


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,
)

snake_case							:       Any										=	logging.getLogger(__name__)

snake_case							:       List[Any]										=	{'facebook/bart-base': BartForConditionalGeneration}
snake_case							:       Union[str, Any]										=	{'facebook/bart-base': BartTokenizer}
def 							__lowerCamelCase		(					):




      """simple docstring"""





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

      a		:int									=			parser.parse_args()

      return args
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[Any]					,    UpperCAmelCase_		:						List[str]="cpu"		):




      """simple docstring"""





      a		:Any									=			model_dict[model_name].from_pretrained(_A		).to(_A		)
      a		:int									=			tokenizer_dict[model_name].from_pretrained(_A		)

      if model_name in ["facebook/bart-base"]:
            a		:str									=			0
            a		:Tuple									=			None
            a		:str									=			0

      return huggingface_model, tokenizer
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      model.eval()

      a		:Optional[int]									=			None
      a		:int									=			torch.jit.script(BARTBeamSearchGenerator(_A		)		)

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

            a		:List[Any]									=			model.generate(
                inputs['''input_ids''']					,    attention_mask=inputs['''attention_mask''']					,    num_beams=_A					,    max_length=_A					,    early_stopping=_A					,    decoder_start_token_id=model.config.decoder_start_token_id					,    )

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

            logger.info('''Model exported to {}'''.format(_A		)		)

            a		:Any									=			remove_dup_initializers(os.path.abspath(_A		)		)

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

            a		:str									=			onnxruntime.InferenceSession(_A		)
            a		:Union[str, Any]									=			ort_sess.run(
                _A					,    {
                    '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                    '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                    '''num_beams''': np.array(_A		),
                    '''max_length''': np.array(_A		),
                    '''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		(					):




      """simple docstring"""





      a		:int									=			parse_args()
      a		:str									=			5
      a		:int									=			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()

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

      a		:str									=			load_model_tokenizer(args.model_name_or_path					,    _A		)

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

      model.to(_A		)

      if args.max_length:
            a		:List[str]									=			args.max_length

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

      if args.output_file_path:
            a		:int									=			args.output_file_path
      else:
            a		:Optional[int]									=			'''BART.onnx'''

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


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 XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 					_lowercase						(   lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Any   				= KandinskyInpaintPipeline
      UpperCAmelCase_   :   str   				= ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      UpperCAmelCase_   :   Union[str, Any]   				= [
          '''prompt''',
          '''negative_prompt''',
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      UpperCAmelCase_   :   Union[str, Any]   				= [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''negative_prompt''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      UpperCAmelCase_   :   Optional[int]   				= False




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return self.time_input_dim




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



         '''simple docstring'''



         return self.time_input_dim * 4




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return 100




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''				)
         return tokenizer




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					Any 					=	MCLIPConfig(
             numDims=self.cross_attention_dim       ,transformerDimensions=self.text_embedder_hidden_size       ,hidden_size=self.text_embedder_hidden_size       ,intermediate_size=37       ,num_attention_heads=4       ,num_hidden_layers=5       ,vocab_size=1005       ,)

         UpperCAmelCase__				:					Any 					=	MultilingualCLIP(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	text_encoder.eval()

         return text_encoder




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         torch.manual_seed(0				)

         UpperCAmelCase__				:					Tuple 					=	{
             '''in_channels''': 9,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''text_image''',
             '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
             '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
             '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
             '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
             '''layers_per_block''': 1,
             '''encoder_hid_dim''': self.text_embedder_hidden_size,
             '''encoder_hid_dim_type''': '''text_image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         UpperCAmelCase__				:					List[str] 					=	UNetaDConditionModel(**lowerCamelCase_				)
         return model




      @property
      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					Union[str, Any] 					=	VQModel(**self.dummy_movq_kwargs				)
         return model




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.dummy_text_encoder
         UpperCAmelCase__				:					Optional[int] 					=	self.dummy_tokenizer
         UpperCAmelCase__				:					Optional[Any] 					=	self.dummy_unet
         UpperCAmelCase__				:					Any 					=	self.dummy_movq

