






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       :       Dict        =				logging.getLogger(__name__)

lowerCamelCase       :       List[Any]        =				{'''facebook/bart-base''': BartForConditionalGeneration}
lowerCamelCase       :       Optional[Any]        =				{'''facebook/bart-base''': BartTokenizer}
def 					snake_case_						(   ):
							__lowercase					:							Dict			=   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."""					)

							__lowercase					:							Any			=   parser.parse_args()

							return args
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]				,				lowerCAmelCase_				:  int="cpu"					):
							__lowercase					:							Union[str, Any]			=   model_dict[model_name].from_pretrained(lowerCAmelCase_					).to(lowerCAmelCase_					)
							__lowercase					:							Dict			=   tokenizer_dict[model_name].from_pretrained(lowerCAmelCase_					)

							if model_name in ["facebook/bart-base"]:
														__lowercase					:							Dict			=   0
														__lowercase					:							Optional[Any]			=   None
														__lowercase					:							Union[str, Any]			=   0

							return huggingface_model, tokenizer
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Union[str, Any]				,				lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  Optional[int]					):
							model.eval()

							__lowercase					:							List[str]			=   None
							__lowercase					:							str			=   torch.jit.script(BARTBeamSearchGenerator(lowerCAmelCase_					)					)

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

														__lowercase					:							List[str]			=   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_					)					)

														__lowercase					:							Optional[Any]			=   remove_dup_initializers(os.path.abspath(lowerCAmelCase_					)					)

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

														__lowercase					:							Tuple			=   onnxruntime.InferenceSession(lowerCAmelCase_					)
														__lowercase					:							Dict			=   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 					snake_case_						(   ):
							__lowercase					:							int			=   parse_args()
							__lowercase					:							List[str]			=   5
							__lowercase					:							Union[str, Any]			=   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()

							__lowercase					:							List[str]			=   torch.device(args.device					)

							__lowercase	,	__lowercase					:							Optional[Any]			=   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:
														__lowercase					:							Any			=   args.max_length

							if args.num_beams:
														__lowercase					:							List[Any]			=   args.num_beams

							if args.output_file_path:
														__lowercase					:							str			=   args.output_file_path
							else:
														__lowercase					:							int			=   """BART.onnx"""

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


if __name__ == "__main__":
	main()







import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort

class 						lowerCAmelCase   (       __a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	List[Any]     =					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''



					def 					lowerCAmelCase (     self :		int						,   __a :		List[Any]=0							)	->     Optional[int]:


												"""simple docstring"""

												__lowercase					:							Any			=   floats_tensor((1, 3, 128, 128)						,   rng=random.Random(__a							)							)
												__lowercase					:							Any			=   np.random.RandomState(__a							)
												__lowercase					:							Union[str, Any]			=   {
												    """prompt""": """A painting of a squirrel eating a burger""",
												    """image""": image,
												    """generator""": generator,
												    """num_inference_steps""": 3,
												    """strength""": 0.75,
												    """guidance_scale""": 7.5,
												    """output_type""": """numpy""",
												}
												return inputs



					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     Any:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Optional[Any]			=   self.get_dummy_inputs()
												__lowercase					:							str			=   pipe(**__a							).images
												__lowercase					:							Union[str, Any]			=   image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							List[Any]			=   np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]							)
												assert np.abs(image_slice - expected_slice							).max() < 1E-1



					def 					lowerCAmelCase (     self :		Any							)	->     Any:


												"""simple docstring"""

												__lowercase					:							str			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Union[str, Any]			=   PNDMScheduler.from_config(pipe.scheduler.config						,   skip_prk_steps=__a							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Dict			=   self.get_dummy_inputs()
												__lowercase					:							Optional[int]			=   pipe(**__a							).images
												__lowercase					:							Union[str, Any]			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Any			=   np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]							)

												assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1



					def 					lowerCAmelCase (     self :		int							)	->     str:


												"""simple docstring"""

												__lowercase					:							int			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Union[str, Any]			=   LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												# warmup pass to apply optimizations
												__lowercase					:							Optional[int]			=   pipe(**self.get_dummy_inputs()							)

