
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	1






	@register_to_config
	def __init__(			self   :					List[Any]				,A   :					str=20_00				,A   :					str=0.1				,A   :					List[str]=20				,A   :					int=1E-3					):
					__A       =					None
					__A       =					None
					__A       =					None






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					List[str]				,A   :					Union[str, torch.device] = None					):
					__A       =					torch.linspace(1				,self.config.sampling_eps				,A				,device=A					)






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					List[Any]				,A   :					Optional[Any]				,A   :					Union[str, Any]				,A   :					Union[str, Any]=None					):
					if self.timesteps is None:
									raise ValueError(
									    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

					# TODO(Patrick) better comments + non-PyTorch
					# postprocess model score
					__A       =					(
					    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
					)
					__A       =					torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
					__A       =					std.flatten()
					while len(std.shape					) < len(score.shape					):
									__A       =					std.unsqueeze(-1					)
					__A       =					-score / std

					# compute
					__A       =					-1.0 / len(self.timesteps					)

					__A       =					self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
					__A       =					beta_t.flatten()
					while len(beta_t.shape					) < len(x.shape					):
									__A       =					beta_t.unsqueeze(-1					)
					__A       =					-0.5 * beta_t * x

					__A       =					torch.sqrt(A					)
					__A       =					drift - diffusion**2 * score
					__A       =					x + drift * dt

					# add noise
					__A       =					randn_tensor(x.shape				,layout=x.layout				,generator=A				,device=x.device				,dtype=x.dtype					)
					__A       =					x_mean + diffusion * math.sqrt(-dt					) * noise

					return x, x_mean







	def __len__(			self   :					Optional[Any]					):
					return self.config.num_train_timesteps


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,
)

__magic_name__  							=	logging.getLogger(__name__)

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





def 						SCREAMING_SNAKE_CASE__     (   ):
    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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase="cpu"				):
    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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    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=1_024     ,		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 						SCREAMING_SNAKE_CASE__     (   ):
    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()



