



"""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    :Dict     =					logging.getLogger(__name__)

_lowerCAmelCase    :Union[str, Any]     =					{'facebook/bart-base': BartForConditionalGeneration}
_lowerCAmelCase    :Dict     =					{'facebook/bart-base': BartTokenizer}





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

    _UpperCAmelCase							:  Optional[int]											=    parser.parse_args()

    return args





def  lowerCamelCase_						(UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Optional[int]="cpu"							):
    _UpperCAmelCase							:  Tuple											=    model_dict[model_name].from_pretrained(UpperCamelCase__							).to(UpperCamelCase__							)
    _UpperCAmelCase							:  Union[str, Any]											=    tokenizer_dict[model_name].from_pretrained(UpperCamelCase__							)

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

    return huggingface_model, tokenizer





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Optional[Any]							):
    model.eval()

    _UpperCAmelCase							:  str											=    None
    _UpperCAmelCase							:  List[str]											=    torch.jit.script(BARTBeamSearchGenerator(UpperCamelCase__							)							)

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

        _UpperCAmelCase							:  Optional[int]											=    model.generate(
            inputs['''input_ids''']     ,       attention_mask=inputs['''attention_mask''']     ,       num_beams=UpperCamelCase__     ,       max_length=UpperCamelCase__     ,       early_stopping=UpperCamelCase__     ,       decoder_start_token_id=model.config.decoder_start_token_id     ,       )

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

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

        _UpperCAmelCase							:  int											=    remove_dup_initializers(os.path.abspath(UpperCamelCase__							)							)

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

        _UpperCAmelCase							:  Optional[Any]											=    onnxruntime.InferenceSession(UpperCamelCase__							)
        _UpperCAmelCase							:  Dict											=    ort_sess.run(
            UpperCamelCase__     ,       {
                '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                '''num_beams''': np.array(UpperCamelCase__							),
                '''max_length''': np.array(UpperCamelCase__							),
                '''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_						():
    _UpperCAmelCase							:  Union[str, Any]											=    parse_args()
    _UpperCAmelCase							:  Optional[Any]											=    5
    _UpperCAmelCase							:  Optional[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()

    _UpperCAmelCase							:  Union[str, Any]											=    torch.device(args.device							)

    _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    load_model_tokenizer(args.model_name_or_path     ,       UpperCamelCase__							)

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

    model.to(UpperCamelCase__							)

    if args.max_length:
        _UpperCAmelCase							:  Dict											=    args.max_length

    if args.num_beams:
        _UpperCAmelCase							:  Any											=    args.num_beams

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

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


if __name__ == "__main__":
  main()







"""simple docstring"""
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


_lowerCAmelCase    :Any     =					False
@skip_mps
class 							_UpperCAmelCase							( a		,a		,a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=StableDiffusionAttendAndExcitePipeline
     a__									=False
     a__									=TEXT_TO_IMAGE_PARAMS
     a__									=TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}      )
     a__									=TEXT_TO_IMAGE_IMAGE_PARAMS
     a__									=TEXT_TO_IMAGE_IMAGE_PARAMS





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					List[str]:
         super().setUpClass()
         torch.use_deterministic_algorithms(A		)





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Union[str, Any]:
         super().tearDownClass()
         torch.use_deterministic_algorithms(A		)





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[int]											=    UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)       ,	layers_per_block=1       ,	sample_size=3_2       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,	cross_attention_dim=3_2       ,	attention_head_dim=(2, 4)       ,	use_linear_projection=A       ,	)
         _UpperCAmelCase							:  List[Any]											=    DDIMScheduler(
             beta_start=0.00_085       ,	beta_end=0.012       ,	beta_schedule='''scaled_linear'''       ,	clip_sample=A       ,	set_alpha_to_one=A       ,	)
         torch.manual_seed(0		)
         _UpperCAmelCase							:  int											=    AutoencoderKL(
             block_out_channels=[3_2, 6_4]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,	latent_channels=4       ,	sample_size=1_2_8       ,	)
         torch.manual_seed(0		)
         _UpperCAmelCase							:  int											=    CLIPTextConfig(
             bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=3_2       ,	intermediate_size=3_7       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=1_0_0_0       ,	hidden_act='''gelu'''       ,	projection_dim=5_1_2       ,	)
         _UpperCAmelCase							:  List[str]											=    CLIPTextModel(A		)
         _UpperCAmelCase							:  str											=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

         _UpperCAmelCase							:  Union[str, Any]											=    {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }

         return components





     def  __lowerCAmelCase	(					self       ,	A       ,	A=0		)							->					List[Any]:
         if str(A		).startswith('''mps'''		):
             _UpperCAmelCase							:  Optional[int]											=    torch.manual_seed(A		)
         else:
             _UpperCAmelCase							:  Union[str, Any]											=    torch.Generator(device=A		).manual_seed(A		)
         _UpperCAmelCase							:  List[str]											=    {
             '''prompt''': '''a cat and a frog''',
             '''token_indices''': [2, 5],
             '''generator''': generator,
             '''num_inference_steps''': 1,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
             '''max_iter_to_alter''': 2,
             '''thresholds''': {0: 0.7},
         }
         return inputs





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  List[str]											=    '''cpu'''

         _UpperCAmelCase							:  Tuple											=    self.get_dummy_components()
         _UpperCAmelCase							:  int											=    self.pipeline_class(**A		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  Dict											=    self.get_dummy_inputs(A		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe(**A		).images
         _UpperCAmelCase							:  Tuple											=    image[0, -3:, -3:, -1]

         self.assertEqual(image.shape       ,	(1, 6_4, 6_4, 3)		)
         _UpperCAmelCase							:  int											=    np.array(
             [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
         _UpperCAmelCase							:  Tuple											=    np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(A       ,	1E-3		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
         self._test_inference_batch_consistent(batch_sizes=[1, 2]		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         self._test_inference_batch_single_identical(batch_size=2       ,	expected_max_diff=7E-4		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					str:
         super().test_save_load_local(expected_max_difference=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         super().test_save_load_optional_components(expected_max_difference=4E-4		)



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





     '''simple docstring'''



     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Union[str, Any]:
         super().setUpClass()
         torch.use_deterministic_algorithms(A		)





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Optional[int]:
         super().tearDownClass()
         torch.use_deterministic_algorithms(A		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    torch.manual_seed(5_1		)

         _UpperCAmelCase							:  Optional[Any]											=    StableDiffusionAttendAndExcitePipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''       ,	safety_checker=A       ,	torch_dtype=torch.floataa		)
         pipe.to('''cuda'''		)

         _UpperCAmelCase							:  Optional[int]											=    '''a painting of an elephant with glasses'''
         _UpperCAmelCase							:  int											=    [5, 7]

         _UpperCAmelCase							:  Dict											=    pipe(
             prompt=A       ,	token_indices=A       ,	guidance_scale=7.5       ,	generator=A       ,	num_inference_steps=5       ,	max_iter_to_alter=5       ,	output_type='''numpy'''       ,	).images[0]

         _UpperCAmelCase							:  List[Any]											=    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'''		)
         assert np.abs((expected_image - image).max()		) < 5E-1


