
"""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,
)

_SCREAMING_SNAKE_CASE	       =      logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE	       =      {"""facebook/bart-base""": BartForConditionalGeneration}
_SCREAMING_SNAKE_CASE	       =      {"""facebook/bart-base""": BartTokenizer}
def   __UpperCamelCase   (					)  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       argparse.ArgumentParser(description="Export Bart model + Beam Search to ONNX graph." )
							parser.add_argument(
							    "--validation_file"							,    type=SCREAMING_SNAKE_CASE							,    default=SCREAMING_SNAKE_CASE							,    help="A csv or a json file containing the validation data." )
							parser.add_argument(
							    "--max_length"							,    type=SCREAMING_SNAKE_CASE							,    default=5							,    help="The maximum total input sequence length after tokenization."							,    )
							parser.add_argument(
							    "--num_beams"							,    type=SCREAMING_SNAKE_CASE							,    default=SCREAMING_SNAKE_CASE							,    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=SCREAMING_SNAKE_CASE							,    help="Path to pretrained model or model identifier from huggingface.co/models."							,    required=SCREAMING_SNAKE_CASE							,    )
							parser.add_argument(
							    "--config_name"							,    type=SCREAMING_SNAKE_CASE							,    default=SCREAMING_SNAKE_CASE							,    help="Pretrained config name or path if not the same as model_name"							,    )
							parser.add_argument(
							    "--device"							,    type=SCREAMING_SNAKE_CASE							,    default="cpu"							,    help="Device where the model will be run"							,    )
							parser.add_argument("--output_file_path"							,    type=SCREAMING_SNAKE_CASE							,    default=SCREAMING_SNAKE_CASE							,    help="Where to store the final ONNX file." )

							__snake_case     			=       parser.parse_args()

							return args
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE="cpu" )  ->     int:







							"""simple docstring"""


							__snake_case     			=       model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE ).to(SCREAMING_SNAKE_CASE )
							__snake_case     			=       tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE )

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

							return huggingface_model, tokenizer
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							model.eval()

							__snake_case     			=       None
							__snake_case     			=       torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE ) )

							with torch.no_grad():
														__snake_case     			=       "My friends are cool but they eat too many carbs."
														__snake_case     			=       tokenizer([ARTICLE_TO_SUMMARIZE]							,    max_length=10_24							,    return_tensors="pt" ).to(model.device )

														__snake_case     			=       model.generate(
														    inputs["input_ids"]							,    attention_mask=inputs["attention_mask"]							,    num_beams=SCREAMING_SNAKE_CASE							,    max_length=SCREAMING_SNAKE_CASE							,    early_stopping=SCREAMING_SNAKE_CASE							,    decoder_start_token_id=model.config.decoder_start_token_id							,    )

														torch.onnx.export(
														    SCREAMING_SNAKE_CASE							,    (
														        inputs["input_ids"],
														        inputs["attention_mask"],
														        num_beams,
														        max_length,
														        model.config.decoder_start_token_id,
														    )							,    SCREAMING_SNAKE_CASE							,    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=SCREAMING_SNAKE_CASE							,    )

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

														__snake_case     			=       remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE ) )

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

														__snake_case     			=       onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE )
														__snake_case     			=       ort_sess.run(
														    SCREAMING_SNAKE_CASE							,    {
														        "input_ids": inputs["input_ids"].cpu().numpy(),
														        "attention_mask": inputs["attention_mask"].cpu().numpy(),
														        "num_beams": np.array(SCREAMING_SNAKE_CASE ),
														        "max_length": np.array(SCREAMING_SNAKE_CASE ),
														        "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   (					)  ->     Tuple:







							"""simple docstring"""


							__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							,    SCREAMING_SNAKE_CASE )

							if model.config.decoder_start_token_id is None:
														raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined" )

							model.to(SCREAMING_SNAKE_CASE )

							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(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		main()


"""simple docstring"""







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

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

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







@dataclass
class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor







class 							__magic_name__     (		lowercase__      ,		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   1





