



'''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  			=						logging.getLogger(__name__)

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





def 		__snake_case(							)						->					Union[str, Any]:
      snake_case__    :       int       		= 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."""						)

      snake_case__    :       List[str]       		= parser.parse_args()

      return args





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase="cpu"						)						->					int:
      snake_case__    :       List[Any]       		= model_dict[model_name].from_pretrained(_lowerCAmelCase						).to(_lowerCAmelCase						)
      snake_case__    :       List[str]       		= tokenizer_dict[model_name].from_pretrained(_lowerCAmelCase						)

      if model_name in ["facebook/bart-base"]:
            snake_case__    :       str       		= 0
            snake_case__    :       Any       		= None
            snake_case__    :       List[str]       		= 0

      return huggingface_model, tokenizer





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					str:
      model.eval()

      snake_case__    :       List[str]       		= None
      snake_case__    :       List[Any]       		= torch.jit.script(BARTBeamSearchGenerator(_lowerCAmelCase						)						)

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

            snake_case__    :       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						)						)

            snake_case__    :       Union[str, Any]       		= remove_dup_initializers(os.path.abspath(_lowerCAmelCase						)						)

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

            snake_case__    :       Any       		= onnxruntime.InferenceSession(_lowerCAmelCase						)
            snake_case__    :       List[str]       		= 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 		__snake_case(							)						->					List[Any]:
      snake_case__    :       str       		= parse_args()
      snake_case__    :       Optional[Any]       		= 5
      snake_case__    :       Optional[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()

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

      snake_case__		,							snake_case__    :       Dict       		= 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:
            snake_case__    :       int       		= args.max_length

      if args.num_beams:
            snake_case__    :       Any       		= args.num_beams

      if args.output_file_path:
            snake_case__    :       Any       		= args.output_file_path
      else:
            snake_case__    :       Any       		= """BART.onnx"""

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


if __name__ == "__main__":
       main()





'''simple docstring'''





import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwiftFormerForImageClassification, SwiftFormerModel
       from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Any					,	snake_case_   :	List[Any]					,	snake_case_   :	Union[str, Any]=13					,	snake_case_   :	Optional[int]=3					,	snake_case_   :	List[Any]=True					,	snake_case_   :	Dict=True					,	snake_case_   :	Optional[Any]=0.1					,	snake_case_   :	Tuple=0.1					,	snake_case_   :	Dict=224					,	snake_case_   :	Dict=1_000					,	snake_case_   :	Tuple=[3, 3, 6, 4]					,	snake_case_   :	Tuple=[48, 56, 112, 220]					,	):
          snake_case__    :       str       		= parent
          snake_case__    :       Dict       		= batch_size
          snake_case__    :       Dict       		= num_channels
          snake_case__    :       Any       		= is_training
          snake_case__    :       Union[str, Any]       		= use_labels
          snake_case__    :       Tuple       		= hidden_dropout_prob
          snake_case__    :       Optional[int]       		= attention_probs_dropout_prob
          snake_case__    :       List[str]       		= num_labels
          snake_case__    :       Any       		= image_size
          snake_case__    :       str       		= layer_depths
          snake_case__    :       Union[str, Any]       		= embed_dims




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       List[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          snake_case__    :       int       		= None
          if self.use_labels:
                snake_case__    :       List[Any]       		= ids_tensor([self.batch_size]					,	self.num_labels		)

          snake_case__    :       List[str]       		= self.get_config()

          return config, pixel_values, labels




    def lowerCamelCase			(       self   :	str		):
          return SwiftFormerConfig(
              depths=self.layer_depths					,	embed_dims=self.embed_dims					,	mlp_ratio=4					,	downsamples=[True, True, True, True]					,	hidden_act="""gelu"""					,	num_labels=self.num_labels					,	down_patch_size=3					,	down_stride=2					,	down_pad=1					,	drop_rate=0.0					,	drop_path_rate=0.0					,	use_layer_scale=snake_case_					,	layer_scale_init_value=1E-5					,	)




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	Any					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	List[str]		):
          snake_case__    :       Dict       		= SwiftFormerModel(config=snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       List[str]       		= model(snake_case_		)
          self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.embed_dims[-1], 7, 7)		)




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Tuple					,	snake_case_   :	Optional[int]					,	snake_case_   :	Optional[int]		):
          snake_case__    :       Optional[int]       		= self.num_labels
          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       Optional[Any]       		= model(snake_case_					,	labels=snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)

