
"""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,
)

UpperCamelCase_					:						str				= logging.getLogger(__name__)

UpperCamelCase_					:						List[Any]				= {'''facebook/bart-base''': BartForConditionalGeneration}
UpperCamelCase_					:						Optional[Any]				= {'''facebook/bart-base''': BartTokenizer}
def 							A_     ():

					'''simple docstring'''





					A_				 =						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."				)

					A_				 =						parser.parse_args()

					return args
def 							A_     (__a				,   __a="cpu"				):

					'''simple docstring'''





					A_				 =						model_dict[model_name].from_pretrained(__a				).to(__a				)
					A_				 =						tokenizer_dict[model_name].from_pretrained(__a				)

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

					return huggingface_model, tokenizer
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					model.eval()

					A_				 =						None
					A_				 =						torch.jit.script(BARTBeamSearchGenerator(__a				)				)

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

										A_				 =						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=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=__a				,   )

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

										A_				 =						remove_dup_initializers(os.path.abspath(__a				)				)

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

										A_				 =						onnxruntime.InferenceSession(__a				)
										A_				 =						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 							A_     ():

					'''simple docstring'''





					A_				 =						parse_args()
					A_				 =						5
					A_				 =						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()

					A_				 =						torch.device(args.device				)

					A_ ,    A_				 =						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:
										A_				 =						args.max_length

					if args.num_beams:
										A_				 =						args.num_beams

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

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


if __name__ == "__main__":
		main()






"""simple docstring"""





from __future__ import annotations

import math

UpperCamelCase_					:						List[str]				= '''2020.9.26'''
UpperCamelCase_					:						List[Any]				= '''xcodz-dot, cclaus, dhruvmanila'''
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not all(isinstance(__a				,   (float, int)				) for val in locals().values()				):
										A_				 =						f'Input values must either be float or int: {list(locals().values()				)}'
										raise TypeError(__a				)
					A_				 =						((x * distance) / (z + distance)) * scale
					A_				 =						((y * distance) / (z + distance)) * scale
					return projected_x, projected_y
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not isinstance(__a				,   __a				):
										raise TypeError("Axis must be a str"				)
					A_				 =						locals()
					del input_variables["axis"]
					if not all(isinstance(__a				,   (float, int)				) for val in input_variables.values()				):
										A_				 =						(
										    "Input values except axis must either be float or int: "
										    f'{list(input_variables.values()				)}'
										)
										raise TypeError(__a				)
					A_				 =						(angle % 360) / 450 * 180 / math.pi
					if axis == "z":
										A_				 =						x * math.cos(__a				) - y * math.sin(__a				)
										A_				 =						y * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						z
					elif axis == "x":
										A_				 =						y * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + y * math.sin(__a				)
										A_				 =						x
					elif axis == "y":
										A_				 =						x * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						y
					else:
										raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'"				)

					return new_x, new_y, new_z


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
		print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")




