
"""simple docstring"""



import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


a					:	str							=		logging.get_logger(__name__)

enable_full_determinism()




class 		a_						( snake_case_     ,						snake_case_     ,						unittest.TestCase	):
    a      :				Union[str, Any]     =				UNetaDModel
    a      :				Optional[Any]     =				"""sample"""







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






        '''simple docstring'''






        _UpperCAmelCase          =   4
        _UpperCAmelCase          =   3
        _UpperCAmelCase          =   (32, 32)

        _UpperCAmelCase          =   floats_tensor((batch_size, num_channels) + sizes     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   torch.tensor([10]     ).to(__UpperCamelCase     )

        return {"sample": noise, "timestep": time_step}







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






        '''simple docstring'''






        return (3, 32, 32)







    @property
    def      _snake_case					(			self			:       Optional[int]     )		->List[Any]:






        '''simple docstring'''






        return (3, 32, 32)







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






        '''simple docstring'''






        _UpperCAmelCase          =   {
            """block_out_channels""": (32, 64),
            """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
            """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
            """attention_head_dim""": 3,
            """out_channels""": 3,
            """in_channels""": 3,
            """layers_per_block""": 2,
            """sample_size""": 32,
        }
        _UpperCAmelCase          =   self.dummy_input
        return init_dict, inputs_dict







class 		a_						( snake_case_     ,						snake_case_     ,						unittest.TestCase	):
    a      :				int     =				UNetaDModel
    a      :				List[str]     =				"""sample"""







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






        '''simple docstring'''






        _UpperCAmelCase          =   4
        _UpperCAmelCase          =   4
        _UpperCAmelCase          =   (32, 32)

        _UpperCAmelCase          =   floats_tensor((batch_size, num_channels) + sizes     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   torch.tensor([10]     ).to(__UpperCamelCase     )

        return {"sample": noise, "timestep": time_step}







    @property
    def      _snake_case					(			self			:       List[Any]     )		->int:






        '''simple docstring'''






        return (4, 32, 32)







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






        '''simple docstring'''






        return (4, 32, 32)







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






        '''simple docstring'''






        _UpperCAmelCase          =   {
            """sample_size""": 32,
            """in_channels""": 4,
            """out_channels""": 4,
            """layers_per_block""": 2,
            """block_out_channels""": (32, 64),
            """attention_head_dim""": 32,
            """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
            """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
        }
        _UpperCAmelCase          =   self.dummy_input
        return init_dict, inputs_dict







    def      _snake_case					(			self			:       List[str]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""     ,					output_loading_info=__UpperCamelCase     )

        self.assertIsNotNone(__UpperCamelCase     )
        self.assertEqual(len(loading_info["""missing_keys"""]     )     ,					0     )

        model.to(__UpperCamelCase     )
        _UpperCAmelCase          =   model(**self.dummy_input     ).sample

        assert image is not None, "Make sure output is not None"







    @unittest.skipIf(torch_device != """cuda"""     ,					"""This test is supposed to run on GPU"""     )
    def      _snake_case					(			self			:       Dict     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""     ,					output_loading_info=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        _UpperCAmelCase          =   model(**self.dummy_input     ).sample

        assert image is not None, "Make sure output is not None"







    @unittest.skipIf(torch_device != """cuda"""     ,					"""This test is supposed to run on GPU"""     )
    def      _snake_case					(			self			:       str     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""     ,					output_loading_info=__UpperCamelCase     )
        model_accelerate.to(__UpperCamelCase     )
        model_accelerate.eval()

        _UpperCAmelCase          =   torch.randn(
            1     ,					model_accelerate.config.in_channels     ,					model_accelerate.config.sample_size     ,					model_accelerate.config.sample_size     ,					generator=torch.manual_seed(0     )     ,					)
        _UpperCAmelCase          =   noise.to(__UpperCamelCase     )
        _UpperCAmelCase          =   torch.tensor([10] * noise.shape[0]     ).to(__UpperCamelCase     )

        _UpperCAmelCase          =   model_accelerate(__UpperCamelCase     ,					__UpperCamelCase     )["""sample"""]

        # two models don't need to stay in the device at the same time
        del model_accelerate
        torch.cuda.empty_cache()
        gc.collect()

        _UpperCAmelCase    ,_UpperCAmelCase          =   UNetaDModel.from_pretrained(
            """fusing/unet-ldm-dummy-update"""     ,					output_loading_info=__UpperCamelCase     ,					low_cpu_mem_usage=__UpperCamelCase     )
        model_normal_load.to(__UpperCamelCase     )
        model_normal_load.eval()
        _UpperCAmelCase          =   model_normal_load(__UpperCamelCase     ,					__UpperCamelCase     )["""sample"""]

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					rtol=1e-3     )







    def      _snake_case					(			self			:       Optional[Any]     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""     )
        model.eval()
        model.to(__UpperCamelCase     )

        _UpperCAmelCase          =   torch.randn(
            1     ,					model.config.in_channels     ,					model.config.sample_size     ,					model.config.sample_size     ,					generator=torch.manual_seed(0     )     ,					)
        _UpperCAmelCase          =   noise.to(__UpperCamelCase     )
        _UpperCAmelCase          =   torch.tensor([10] * noise.shape[0]     ).to(__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					__UpperCamelCase     ).sample

        _UpperCAmelCase          =   output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        _UpperCAmelCase          =   torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]     )
        # fmt: on

        self.assertTrue(torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					rtol=1e-3     )     )







class 		a_						( snake_case_     ,						snake_case_     ,						unittest.TestCase	):
    a      :				Dict     =				UNetaDModel
    a      :				Tuple     =				"""sample"""







