



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,
)

SCREAMING_SNAKE_CASE__       =      logging.getLogger(__name__)

SCREAMING_SNAKE_CASE__       =      {'''facebook/bart-base''': BartForConditionalGeneration}
SCREAMING_SNAKE_CASE__       =      {'''facebook/bart-base''': BartTokenizer}


def A						(       )						->      Optional[int]:
			A__       =    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.'					)

			A__       =    parser.parse_args()

			return args


def A						(       __UpperCamelCase ,	__UpperCamelCase="cpu"					)						->      str:
			A__       =    model_dict[model_name].from_pretrained(__UpperCamelCase					).to(__UpperCamelCase					)
			A__       =    tokenizer_dict[model_name].from_pretrained(__UpperCamelCase					)

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

			return huggingface_model, tokenizer


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			model.eval()

			A__       =    None
			A__       =    torch.jit.script(BARTBeamSearchGenerator(__UpperCamelCase					)					)

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

						A__       =    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					)					)

						A__       =    remove_dup_initializers(os.path.abspath(__UpperCamelCase					)					)

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

						A__       =    onnxruntime.InferenceSession(__UpperCamelCase					)
						A__       =    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 A						(       )						->      Tuple:
			A__       =    parse_args()
			A__       =    5
			A__       =    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__       =    torch.device(args.device					)

			A__     , A__       =    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:
						A__       =    args.max_length

			if args.num_beams:
						A__       =    args.num_beams

			if args.output_file_path:
						A__       =    args.output_file_path
			else:
						A__       =    'BART.onnx'

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


if __name__ == "__main__":
					main()









import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
							from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image


class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       List[Any]				,					_snake_case					:       Any				,					_snake_case					:       Optional[int]=13				,					_snake_case					:       Optional[Any]=64				,					_snake_case					:       List[str]=2				,					_snake_case					:       Any=3				,					_snake_case					:       Union[str, Any]=True				,					_snake_case					:       Dict=True				,					_snake_case					:       int=32				,					_snake_case					:       int=5				,					_snake_case					:       Union[str, Any]=4				,					_snake_case					:       int=37				,					_snake_case					:       Tuple="gelu"				,					_snake_case					:       Optional[int]=0.1				,					_snake_case					:       Dict=0.1				,					_snake_case					:       List[str]=10				,					_snake_case					:       Union[str, Any]=0.02				,					_snake_case					:       Dict=[1, 16, 4, 4]				,					_snake_case					:       Dict=None				,					):






								"""simple docstring"""


								A__       =    parent
								A__       =    batch_size
								A__       =    image_size
								A__       =    patch_size
								A__       =    num_channels
								A__       =    is_training
								A__       =    use_labels
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    intermediate_size
								A__       =    hidden_act
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    type_sequence_label_size
								A__       =    initializer_range
								A__       =    scope
								A__       =    backbone_featmap_shape

								# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								# the number of patches is based on the feature map of the backbone, which by default uses an output stride
								# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
								A__       =    (self.image_size // 32) ** 2
								A__       =    num_patches + 1



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								A__       =    None
								if self.use_labels:
											A__       =    ids_tensor([self.batch_size]				,					self.type_sequence_label_size					)

								A__       =    self.get_config()

								return config, pixel_values, labels



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    {
								    'global_padding': 'same',
								    'layer_type': 'bottleneck',
								    'depths': [3, 4, 9],
								    'out_features': ['stage1', 'stage2', 'stage3'],
								    'embedding_dynamic_padding': True,
								    'hidden_sizes': [4, 8, 16, 32],
								    'num_groups': 2,
								}

								return ViTHybridConfig(
								    image_size=self.image_size				,					patch_size=self.patch_size				,					num_channels=self.num_channels				,					hidden_size=self.hidden_size				,					num_hidden_layers=self.num_hidden_layers				,					num_attention_heads=self.num_attention_heads				,					intermediate_size=self.intermediate_size				,					hidden_act=self.hidden_act				,					hidden_dropout_prob=self.hidden_dropout_prob				,					attention_probs_dropout_prob=self.attention_probs_dropout_prob				,					is_decoder=_snake_case				,					initializer_range=self.initializer_range				,					backbone_featmap_shape=self.backbone_featmap_shape				,					backbone_config=_snake_case				,					)



					def     _a							(     self					:       int				,					_snake_case					:       Optional[int]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    ViTHybridModel(config=_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case					)
								self.parent.assertEqual(result.last_hidden_state.shape				,					(self.batch_size, self.seq_length, self.hidden_size)					)



