



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,
)

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

__lowerCamelCase     :							Tuple  							=				{"facebook/bart-base": BartForConditionalGeneration}
__lowerCamelCase     :							int  							=				{"facebook/bart-base": BartTokenizer}

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

						UpperCAmelCase         =     parser.parse_args()

						return args

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_="cpu"    ) ->       Dict:
						UpperCAmelCase         =     model_dict[model_name].from_pretrained(lowerCamelCase_    ).to(lowerCamelCase_    )
						UpperCAmelCase         =     tokenizer_dict[model_name].from_pretrained(lowerCamelCase_    )

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

						return huggingface_model, tokenizer

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       Tuple:
						model.eval()

						UpperCAmelCase         =     None
						UpperCAmelCase         =     torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase_    )    )

						with torch.no_grad():
												UpperCAmelCase         =     "My friends are cool but they eat too many carbs."
												UpperCAmelCase         =     tokenizer([ARTICLE_TO_SUMMARIZE]    ,   max_length=1_024    ,   return_tensors="pt"    ).to(model.device    )

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

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

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

												UpperCAmelCase         =     remove_dup_initializers(os.path.abspath(lowerCamelCase_    )    )

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

												UpperCAmelCase         =     onnxruntime.InferenceSession(lowerCamelCase_    )
												UpperCAmelCase         =     ort_sess.run(
												    lowerCamelCase_    ,   {
												        "input_ids": inputs["input_ids"].cpu().numpy(),
												        "attention_mask": inputs["attention_mask"].cpu().numpy(),
												        "num_beams": np.array(lowerCamelCase_    ),
												        "max_length": np.array(lowerCamelCase_    ),
												        "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    lowerCamelCase_() ->       List[Any]:
						UpperCAmelCase         =     parse_args()
						UpperCAmelCase         =     5
						UpperCAmelCase         =     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()

						UpperCAmelCase         =     torch.device(args.device    )

						UpperCAmelCase       ,		UpperCAmelCase         =     load_model_tokenizer(args.model_name_or_path    ,   lowerCamelCase_    )

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

						model.to(lowerCamelCase_    )

						if args.max_length:
												UpperCAmelCase         =     args.max_length

						if args.num_beams:
												UpperCAmelCase         =     args.num_beams

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

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


if __name__ == "__main__":
		main()








import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
		import jax
		import jax.numpy as jnp
		from flax.jax_utils import replicate
		from flax.training.common_utils import shard





@slow
@require_flax
class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Optional[Any]:
								'''simple docstring'''







								super().tearDown()
								gc.collect()






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxControlNetModel.from_pretrained(
								    "lllyasviel/sd-controlnet-canny"				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    "runwayml/stable-diffusion-v1-5"				,		controlnet=UpperCamelCase__				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase         =     controlnet_params

								UpperCAmelCase         =     "bird"
								UpperCAmelCase         =     jax.device_count()
								UpperCAmelCase         =     pipe.prepare_text_inputs([prompts] * num_samples      )

								UpperCAmelCase         =     load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"      )
								UpperCAmelCase         =     pipe.prepare_image_inputs([canny_image] * num_samples      )

								UpperCAmelCase         =     jax.random.PRNGKey(0      )
								UpperCAmelCase         =     jax.random.split(UpperCamelCase__				,		jax.device_count()      )

								UpperCAmelCase         =     replicate(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )

								UpperCAmelCase         =     pipe(
								    prompt_ids=UpperCamelCase__				,		image=UpperCamelCase__				,		params=UpperCamelCase__				,		prng_seed=UpperCamelCase__				,		num_inference_steps=50				,		jit=UpperCamelCase__				,		).images
								assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

								UpperCAmelCase         =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
								UpperCAmelCase         =     images[0, 2_53:2_56, 2_53:2_56, -1]

								UpperCAmelCase         =     jnp.asarray(jax.device_get(image_slice.flatten()      )      )
								UpperCAmelCase         =     jnp.array(
								    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]      )
								print(F'output_slice: {output_slice}'      )
								assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxControlNetModel.from_pretrained(
								    "lllyasviel/sd-controlnet-openpose"				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase       ,		UpperCAmelCase         =     FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    "runwayml/stable-diffusion-v1-5"				,		controlnet=UpperCamelCase__				,		from_pt=UpperCamelCase__				,		dtype=jnp.bfloataa      )
								UpperCAmelCase         =     controlnet_params

								UpperCAmelCase         =     "Chef in the kitchen"
								UpperCAmelCase         =     jax.device_count()
								UpperCAmelCase         =     pipe.prepare_text_inputs([prompts] * num_samples      )

								UpperCAmelCase         =     load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"      )
								UpperCAmelCase         =     pipe.prepare_image_inputs([pose_image] * num_samples      )

								UpperCAmelCase         =     jax.random.PRNGKey(0      )
								UpperCAmelCase         =     jax.random.split(UpperCamelCase__				,		jax.device_count()      )

								UpperCAmelCase         =     replicate(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )
								UpperCAmelCase         =     shard(UpperCamelCase__      )

								UpperCAmelCase         =     pipe(
								    prompt_ids=UpperCamelCase__				,		image=UpperCamelCase__				,		params=UpperCamelCase__				,		prng_seed=UpperCamelCase__				,		num_inference_steps=50				,		jit=UpperCamelCase__				,		).images
								assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

								UpperCAmelCase         =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
								UpperCAmelCase         =     images[0, 2_53:2_56, 2_53:2_56, -1]

								UpperCAmelCase         =     jnp.asarray(jax.device_get(image_slice.flatten()      )      )
								UpperCAmelCase         =     jnp.array(
								    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]      )
								print(F'output_slice: {output_slice}'      )
								assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2