          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()

          snake_case__    :       List[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
          snake_case__    :       Any       		= model(snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)




    def lowerCamelCase			(       self   :	List[Any]		):
          ((snake_case__)		,							(snake_case__)		,							(snake_case__))    :       Union[str, Any]       		= self.prepare_config_and_inputs()
          snake_case__    :       int       		= {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class        UpperCAmelCase_			(			_a						,						_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    lowercase   =						(
        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Optional[Any]       		= SwiftFormerModelTester(self		)
          snake_case__    :       Union[str, Any]       		= ConfigTester(
              self					,	config_class=snake_case_					,	has_text_modality=snake_case_					,	hidden_size=37					,	num_attention_heads=12					,	num_hidden_layers=12					,	)




    def lowerCamelCase			(       self   :	Optional[int]		):
          self.config_tester.run_common_tests()




    @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""		)
    def lowerCamelCase			(       self   :	Optional[Any]		):
          pass




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__		,							snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(snake_case_		)
                snake_case__    :       Any       		= model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case_					,	nn.Linear		)		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__		,							snake_case__    :       Dict       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(snake_case_		)
                snake_case__    :       Any       		= inspect.signature(model.forward		)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case__    :       List[str]       		= [*signature.parameters.keys()]

                snake_case__    :       Optional[int]       		= ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]					,	snake_case_		)




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       List[str]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_		)




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_		)




    @slow
    def lowerCamelCase			(       self   :	List[str]		):
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__    :       Union[str, Any]       		= SwiftFormerModel.from_pretrained(snake_case_		)
                self.assertIsNotNone(snake_case_		)




    @unittest.skip(reason="""SwiftFormer does not output attentions"""		)
    def lowerCamelCase			(       self   :	Union[str, Any]		):
          pass




    def lowerCamelCase			(       self   :	List[Any]		):
          def check_hidden_states_output(snake_case_   :	str					,	snake_case_   :	Tuple					,	snake_case_   :	List[Any]		):
                snake_case__    :       Optional[Any]       		= model_class(snake_case_		)
                model.to(snake_case_		)
                model.eval()

                with torch.no_grad():
                      snake_case__    :       Tuple       		= model(**self._prepare_for_class(snake_case_					,	snake_case_		)		)

                snake_case__    :       Optional[Any]       		= outputs.hidden_states

                snake_case__    :       List[Any]       		= 8
                self.assertEqual(len(snake_case_		)					,	snake_case_		)  # TODO

                # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
                # with the width and height being successively divided by 2, after every 2 blocks
                for i in range(len(snake_case_		)		):
                      self.assertEqual(
                          hidden_states[i].shape					,	torch.Size(
                              [
                                  self.model_tester.batch_size,
                                  self.model_tester.embed_dims[i // 2],
                                  (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                  (self.model_tester.image_size // 4) // 2 ** (i // 2),
                              ]		)					,	)

          snake_case__		,							snake_case__    :       int       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Any       		= True
                check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_		)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                snake_case__    :       str       		= True

                check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          def _config_zero_init(snake_case_   :	Optional[Any]		):
                snake_case__    :       Union[str, Any]       		= copy.deepcopy(snake_case_		)
                for key in configs_no_init.__dict__.keys():
                      if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                            setattr(snake_case_					,	snake_case_					,	1E-1_0		)
                      if isinstance(getattr(snake_case_					,	snake_case_					,	snake_case_		)					,	snake_case_		):
                            snake_case__    :       List[str]       		= _config_zero_init(getattr(snake_case_					,	snake_case_		)		)
                            setattr(snake_case_					,	snake_case_					,	snake_case_		)
                return configs_no_init

          snake_case__		,							snake_case__    :       Tuple       		= self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__    :       List[str]       		= _config_zero_init(snake_case_		)
          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(config=snake_case_		)
                for name, param in model.named_parameters():
                      if param.requires_grad:
                            self.assertIn(
                                ((param.data.mean() * 1E9) / 1E9).round().item()					,	[0.0, 1.0]					,	msg=f"Parameter {name} of model {model_class} seems not properly initialized"					,	)




    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
    def lowerCamelCase			(       self   :	Tuple		):
          pass





def 		__snake_case(							)						->					Optional[int]:
      snake_case__    :       Optional[int]       		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      return image







@require_torch
@require_vision
class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    @cached_property
    def lowerCamelCase			(       self   :	List[str]		):
          return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""		) if is_vision_available() else None




    @slow
    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       str       		= SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""		).to(snake_case_		)

          snake_case__    :       Dict       		= self.default_image_processor
          snake_case__    :       Optional[Any]       		= prepare_img()
          snake_case__    :       Union[str, Any]       		= image_processor(images=snake_case_					,	return_tensors="""pt"""		).to(snake_case_		)

          # forward pass
          with torch.no_grad():
                snake_case__    :       Dict       		= model(**snake_case_		)

          # verify the logits
          snake_case__    :       int       		= torch.Size((1, 1_000)		)
          self.assertEqual(outputs.logits.shape					,	snake_case_		)

          snake_case__    :       int       		= torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]		).to(snake_case_		)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]					,	snake_case_					,	atol=1E-4		)		)