    @property
    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       List[str]=(32, 32)     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   4
        _UpperCAmelCase          =   3

        _UpperCAmelCase          =   floats_tensor((batch_size, num_channels) + sizes     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   torch.tensor(batch_size * [10]     ).to(dtype=torch.intaa     ,					device=__UpperCamelCase     )

        return {"sample": noise, "timestep": time_step}







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






        '''simple docstring'''






        return (3, 32, 32)







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






        '''simple docstring'''






        return (3, 32, 32)







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






        '''simple docstring'''






        _UpperCAmelCase          =   {
            """block_out_channels""": [32, 64, 64, 64],
            """in_channels""": 3,
            """layers_per_block""": 1,
            """out_channels""": 3,
            """time_embedding_type""": """fourier""",
            """norm_eps""": 1e-6,
            """mid_block_scale_factor""": math.sqrt(2.0     ),
            """norm_num_groups""": None,
            """down_block_types""": [
                """SkipDownBlock2D""",
                """AttnSkipDownBlock2D""",
                """SkipDownBlock2D""",
                """SkipDownBlock2D""",
            ],
            """up_block_types""": [
                """SkipUpBlock2D""",
                """SkipUpBlock2D""",
                """AttnSkipUpBlock2D""",
                """SkipUpBlock2D""",
            ],
        }
        _UpperCAmelCase          =   self.dummy_input
        return init_dict, inputs_dict







    @slow
    def      _snake_case					(			self			:       Optional[int]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""     ,					output_loading_info=__UpperCamelCase     )
        self.assertIsNotNone(__UpperCamelCase     )
        self.assertEqual(len(loading_info["""missing_keys"""]     )     ,					0     )

        model.to(__UpperCamelCase     )
        _UpperCAmelCase          =   self.dummy_input
        _UpperCAmelCase          =   floats_tensor((4, 3) + (2_56, 2_56)     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   noise
        _UpperCAmelCase          =   model(**__UpperCamelCase     )

        assert image is not None, "Make sure output is not None"







    @slow
    def      _snake_case					(			self			:       Dict     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""     )
        model.to(__UpperCamelCase     )

        _UpperCAmelCase          =   4
        _UpperCAmelCase          =   3
        _UpperCAmelCase          =   (2_56, 2_56)

        _UpperCAmelCase          =   torch.ones((batch_size, num_channels) + sizes     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   torch.tensor(batch_size * [1e-4]     ).to(__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					__UpperCamelCase     ).sample

        _UpperCAmelCase          =   output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        _UpperCAmelCase          =   torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_09_80.71_29, -2_00_28.85_35, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]     )
        # fmt: on

        self.assertTrue(torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					rtol=1e-2     )     )







    def      _snake_case					(			self			:       str     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""     )
        model.to(__UpperCamelCase     )

        _UpperCAmelCase          =   4
        _UpperCAmelCase          =   3
        _UpperCAmelCase          =   (32, 32)

        _UpperCAmelCase          =   torch.ones((batch_size, num_channels) + sizes     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   torch.tensor(batch_size * [1e-4]     ).to(__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					__UpperCamelCase     ).sample

        _UpperCAmelCase          =   output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        _UpperCAmelCase          =   torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]     )
        # fmt: on

        self.assertTrue(torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					rtol=1e-2     )     )







    def      _snake_case					(			self			:       Any     )		->Any:






        '''simple docstring'''






        pass

"""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,
)

a					:	List[str]							=		logging.getLogger(__name__)

a					:	int							=		{'''facebook/bart-base''': BartForConditionalGeneration}
a					:	Dict							=		{'''facebook/bart-base''': BartTokenizer}


def  _UpperCamelCase	(						)    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   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."""      )

    _UpperCAmelCase          =   parser.parse_args()

    return args


def  _UpperCamelCase	(						_A					,      _A="cpu"      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   model_dict[model_name].from_pretrained(_A      ).to(_A      )
    _UpperCAmelCase          =   tokenizer_dict[model_name].from_pretrained(_A      )

    if model_name in ["facebook/bart-base"]:
        _UpperCAmelCase          =   0
        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   0

    return huggingface_model, tokenizer


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  Optional[int]:





    """simple docstring"""

    model.eval()

    _UpperCAmelCase          =   None
    _UpperCAmelCase          =   torch.jit.script(BARTBeamSearchGenerator(_A      )      )

    with torch.no_grad():
        _UpperCAmelCase          =   """My friends are cool but they eat too many carbs."""
        _UpperCAmelCase          =   tokenizer([ARTICLE_TO_SUMMARIZE]					,      max_length=1_0_2_4					,      return_tensors="""pt"""      ).to(model.device      )

        _UpperCAmelCase          =   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=1_4					,      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      )      )

        _UpperCAmelCase          =   remove_dup_initializers(os.path.abspath(_A      )      )

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

        _UpperCAmelCase          =   onnxruntime.InferenceSession(_A      )
        _UpperCAmelCase          =   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  _UpperCamelCase	(						)    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   parse_args()
    _UpperCAmelCase          =   5
    _UpperCAmelCase          =   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          =   torch.device(args.device      )

    _UpperCAmelCase    ,_UpperCAmelCase          =   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:
        _UpperCAmelCase          =   args.max_length

    if args.num_beams:
        _UpperCAmelCase          =   args.num_beams

    if args.output_file_path:
        _UpperCAmelCase          =   args.output_file_path
    else:
        _UpperCAmelCase          =   """BART.onnx"""

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


if __name__ == "__main__":
 main()