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






								"""simple docstring"""


								A__       =    self.type_sequence_label_size
								A__       =    ViTHybridForImageClassification(_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case				,					labels=_snake_case					)
								self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.type_sequence_label_size)					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    self.prepare_config_and_inputs()
								A__     , A__     , A__       =    config_and_inputs
								A__       =    {'pixel_values': pixel_values}
								return config, inputs_dict







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






					A__				:		Union[str, Any]			       =    (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					A__				:		str			       =    (
					    {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					A__				:		Union[str, Any]			       =    False
					A__				:		Any			       =    False
					A__				:		Union[str, Any]			       =    False



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    ViTHybridModelTester(self					)
								A__       =    ConfigTester(self				,					config_class=_snake_case				,					has_text_modality=_snake_case				,					hidden_size=37					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self.config_tester.run_common_tests()



					@unittest.skip(reason='ViT does not use inputs_embeds'					)
					def     _a							(     self					:       int					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__       =    model_class(_snake_case					)
											self.assertIsInstance(model.get_input_embeddings()				,					(nn.Module)					)
											A__       =    model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_snake_case				,					nn.Linear					)					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__       =    model_class(_snake_case					)
											A__       =    inspect.signature(model.forward					)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											A__       =    [*signature.parameters.keys()]

											A__       =    ['pixel_values']
											self.assertListEqual(arg_names[:1]				,					_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								A__       =    _config_zero_init(_snake_case					)
								for model_class in self.all_model_classes:
											A__       =    model_class(config=_snake_case					)
											# Skip the check for the backbone
											for name, module in model.named_modules():
														if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																	A__       =    [F'''{name}.{key}''' for key in module.state_dict().keys()]
																	break

											for name, param in model.named_parameters():
														if param.requires_grad:
																	if name in backbone_params:
																				continue
																	self.assertIn(
																	    ((param.data.mean() * 1E9).round() / 1E9).item()				,					[0.0, 1.0]				,					msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,					)



					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__       =    ViTHybridModel.from_pretrained(_snake_case					)
											self.assertIsNotNone(_snake_case					)







def A						(       )						->      Union[str, Any]:
			A__       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
			return image






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






					@cached_property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								return (
								    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
								    if is_vision_available()
								    else None
								)



					@slow
					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(
								    _snake_case					)

								A__       =    self.default_image_processor
								A__       =    prepare_img()
								A__       =    image_processor(images=_snake_case				,					return_tensors='pt'					).to(_snake_case					)

								# forward pass
								with torch.no_grad():
											A__       =    model(**_snake_case					)

								# verify the logits
								A__       =    torch.Size((1, 10_00)					)
								self.assertEqual(outputs.logits.shape				,					_snake_case					)

								A__       =    torch.tensor([-1.9090, -0.4993, -0.2389]					).to(_snake_case					)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]				,					_snake_case				,					atol=1E-4					)					)



					@slow
					@require_accelerate
					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'					)
								A__       =    ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'				,					device_map='auto'					)

								A__       =    prepare_img()

								A__       =    image_processor(images=_snake_case				,					return_tensors='pt'					)
								A__       =    model(**_snake_case					)
								A__       =    outputs.logits
								# model predicts one of the 1000 ImageNet classes
								A__       =    logits.argmax(-1					).item()

								self.assertTrue(model.config.idalabel[predicted_class_idx]				,					'tabby, tabby cat'					)




