
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      :							Optional[int]     	=    logging.getLogger(__name__)

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

def  __snake_case							(	):







				'''simple docstring'''


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

				lowercase         =      parser.parse_args()

				return args

def  __snake_case							(	__magic_name__		,    __magic_name__="cpu"						):







				'''simple docstring'''


				lowercase         =      model_dict[model_name].from_pretrained(__magic_name__						).to(__magic_name__						)
				lowercase         =      tokenizer_dict[model_name].from_pretrained(__magic_name__						)

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

				return huggingface_model, tokenizer

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				model.eval()

				lowercase         =      None
				lowercase         =      torch.jit.script(BARTBeamSearchGenerator(__magic_name__						)						)

				with torch.no_grad():
								lowercase         =      "My friends are cool but they eat too many carbs."
								lowercase         =      tokenizer([ARTICLE_TO_SUMMARIZE]		,    max_length=1024		,    return_tensors="pt"						).to(model.device						)

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

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

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

								lowercase         =      remove_dup_initializers(os.path.abspath(__magic_name__						)						)

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

								lowercase         =      onnxruntime.InferenceSession(__magic_name__						)
								lowercase         =      ort_sess.run(
								    __magic_name__		,    {
								        "input_ids": inputs["input_ids"].cpu().numpy(),
								        "attention_mask": inputs["attention_mask"].cpu().numpy(),
								        "num_beams": np.array(__magic_name__						),
								        "max_length": np.array(__magic_name__						),
								        "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							(	):







				'''simple docstring'''


				lowercase         =      parse_args()
				lowercase         =      5
				lowercase         =      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()

				lowercase         =      torch.device(args.device						)

				lowercase     ,	lowercase         =      load_model_tokenizer(args.model_name_or_path		,    __magic_name__						)

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

				model.to(__magic_name__						)

				if args.max_length:
								lowercase         =      args.max_length

				if args.num_beams:
								lowercase         =      args.num_beams

				if args.output_file_path:
								lowercase         =      args.output_file_path
				else:
								lowercase         =      "BART.onnx"

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


if __name__ == "__main__":
						main()


import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
						raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
_snake_case      :							Any     	=    False
_snake_case      :							int     	=    False

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				return TrainCommand(__magic_name__						)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@staticmethod
						def        SCREAMING_SNAKE_CASE(			lowerCAmelCase__							:ArgumentParser     )  ->str:
										lowercase         =      parser.add_parser("train"		,    help="CLI tool to train a model on a task."     )

										train_parser.add_argument(
										    "--train_data"		,    type=lowerCAmelCase__		,    required=lowerCAmelCase__		,    help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."		,    )
										train_parser.add_argument(
										    "--column_label"		,    type=lowerCAmelCase__		,    default=0		,    help="Column of the dataset csv file with example labels."     )
										train_parser.add_argument(
										    "--column_text"		,    type=lowerCAmelCase__		,    default=1		,    help="Column of the dataset csv file with example texts."     )
										train_parser.add_argument(
										    "--column_id"		,    type=lowerCAmelCase__		,    default=2		,    help="Column of the dataset csv file with example ids."     )
										train_parser.add_argument(
										    "--skip_first_row"		,    action="store_true"		,    help="Skip the first row of the csv file (headers)."     )

										train_parser.add_argument("--validation_data"		,    type=lowerCAmelCase__		,    default=""		,    help="path to validation dataset."     )
										train_parser.add_argument(
										    "--validation_split"		,    type=lowerCAmelCase__		,    default=0.1		,    help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."		,    )

										train_parser.add_argument("--output"		,    type=lowerCAmelCase__		,    default="./"		,    help="path to saved the trained model."     )

										train_parser.add_argument(
										    "--task"		,    type=lowerCAmelCase__		,    default="text_classification"		,    help="Task to train the model on."     )
										train_parser.add_argument(
										    "--model"		,    type=lowerCAmelCase__		,    default="bert-base-uncased"		,    help="Model's name or path to stored model."     )
										train_parser.add_argument("--train_batch_size"		,    type=lowerCAmelCase__		,    default=32		,    help="Batch size for training."     )
										train_parser.add_argument("--valid_batch_size"		,    type=lowerCAmelCase__		,    default=64		,    help="Batch size for validation."     )
										train_parser.add_argument("--learning_rate"		,    type=lowerCAmelCase__		,    default=3E-5		,    help="Learning rate."     )
										train_parser.add_argument("--adam_epsilon"		,    type=lowerCAmelCase__		,    default=1E-0_8		,    help="Epsilon for Adam optimizer."     )
										train_parser.set_defaults(func=lowerCAmelCase__     )





						def __init__(			self							:Optional[int]		,    lowerCAmelCase__							:Namespace     )  ->str:
										lowercase         =      logging.get_logger("transformers-cli/training"     )

										lowercase         =      "tf" if is_tf_available() else "torch"

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase__     )
										lowercase         =      args.output

										lowercase         =      args.column_label
										lowercase         =      args.column_text
										lowercase         =      args.column_id

										self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''     )
										if args.task == "text_classification":
														lowercase         =      TextClassificationPipeline.from_pretrained(args.model     )
										elif args.task == "token_classification":
														raise NotImplementedError
										elif args.task == "question_answering":
														raise NotImplementedError

										self.logger.info(F'''Loading dataset from {args.train_data}'''     )
										lowercase         =      Processor.create_from_csv(
										    args.train_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )
										lowercase         =      None
										if args.validation_data:
														self.logger.info(F'''Loading validation dataset from {args.validation_data}'''     )
														lowercase         =      Processor.create_from_csv(
														    args.validation_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )

										lowercase         =      args.validation_split
										lowercase         =      args.train_batch_size
										lowercase         =      args.valid_batch_size
										lowercase         =      args.learning_rate
										lowercase         =      args.adam_epsilon





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->List[str]:
										if self.framework == "tf":
														return self.run_tf()
										return self.run_torch()





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Dict:
										raise NotImplementedError





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										self.pipeline.fit(
										    self.train_dataset		,    validation_data=self.valid_dataset		,    validation_split=self.validation_split		,    learning_rate=self.learning_rate		,    adam_epsilon=self.adam_epsilon		,    train_batch_size=self.train_batch_size		,    valid_batch_size=self.valid_batch_size		,    )

										# Save trained pipeline
										self.pipeline.save_pretrained(self.output     )