					@register_to_config
					def __init__(				self   :							List[Any]							,						snake_case_   :							int = 2000							,						snake_case_   :							float = 0.15							,						snake_case_   :							float = 0.01							,						snake_case_   :							float = 1348.0							,						snake_case_   :							float = 1e-5							,						snake_case_   :							int = 1							,						):
												# standard deviation of the initial noise distribution
												__snake_case     			=       sigma_max

												# setable values
												__snake_case     			=       None

												self.set_sigmas(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[int] = None    ):
												return sample





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							Union[str, torch.device] = None    ):
												__snake_case     			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

												__snake_case     			=       torch.linspace(1							,						snake_case_							,						snake_case_							,						device=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							float = None							,						snake_case_   :							float = None    ):
												__snake_case     			=       sigma_min if sigma_min is not None else self.config.sigma_min
												__snake_case     			=       sigma_max if sigma_max is not None else self.config.sigma_max
												__snake_case     			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if self.timesteps is None:
																			self.set_timesteps(snake_case_							,						snake_case_    )

												__snake_case     			=       sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
												__snake_case     			=       torch.exp(torch.linspace(math.log(snake_case_    )							,						math.log(snake_case_    )							,						snake_case_    )    )
												__snake_case     			=       torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							str							,						snake_case_   :							Dict    ):
												return torch.where(
												    timesteps == 0							,						torch.zeros_like(t.to(timesteps.device    )    )							,						self.discrete_sigmas[timesteps - 1].to(timesteps.device    )							,						)





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							int							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

												__snake_case     			=       timestep * torch.ones(
												    sample.shape[0]							,						device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
												__snake_case     			=       (timestep * (len(self.timesteps    ) - 1)).long()

												# mps requires indices to be in the same device, so we use cpu as is the default with cuda
												__snake_case     			=       timesteps.to(self.discrete_sigmas.device    )

												__snake_case     			=       self.discrete_sigmas[timesteps].to(sample.device    )
												__snake_case     			=       self.get_adjacent_sigma(snake_case_							,						snake_case_    ).to(sample.device    )
												__snake_case     			=       torch.zeros_like(snake_case_    )
												__snake_case     			=       (sigma**2 - adjacent_sigma**2) ** 0.5

												# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
												# also equation 47 shows the analog from SDE models to ancestral sampling methods
												__snake_case     			=       diffusion.flatten()
												while len(diffusion.shape    ) < len(sample.shape    ):
																			__snake_case     			=       diffusion.unsqueeze(-1    )
												__snake_case     			=       drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												__snake_case     			=       randn_tensor(
												    sample.shape							,						layout=sample.layout							,						generator=snake_case_							,						device=sample.device							,						dtype=sample.dtype    )
												__snake_case     			=       sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												__snake_case     			=       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

												if not return_dict:
																			return (prev_sample, prev_sample_mean)

												return SdeVeOutput(prev_sample=snake_case_							,						prev_sample_mean=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

												# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
												# sample noise for correction
												__snake_case     			=       randn_tensor(sample.shape							,						layout=sample.layout							,						generator=snake_case_    ).to(sample.device    )

												# compute step size from the model_output, the noise, and the snr
												__snake_case     			=       torch.norm(model_output.reshape(model_output.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       torch.norm(noise.reshape(noise.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
												__snake_case     			=       step_size * torch.ones(sample.shape[0]    ).to(sample.device    )
												# self.repeat_scalar(step_size, sample.shape[0])

												# compute corrected sample: model_output term and noise term
												__snake_case     			=       step_size.flatten()
												while len(step_size.shape    ) < len(sample.shape    ):
																			__snake_case     			=       step_size.unsqueeze(-1    )
												__snake_case     			=       sample + step_size * model_output
												__snake_case     			=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												__snake_case     			=       timesteps.to(original_samples.device    )
												__snake_case     			=       self.discrete_sigmas.to(original_samples.device    )[timesteps]
												__snake_case     			=       (
												    noise * sigmas[:, None, None, None]
												    if noise is not None
												    else torch.randn_like(snake_case_    ) * sigmas[:, None, None, None]
												)
												__snake_case     			=       noise + original_samples
												return noisy_samples





					def __len__(				self   :							Tuple    ):
												return self.config.num_train_timesteps