         UpperCAmelCase__				:					Tuple 					=	DDIMScheduler(
             num_train_timesteps=1000       ,beta_schedule='''linear'''       ,beta_start=0.0_0085       ,beta_end=0.012       ,clip_sample=lowerCamelCase_       ,set_alpha_to_one=lowerCamelCase_       ,steps_offset=1       ,prediction_type='''epsilon'''       ,thresholding=lowerCamelCase_       ,)

         UpperCAmelCase__				:					Dict 					=	{
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=0				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(seed + 1				)				).to(lowerCamelCase_				)
         # create init_image
         UpperCAmelCase__				:					str 					=	floats_tensor((1, 3, 64, 64)       ,rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	image.cpu().permute(0       ,2       ,3       ,1				)[0]
         UpperCAmelCase__				:					Union[str, Any] 					=	Image.fromarray(np.uinta(lowerCamelCase_				)				).convert('''RGB'''				).resize((256, 256)				)
         # create mask
         UpperCAmelCase__				:					List[str] 					=	np.ones((64, 64)       ,dtype=np.floataa				)
         UpperCAmelCase__				:					Optional[Any] 					=	0

         if str(lowerCamelCase_				).startswith('''mps'''				):
            UpperCAmelCase__				:					Any 					=	torch.manual_seed(lowerCamelCase_				)
         else:
            UpperCAmelCase__				:					Dict 					=	torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	{
             '''prompt''': '''horse''',
             '''image''': init_image,
             '''mask_image''': mask,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 64,
             '''width''': 64,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 4.0,
             '''output_type''': '''np''',
         }
         return inputs




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	'''cpu'''

         UpperCAmelCase__				:					List[str] 					=	self.get_dummy_components()

         UpperCAmelCase__				:					Tuple 					=	self.pipeline_class(**lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	pipe.to(lowerCamelCase_				)

         pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	pipe(**self.get_dummy_inputs(lowerCamelCase_				)				)
         UpperCAmelCase__				:					List[Any] 					=	output.images

         UpperCAmelCase__				:					Union[str, Any] 					=	pipe(
             **self.get_dummy_inputs(lowerCamelCase_				)       ,return_dict=lowerCamelCase_       ,)[0]

         UpperCAmelCase__				:					Tuple 					=	image[0, -3:, -3:, -1]
         UpperCAmelCase__				:					int 					=	image_from_tuple[0, -3:, -3:, -1]

         print(f'''image.shape {image.shape}'''				)

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase__				:					List[Any] 					=	np.array(
             [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]				)

         assert (
             np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
         ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
         ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         super().test_inference_batch_single_identical(expected_max_diff=3e-3				)




@slow
@require_torch_gpu
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''				)

         UpperCAmelCase__				:					str 					=	load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''				)
         UpperCAmelCase__				:					Union[str, Any] 					=	np.ones((768, 768)       ,dtype=np.floataa				)
         UpperCAmelCase__				:					int 					=	0

         UpperCAmelCase__				:					List[Any] 					=	'''a hat'''

         UpperCAmelCase__				:					str 					=	KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''       ,torch_dtype=torch.floataa				)
         pipe_prior.to(lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint'''       ,torch_dtype=torch.floataa				)
         UpperCAmelCase__				:					Tuple 					=	pipeline.to(lowerCamelCase_				)
         pipeline.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					List[Any] 					=	torch.Generator(device='''cpu'''				).manual_seed(0				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					int 					=	pipe_prior(
             lowerCamelCase_       ,generator=lowerCamelCase_       ,num_inference_steps=5       ,negative_prompt=''''''       ,).to_tuple()

         UpperCAmelCase__				:					Optional[Any] 					=	pipeline(
             lowerCamelCase_       ,image=lowerCamelCase_       ,mask_image=lowerCamelCase_       ,image_embeds=lowerCamelCase_       ,negative_image_embeds=lowerCamelCase_       ,generator=lowerCamelCase_       ,num_inference_steps=100       ,height=768       ,width=768       ,output_type='''np'''       ,)

         UpperCAmelCase__				:					str 					=	output.images[0]

         assert image.shape == (768, 768, 3)

         assert_mean_pixel_difference(lowerCamelCase_       ,lowerCamelCase_				)




