




import numpy as np


def 					UpperCamelCase_(     snake_case							:	np.ndarray							,    snake_case							:	np.ndarray							,    snake_case							:	float = 1e-12							,    snake_case							:	int = 1_0_0							,    ):

							'''simple docstring'''






							assert np.shape(snake_case					)[0] == np.shape(snake_case					)[1]
							# Ensure proper dimensionality.
							assert np.shape(snake_case					)[0] == np.shape(snake_case					)[0]
							# Ensure inputs are either both complex or both real
							assert np.iscomplexobj(snake_case					) == np.iscomplexobj(snake_case					)
							snake_case_      =				np.iscomplexobj(snake_case					)
							if is_complex:
														# Ensure complex input_matrix is Hermitian
														assert np.array_equal(snake_case							,    input_matrix.conj().T					)

							# Set convergence to False. Will define convergence when we exceed max_iterations
							# or when we have small changes from one iteration to next.

							snake_case_      =				False
							snake_case_      =				0
							snake_case_      =				0
							snake_case_      =				1e12

							while not convergence:
														# Multiple matrix by the vector.
														snake_case_      =				np.dot(snake_case							,    snake_case					)
														# Normalize the resulting output vector.
														snake_case_      =				w / np.linalg.norm(snake_case					)
														# Find rayleigh quotient
														# (faster than usual b/c we know vector is normalized already)
														snake_case_      =				vector.conj().T if is_complex else vector.T
														snake_case_      =				np.dot(snake_case							,    np.dot(snake_case							,    snake_case					)					)

														# Check convergence.
														snake_case_      =				np.abs(lambda_ - lambda_previous					) / lambda_
														iterations += 1

														if error <= error_tol or iterations >= max_iterations:
																					snake_case_      =				True

														snake_case_      =				lambda_

							if is_complex:
														snake_case_      =				np.real(lambda_					)

							return lambda_, vector


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]					)
							snake_case_      =				np.array([4_1, 4, 2_0]					)
							snake_case_      =				real_input_matrix.astype(np.complexaaa					)
							snake_case_      =				np.triu(1j * complex_input_matrix							,    1					)
							complex_input_matrix += imag_matrix
							complex_input_matrix += -1 * imag_matrix.T
							snake_case_      =				np.array([4_1, 4, 2_0]					).astype(np.complexaaa					)

							for problem_type in ["real", "complex"]:
														if problem_type == "real":
																					snake_case_      =				real_input_matrix
																					snake_case_      =				real_vector
														elif problem_type == "complex":
																					snake_case_      =				complex_input_matrix
																					snake_case_      =				complex_vector

														# Our implementation.
														snake_case_ ,				snake_case_      =				power_iteration(snake_case							,    snake_case					)

														# Numpy implementation.

														# Get eigenvalues and eigenvectors using built-in numpy
														# eigh (eigh used for symmetric or hermetian matrices).
														snake_case_ ,				snake_case_      =				np.linalg.eigh(snake_case					)
														# Last eigenvalue is the maximum one.
														snake_case_      =				eigen_values[-1]
														# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
														snake_case_      =				eigen_vectors[:, -1]

														# Check our implementation and numpy gives close answers.
														assert np.abs(eigen_value - eigen_value_max					) <= 1e-6
														# Take absolute values element wise of each eigenvector.
														# as they are only unique to a minus sign.
														assert np.linalg.norm(np.abs(snake_case					) - np.abs(snake_case					)					) <= 1e-6


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		test_power_iteration()










'''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							:       Dict						=			logging.getLogger(__name__)

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


def 					UpperCamelCase_(     ):

							'''simple docstring'''






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

							snake_case_      =				parser.parse_args()

							return args


def 					UpperCamelCase_(     snake_case							:	Union[str, Any]							,    snake_case							:	List[str]="cpu"					):

							'''simple docstring'''






							snake_case_      =				model_dict[model_name].from_pretrained(snake_case					).to(snake_case					)
							snake_case_      =				tokenizer_dict[model_name].from_pretrained(snake_case					)

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

							return huggingface_model, tokenizer


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	Tuple							,    snake_case							:	Optional[int]							,    snake_case							:	Optional[Any]							,    snake_case							:	Optional[Any]					):

							'''simple docstring'''






							model.eval()

							snake_case_      =				None
							snake_case_      =				torch.jit.script(BARTBeamSearchGenerator(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=1_0_2_4							,    return_tensors="pt"					).to(model.device					)

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

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

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

														snake_case_      =				remove_dup_initializers(os.path.abspath(snake_case					)					)

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

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

							'''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							,    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(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(snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case					)


if __name__ == "__main__":
		main()