												__lowercase					:							Dict			=   self.get_dummy_inputs()
												__lowercase					:							str			=   pipe(**__a							).images
												__lowercase					:							Optional[Any]			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Any			=   np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]							)

												assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							List[str]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							List[Any]			=   EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Any			=   self.get_dummy_inputs()
												__lowercase					:							int			=   pipe(**__a							).images
												__lowercase					:							Dict			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Optional[int]			=   np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]							)

												assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1



					def 					lowerCAmelCase (     self :		List[Any]							)	->     str:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Dict			=   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Tuple			=   self.get_dummy_inputs()
												__lowercase					:							Union[str, Any]			=   pipe(**__a							).images
												__lowercase					:							int			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Union[str, Any]			=   np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]							)

												assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1



					def 					lowerCAmelCase (     self :		Dict							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Tuple			=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							str			=   self.get_dummy_inputs()
												__lowercase					:							Dict			=   pipe(**__a							).images
												__lowercase					:							int			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Dict			=   np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]							)

												assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1







@nightly
@require_onnxruntime
@require_torch_gpu
class 						lowerCAmelCase   (       unittest.TestCase	):

					'''simple docstring'''

					@property
					def 					lowerCAmelCase (     self :		Tuple							)	->     Optional[int]:


												"""simple docstring"""

												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)



					@property
					def 					lowerCAmelCase (     self :		str							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   ort.SessionOptions()
												__lowercase					:							Dict			=   False
												return options



					def 					lowerCAmelCase (     self :		int							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg"""							)
												__lowercase					:							Union[str, Any]			=   init_image.resize((768, 512)							)
												# using the PNDM scheduler by default
												__lowercase					:							Optional[int]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    """CompVis/stable-diffusion-v1-4"""						,   revision="""onnx"""						,   safety_checker=__a						,   feature_extractor=__a						,   provider=self.gpu_provider						,   sess_options=self.gpu_options						,   )
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							int			=   """A fantasy landscape, trending on artstation"""

												__lowercase					:							int			=   np.random.RandomState(0							)
												__lowercase					:							List[str]			=   pipe(
												    prompt=__a						,   image=__a						,   strength=0.75						,   guidance_scale=7.5						,   num_inference_steps=10						,   generator=__a						,   output_type="""np"""						,   )
												__lowercase					:							Any			=   output.images
												__lowercase					:							Optional[int]			=   images[0, 255:258, 383:386, -1]

												assert images.shape == (1, 512, 768, 3)
												__lowercase					:							Optional[int]			=   np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]							)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

												assert np.abs(image_slice.flatten() - expected_slice							).max() < 2E-2



					def 					lowerCAmelCase (     self :		List[Any]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg"""							)
												__lowercase					:							Any			=   init_image.resize((768, 512)							)
												__lowercase					:							int			=   LMSDiscreteScheduler.from_pretrained(
												    """runwayml/stable-diffusion-v1-5"""						,   subfolder="""scheduler"""						,   revision="""onnx"""							)
												__lowercase					:							str			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    """runwayml/stable-diffusion-v1-5"""						,   revision="""onnx"""						,   scheduler=__a						,   safety_checker=__a						,   feature_extractor=__a						,   provider=self.gpu_provider						,   sess_options=self.gpu_options						,   )
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Union[str, Any]			=   """A fantasy landscape, trending on artstation"""

												__lowercase					:							Any			=   np.random.RandomState(0							)
												__lowercase					:							Optional[Any]			=   pipe(
												    prompt=__a						,   image=__a						,   strength=0.75						,   guidance_scale=7.5						,   num_inference_steps=20						,   generator=__a						,   output_type="""np"""						,   )
												__lowercase					:							str			=   output.images
												__lowercase					:							Dict			=   images[0, 255:258, 383:386, -1]

												assert images.shape == (1, 512, 768, 3)
												__lowercase					:							Union[str, Any]			=   np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]							)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

												assert np.abs(image_slice.flatten() - expected_slice							).max() < 2E-2