     )
														__snake_case		:      Any										=       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     lowerCAmelCase_					( ):
							__snake_case		:      Union[str, Any]										=       parse_args()
							__snake_case		:      List[str]										=       5
							__snake_case		:      Optional[int]										=       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		:      Any										=       torch.device(args.device     )

							__snake_case		:      str										=       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		:      int										=       args.max_length

							if args.num_beams:
														__snake_case		:      Tuple										=       args.num_beams

							if args.output_file_path:
														__snake_case		:      List[Any]										=       args.output_file_path
							else:
														__snake_case		:      Union[str, Any]										=       "BART.onnx"

							logger.info("Exporting model to ONNX"     )
							export_and_validate_model(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )


if __name__ == "__main__":
			main()





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

_snake_case					:		Optional[int]      =    logging.get_logger(__name__)

@add_end_docstrings(_lowerCAmelCase				)
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     *lowerCamelCase	:  Any   ,     **lowerCamelCase	:  Tuple    )     ->   int:
														super().__init__(*lowerCamelCase   ,     **lowerCamelCase    )
														requires_backends(self   ,     "vision"    )
														self.check_model_type(
														    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
														    if self.framework == "tf"
														    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING    )

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[Any]=None    )     ->   Optional[int]:
														__snake_case		:      Optional[Any]										=       {}
														if top_k is not None:
																					__snake_case		:      List[Any]										=       top_k
														return {}, {}, postprocess_params

							def __call__(							self	:  List[Any]   ,     lowerCamelCase	:  Union[str, List[str], "Image.Image", List["Image.Image"]]   ,     **lowerCamelCase	:  Dict    )     ->   Optional[int]:
														return super().__call__(lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  List[Any]    )     ->   int:
														__snake_case		:      Any										=       load_image(lowerCamelCase    )
														__snake_case		:      str										=       self.image_processor(images=lowerCamelCase   ,     return_tensors=self.framework    )
														return model_inputs

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  List[str]    )     ->   Tuple:
														__snake_case		:      List[Any]										=       self.model(**lowerCamelCase    )
														return model_outputs






							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Optional[Any]=5    )     ->   List[str]:
														if top_k > self.model.config.num_labels:
																					__snake_case		:      int										=       self.model.config.num_labels

														if self.framework == "pt":
																					__snake_case		:      Optional[Any]										=       model_outputs.logits.softmax(-1    )[0]
																					__snake_case			,      __snake_case		:      List[str]										=       probs.topk(lowerCamelCase    )
														elif self.framework == "tf":
																					__snake_case		:      Tuple										=       stable_softmax(model_outputs.logits   ,     axis=-1    )[0]
																					__snake_case		:      Optional[Any]										=       tf.math.top_k(lowerCamelCase   ,     k=lowerCamelCase    )
																					__snake_case			,      __snake_case		:      List[Any]										=       topk.values.numpy(), topk.indices.numpy()
														else:
																					raise ValueError(F'Unsupported framework: {self.framework}'    )

														__snake_case		:      Any										=       scores.tolist()
														__snake_case		:      Optional[int]										=       ids.tolist()
														return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase   ,     lowerCamelCase    )]

