






'''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,
)

lowerCAmelCase__          =						logging.getLogger(__name__)

lowerCAmelCase__          =						{"facebook/bart-base": BartForConditionalGeneration}
lowerCAmelCase__          =						{"facebook/bart-base": BartTokenizer}







def       __UpperCAmelCase						(  )							->     Any:
  UpperCamelCase__				:					Union[str, Any]					=					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__				:					List[str]					=					parser.parse_args()

  return args







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_="cpu")							->     int:
  UpperCamelCase__				:					Optional[int]					=					model_dict[model_name].from_pretrained(__lowerCAmelCase).to(__lowerCAmelCase)
  UpperCamelCase__				:					List[str]					=					tokenizer_dict[model_name].from_pretrained(__lowerCAmelCase)

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

  return huggingface_model, tokenizer







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     str:
  model.eval()

  UpperCamelCase__				:					Optional[Any]					=					None
  UpperCamelCase__				:					Union[str, Any]					=					torch.jit.script(BARTBeamSearchGenerator(__lowerCAmelCase))

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

    UpperCamelCase__				:					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))

    UpperCamelCase__				:					str					=					remove_dup_initializers(os.path.abspath(__lowerCAmelCase))

    logger.info('Deduplicated and optimized model written to {}'.format(__lowerCAmelCase))

    UpperCamelCase__				:					int					=					onnxruntime.InferenceSession(__lowerCAmelCase)
    UpperCamelCase__				:					Any					=					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       __UpperCAmelCase						(  )							->     Any:
  UpperCamelCase__				:					Union[str, Any]					=					parse_args()
  UpperCamelCase__				:					Optional[Any]					=					5
  UpperCamelCase__				:					List[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()

  UpperCamelCase__				:					List[Any]					=					torch.device(args.device)

  UpperCamelCase__,	UpperCamelCase__				:					Union[str, 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:
    UpperCamelCase__				:					str					=					args.max_length

  if args.num_beams:
    UpperCamelCase__				:					str					=					args.num_beams

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

  logger.info('Exporting model to ONNX')
  export_and_validate_model(__lowerCAmelCase     ,      __lowerCAmelCase     ,      __lowerCAmelCase     ,      __lowerCAmelCase     ,      __lowerCAmelCase)


if __name__ == "__main__":
  main()












'''simple docstring'''




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class       __lowercase					:

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			torch.manual_seed(0)
			UpperCamelCase__				:					Dict					=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Union[str, Any]					=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					UNetaDConditionModel(
			    sample_size=32  ,					layers_per_block=1  ,					block_out_channels=[32, 64]  ,					down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]  ,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,					in_channels=3  ,					out_channels=6  ,					cross_attention_dim=32  ,					encoder_hid_dim=32  ,					attention_head_dim=8  ,					addition_embed_type='text'  ,					addition_embed_type_num_heads=2  ,					cross_attention_norm='group_norm'  ,					resnet_time_scale_shift='scale_shift'  ,					act_fn='gelu'  ,					)
			unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					thresholding=UpperCAmelCase_  ,					dynamic_thresholding_ratio=0.95  ,					sample_max_value=1.0  ,					prediction_type='epsilon'  ,					variance_type='learned_range'  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}

	def      __UpperCamelCase							(      self   :						Dict):
			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					UNetaDConditionModel(
			    sample_size=32  ,					layers_per_block=[1, 2]  ,					block_out_channels=[32, 64]  ,					down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]  ,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,					in_channels=6  ,					out_channels=6  ,					cross_attention_dim=32  ,					encoder_hid_dim=32  ,					attention_head_dim=8  ,					addition_embed_type='text'  ,					addition_embed_type_num_heads=2  ,					cross_attention_norm='group_norm'  ,					resnet_time_scale_shift='scale_shift'  ,					act_fn='gelu'  ,					class_embed_type='timestep'  ,					mid_block_scale_factor=1.4_14  ,					time_embedding_act_fn='gelu'  ,					time_embedding_dim=32  ,					)
			unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			torch.manual_seed(0)
			UpperCamelCase__				:					str					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					thresholding=UpperCAmelCase_  ,					dynamic_thresholding_ratio=0.95  ,					sample_max_value=1.0  ,					prediction_type='epsilon'  ,					variance_type='learned_range'  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "image_noising_scheduler": image_noising_scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Dict					=					self.get_dummy_components()
			UpperCamelCase__				:					List[Any]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					inputs['prompt']
			UpperCamelCase__				:					List[Any]					=					inputs['generator']
			UpperCamelCase__				:					Tuple					=					inputs['num_inference_steps']
			UpperCamelCase__				:					List[Any]					=					inputs['output_type']

			if "image" in inputs:
					UpperCamelCase__				:					Tuple					=					inputs['image']
			else:
					UpperCamelCase__				:					Union[str, Any]					=					None

			if "mask_image" in inputs:
					UpperCamelCase__				:					Optional[int]					=					inputs['mask_image']
			else:
					UpperCamelCase__				:					int					=					None

			if "original_image" in inputs:
					UpperCamelCase__				:					List[Any]					=					inputs['original_image']
			else:
					UpperCamelCase__				:					Optional[Any]					=					None

			UpperCamelCase__,	UpperCamelCase__				:					Any					=					pipe.encode_prompt(UpperCAmelCase_)

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					List[Any]					=					{
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					UpperCamelCase__				:					Dict					=					image

			if mask_image is not None:
					UpperCamelCase__				:					Optional[int]					=					mask_image

			if original_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					original_image

			# set all optional components to None
			for optional_component in pipe._optional_components:
					setattr(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

			UpperCamelCase__				:					int					=					pipe(**UpperCAmelCase_)[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					self.pipeline_class.from_pretrained(UpperCAmelCase_)
					pipe_loaded.to(UpperCAmelCase_)
					pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_)

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			for optional_component in pipe._optional_components:
					self.assertTrue(
					    getattr(UpperCAmelCase_  ,					UpperCAmelCase_) is None  ,					F'`{optional_component}` did not stay set to None after loading.'  ,					)

			UpperCamelCase__				:					Optional[int]					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					inputs['generator']
			UpperCamelCase__				:					List[Any]					=					inputs['num_inference_steps']
			UpperCamelCase__				:					Optional[int]					=					inputs['output_type']

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					Any					=					{
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					UpperCamelCase__				:					Tuple					=					image

			if mask_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					mask_image

			if original_image is not None:
					UpperCamelCase__				:					str					=					original_image

			UpperCamelCase__				:					Union[str, Any]					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Dict					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)





	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Any					=					self.get_dummy_components()
			UpperCamelCase__				:					List[str]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					self.get_dummy_inputs(UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					pipe(**UpperCAmelCase_)[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					self.pipeline_class.from_pretrained(UpperCAmelCase_)
					pipe_loaded.to(UpperCAmelCase_)
					pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_)

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			UpperCamelCase__				:					Any					=					self.get_dummy_inputs(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Optional[int]					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)




