





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,
)

_UpperCAmelCase       =				logging.getLogger(__name__)

_UpperCAmelCase       =				{'facebook/bart-base': BartForConditionalGeneration}
_UpperCAmelCase       =				{'facebook/bart-base': BartTokenizer}






def 			_SCREAMING_SNAKE_CASE						(     )     ->       Union[str, Any]:
 __lowerCAmelCase	:			List[str]         =  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."""       )

 __lowerCAmelCase	:			Dict         =  parser.parse_args()

 return args






def 			_SCREAMING_SNAKE_CASE						(     SCREAMING_SNAKE_CASE						:List[str]       ,       SCREAMING_SNAKE_CASE						:Optional[Any]="cpu"       )     ->       Any:
 __lowerCAmelCase	:			Tuple         =  model_dict[model_name].from_pretrained(lowerCAmelCase_       ).to(lowerCAmelCase_       )
 __lowerCAmelCase	:			Optional[int]         =  tokenizer_dict[model_name].from_pretrained(lowerCAmelCase_       )

 if model_name in ["facebook/bart-base"]:
  __lowerCAmelCase	:			Union[str, Any]         =  0
  __lowerCAmelCase	:			Union[str, Any]         =  None
  __lowerCAmelCase	:			Tuple         =  0

 return huggingface_model, tokenizer






def 			_SCREAMING_SNAKE_CASE						(     SCREAMING_SNAKE_CASE						:int       ,       SCREAMING_SNAKE_CASE						:Optional[int]       ,       SCREAMING_SNAKE_CASE						:Union[str, Any]       ,       SCREAMING_SNAKE_CASE						:Any       ,       SCREAMING_SNAKE_CASE						:Optional[Any]       )     ->       Union[str, Any]:
 model.eval()

 __lowerCAmelCase	:			Optional[int]         =  None
 __lowerCAmelCase	:			List[str]         =  torch.jit.script(BARTBeamSearchGenerator(lowerCAmelCase_       )       )

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

  __lowerCAmelCase	:			Optional[int]         =  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_       )       )

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

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

  __lowerCAmelCase	:			List[str]         =  onnxruntime.InferenceSession(lowerCAmelCase_       )
  __lowerCAmelCase	:			Tuple         =  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 			_SCREAMING_SNAKE_CASE						(     )     ->       Optional[int]:
 __lowerCAmelCase	:			Optional[int]         =  parse_args()
 __lowerCAmelCase	:			Dict         =  5
 __lowerCAmelCase	:			Dict         =  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()

 __lowerCAmelCase	:			str         =  torch.device(args.device       )

 __lowerCAmelCase	:			List[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:
  __lowerCAmelCase	:			List[str]         =  args.max_length

 if args.num_beams:
  __lowerCAmelCase	:			Optional[Any]         =  args.num_beams

 if args.output_file_path:
  __lowerCAmelCase	:			int         =  args.output_file_path
 else:
  __lowerCAmelCase	:			List[str]         =  'BART.onnx'

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


if __name__ == "__main__":
      main()




'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   StableDiffusionInpaintPipeline
							lowerCAmelCase			:       int  							=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							lowerCAmelCase			:       Optional[Any]  							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowerCAmelCase			:       List[str]  							=   frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase			:       Optional[Any]  							=   frozenset([]  )







							def 				__lowercase     (				self   :			Dict						):
									torch.manual_seed(0						)
									_a	: Optional[int]								=				UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,cross_attention_dim=32      ,attention_head_dim=(2, 4)      ,use_linear_projection=_UpperCAmelCase      ,)
									_a	: Optional[int]								=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase						)
									torch.manual_seed(0						)
									_a	: Dict								=				AutoencoderKL(
									    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,latent_channels=4      ,sample_size=128      ,)
									torch.manual_seed(0						)
									_a	: Union[str, Any]								=				CLIPTextConfig(
									    bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=1000      ,hidden_act='gelu'      ,projection_dim=512      ,)
									_a	: str								=				CLIPTextModel(_UpperCAmelCase						)
									_a	: Any								=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_a	: Optional[Any]								=				{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			int=0						):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_a	: Any								=				floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
									_a	: Any								=				image.cpu().permute(0      ,2      ,3      ,1						)[0]
									_a	: Optional[int]								=				Image.fromarray(np.uinta(_UpperCAmelCase						)						).convert('RGB'						).resize((64, 64)						)
									_a	: Tuple								=				Image.fromarray(np.uinta(image + 4						)						).convert('RGB'						).resize((64, 64)						)
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: str								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Dict								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
									_a	: Any								=				{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': init_image,
									    'mask_image': mask_image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Optional[int]								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a	: Dict								=				self.get_dummy_components()
									_a	: int								=				StableDiffusionInpaintPipeline(**_UpperCAmelCase						)
									_a	: Any								=				sd_pipe.to(_UpperCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				self.get_dummy_inputs(_UpperCAmelCase						)
									_a	: Tuple								=				sd_pipe(**_UpperCAmelCase						).images
									_a	: Dict								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: List[Any]								=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2







							def 				__lowercase     (				self   :			List[Any]						):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

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







							def 				__lowercase     (				self   :			List[str]						):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 				__lowercase     (				self   :			int						):
									_a	: Tuple								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Union[str, Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'						)

									_a	: List[Any]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Tuple								=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase      ,safety_checker=_UpperCAmelCase						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: int								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: Any								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: List[str]								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 9E-3







							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'						)

									_a	: int								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: List[str]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,torch_dtype=torch.floataa      ,safety_checker=_UpperCAmelCase      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: Dict								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: int								=				torch.manual_seed(0						)
									_a	: Dict								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: str								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 5E-1







							def 				__lowercase     (				self   :			Tuple						):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_a	: Dict								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[int]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

									_a	: List[str]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Any								=				PNDMScheduler.from_pretrained(_UpperCAmelCase      ,subfolder='scheduler'						)
									_a	: Optional[Any]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,safety_checker=_UpperCAmelCase      ,scheduler=_UpperCAmelCase      ,torch_dtype=torch.floataa      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									_a	: Any								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Optional[Any]								=				torch.manual_seed(0						)
									_a	: int								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: int								=				torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9




