
"""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"""







from __future__ import annotations

_SCREAMING_SNAKE_CASE	       =      {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}







class 							__magic_name__     :





					def __init__(				self   :							Any							,						snake_case_   :							dict[str, list[str]]							,						snake_case_   :							str    ):
												__snake_case     			=       graph
												# mapping node to its parent in resulting breadth first tree
												__snake_case     			=       {}
												__snake_case     			=       source_vertex





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       {self.source_vertex}
												__snake_case     			=       None
												__snake_case     			=       [self.source_vertex]  # first in first out queue

												while queue:
																			__snake_case     			=       queue.pop(0    )
																			for adjacent_vertex in self.graph[vertex]:
																										if adjacent_vertex not in visited:
																																	visited.add(snake_case_    )
																																	__snake_case     			=       vertex
																																	queue.append(snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							str    ):
												if target_vertex == self.source_vertex:
																			return self.source_vertex

												__snake_case     			=       self.parent.get(snake_case_    )
												if target_vertex_parent is None:
																			__snake_case     			=       (
																			    F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
																			)
																			raise ValueError(snake_case_    )

												return self.shortest_path(snake_case_    ) + F'''->{target_vertex}'''


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      Graph(graph, """G""")
		g.breath_first_search()
		print(g.shortest_path("""D"""))
		print(g.shortest_path("""G"""))
		print(g.shortest_path("""Foo"""))
