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,
)

A   :     List[Any]       =				logging.getLogger(__name__)

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


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

      SCREAMING_SNAKE_CASE_      =     parser.parse_args()

      return args


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase="cpu" ):
      SCREAMING_SNAKE_CASE_      =     model_dict[model_name].from_pretrained(__UpperCamelCase ).to(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     tokenizer_dict[model_name].from_pretrained(__UpperCamelCase )

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

      return huggingface_model, tokenizer


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      model.eval()

      SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     torch.jit.script(BARTBeamSearchGenerator(__UpperCamelCase ) )

      with torch.no_grad():
            SCREAMING_SNAKE_CASE_      =     """My friends are cool but they eat too many carbs."""
            SCREAMING_SNAKE_CASE_      =     tokenizer([ARTICLE_TO_SUMMARIZE]   ,					max_length=1_0_2_4   ,					return_tensors="pt" ).to(model.device )

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

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

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

            SCREAMING_SNAKE_CASE_      =     remove_dup_initializers(os.path.abspath(__UpperCamelCase ) )

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

            SCREAMING_SNAKE_CASE_      =     onnxruntime.InferenceSession(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     ort_sess.run(
                __UpperCamelCase   ,					{
                    "input_ids": inputs["input_ids"].cpu().numpy(),
                    "attention_mask": inputs["attention_mask"].cpu().numpy(),
                    "num_beams": np.array(__UpperCamelCase ),
                    "max_length": np.array(__UpperCamelCase ),
                    "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__       (						):
      SCREAMING_SNAKE_CASE_      =     parse_args()
      SCREAMING_SNAKE_CASE_      =     5
      SCREAMING_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()

      SCREAMING_SNAKE_CASE_      =     torch.device(args.device )

      SCREAMING_SNAKE_CASE_      =     load_model_tokenizer(args.model_name_or_path   ,					__UpperCamelCase )

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

      model.to(__UpperCamelCase )

      if args.max_length:
            SCREAMING_SNAKE_CASE_      =     args.max_length

      if args.num_beams:
            SCREAMING_SNAKE_CASE_      =     args.num_beams

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

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


if __name__ == "__main__":
      main()







"""simple docstring"""
from math import pi, sqrt, tan

def       _snake_case						(						UpperCamelCase						:		float						):
 if side_length < 0:
  raise ValueError("""surface_area_cube() only accepts non-negative values"""						)
 return 6 * side_length**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if length < 0 or breadth < 0 or height < 0:
  raise ValueError("""surface_area_cuboid() only accepts non-negative values"""						)
 return 2 * ((length * breadth) + (breadth * height) + (length * height))

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""surface_area_sphere() only accepts non-negative values"""						)
 return 4 * pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""surface_area_hemisphere() only accepts non-negative values"""						)
 return 3 * pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius < 0 or height < 0:
  raise ValueError("""surface_area_cone() only accepts non-negative values"""						)
 return pi * radius * (radius + (height**2 + radius**2) ** 0.5)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError(
      """surface_area_conical_frustum() only accepts non-negative values"""						)
 UpperCAmelCase	:					Tuple	=	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
 return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius < 0 or height < 0:
  raise ValueError("""surface_area_cylinder() only accepts non-negative values"""						)
 return 2 * pi * radius * (height + radius)

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if torus_radius < 0 or tube_radius < 0:
  raise ValueError("""surface_area_torus() only accepts non-negative values"""						)
 if torus_radius < tube_radius:
  raise ValueError(
      """surface_area_torus() does not support spindle or self intersecting tori"""						)
 return 4 * pow(UpperCamelCase			,		2						) * torus_radius * tube_radius

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if length < 0 or width < 0:
  raise ValueError("""area_rectangle() only accepts non-negative values"""						)
 return length * width

def       _snake_case						(						UpperCamelCase						:		float						):
 if side_length < 0:
  raise ValueError("""area_square() only accepts non-negative values"""						)
 return side_length**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if base < 0 or height < 0:
  raise ValueError("""area_triangle() only accepts non-negative values"""						)
 return (base * height) / 2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if sidea < 0 or sidea < 0 or sidea < 0:
  raise ValueError("""area_triangle_three_sides() only accepts non-negative values"""						)
 elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
  raise ValueError("""Given three sides do not form a triangle"""						)
 UpperCAmelCase	:					Union[str, Any]	=	(sidea + sidea + sidea) / 2
 UpperCAmelCase	:					Union[str, Any]	=	sqrt(
     semi_perimeter
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)						)
 return area

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if base < 0 or height < 0:
  raise ValueError("""area_parallelogram() only accepts non-negative values"""						)
 return base * height

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if basea < 0 or basea < 0 or height < 0:
  raise ValueError("""area_trapezium() only accepts non-negative values"""						)
 return 1 / 2 * (basea + basea) * height

def       _snake_case						(						UpperCamelCase						:		float						):
 if radius < 0:
  raise ValueError("""area_circle() only accepts non-negative values"""						)
 return pi * radius**2

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if radius_x < 0 or radius_y < 0:
  raise ValueError("""area_ellipse() only accepts non-negative values"""						)
 return pi * radius_x * radius_y

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 if diagonal_a < 0 or diagonal_a < 0:
  raise ValueError("""area_rhombus() only accepts non-negative values"""						)
 return 1 / 2 * diagonal_a * diagonal_a

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		float						):
 if not isinstance(UpperCamelCase			,		UpperCamelCase						) or sides < 3:
  raise ValueError(
      """area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"""						)
 elif length < 0:
  raise ValueError(
      """area_reg_polygon() only accepts non-negative values as \
length of a side"""						)
 return (sides * length**2) / (4 * tan(pi / sides						))
 return (sides * length**2) / (4 * tan(pi / sides						))


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

      print("[DEMO] Areas of various geometric shapes: \n")
      print(f"""Rectangle: {area_rectangle(1_0, 2_0) = }""")
      print(f"""Square: {area_square(1_0) = }""")
      print(f"""Triangle: {area_triangle(1_0, 1_0) = }""")
      print(f"""Triangle: {area_triangle_three_sides(5, 1_2, 1_3) = }""")
      print(f"""Parallelogram: {area_parallelogram(1_0, 2_0) = }""")
      print(f"""Rhombus: {area_rhombus(1_0, 2_0) = }""")
      print(f"""Trapezium: {area_trapezium(1_0, 2_0, 3_0) = }""")
      print(f"""Circle: {area_circle(2_0) = }""")
      print(f"""Ellipse: {area_ellipse(1_0, 2_0) = }""")
      print("\nSurface Areas of various geometric shapes: \n")
      print(f"""Cube: {surface_area_cube(2_0) = }""")
      print(f"""Cuboid: {surface_area_cuboid(1_0, 2_0, 3_0) = }""")
      print(f"""Sphere: {surface_area_sphere(2_0) = }""")
      print(f"""Hemisphere: {surface_area_hemisphere(2_0) = }""")
      print(f"""Cone: {surface_area_cone(1_0, 2_0) = }""")
      print(f"""Conical Frustum: {surface_area_conical_frustum(1_0, 2_0, 3_0) = }""")
      print(f"""Cylinder: {surface_area_cylinder(1_0, 2_0) = }""")
      print(f"""Torus: {surface_area_torus(2_0, 1_0) = }""")
      print(f"""Equilateral Triangle: {area_reg_polygon(3, 1_0) = }""")
      print(f"""Square: {area_reg_polygon(4, 1_0) = }""")
      print(f"""Reqular Pentagon: {area_reg_polygon(5, 1_0) = }""")
