
"""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()

"""simple docstring"""



import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
 from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DPTImageProcessor




class 		a_						:







    def __init__(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Optional[int]=2     ,					__UpperCamelCase			:       int=32     ,					__UpperCamelCase			:       Tuple=16     ,					__UpperCamelCase			:       Dict=3     ,					__UpperCamelCase			:       Dict=True     ,					__UpperCamelCase			:       Optional[Any]=True     ,					__UpperCamelCase			:       Optional[Any]=32     ,					__UpperCamelCase			:       Any=4     ,					__UpperCamelCase			:       Optional[int]=[0, 1, 2, 3]     ,					__UpperCamelCase			:       str=4     ,					__UpperCamelCase			:       Optional[Any]=37     ,					__UpperCamelCase			:       str="gelu"     ,					__UpperCamelCase			:       Optional[int]=0.1     ,					__UpperCamelCase			:       Any=0.1     ,					__UpperCamelCase			:       Any=0.0_2     ,					__UpperCamelCase			:       Optional[int]=3     ,					__UpperCamelCase			:       int=[1, 3_84, 24, 24]     ,					__UpperCamelCase			:       Union[str, Any]=True     ,					__UpperCamelCase			:       Any=None     ,					)		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   image_size
        _UpperCAmelCase          =   patch_size
        _UpperCAmelCase          =   num_channels
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   backbone_out_indices
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   intermediate_size
        _UpperCAmelCase          =   hidden_act
        _UpperCAmelCase          =   hidden_dropout_prob
        _UpperCAmelCase          =   attention_probs_dropout_prob
        _UpperCAmelCase          =   initializer_range
        _UpperCAmelCase          =   num_labels
        _UpperCAmelCase          =   backbone_featmap_shape
        _UpperCAmelCase          =   scope
        _UpperCAmelCase          =   is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        _UpperCAmelCase          =   (image_size // patch_size) ** 2
        _UpperCAmelCase          =   num_patches + 1







    def      _snake_case					(			self			:       str     )		->int:






        '''simple docstring'''






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

        _UpperCAmelCase          =   None
        if self.use_labels:
            _UpperCAmelCase          =   ids_tensor([self.batch_size, self.image_size, self.image_size]     ,					self.num_labels     )

        _UpperCAmelCase          =   self.get_config()

        return config, pixel_values, labels







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






        '''simple docstring'''






        _UpperCAmelCase          =   {
            """global_padding""": """same""",
            """layer_type""": """bottleneck""",
            """depths""": [3, 4, 9],
            """out_features""": ["""stage1""", """stage2""", """stage3"""],
            """embedding_dynamic_padding""": True,
            """hidden_sizes""": [96, 1_92, 3_84, 7_68],
            """num_groups""": 2,
        }

        return DPTConfig(
            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     ,					backbone_out_indices=self.backbone_out_indices     ,					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=__UpperCamelCase     ,					initializer_range=self.initializer_range     ,					is_hybrid=self.is_hybrid     ,					backbone_config=__UpperCamelCase     ,					backbone_featmap_shape=self.backbone_featmap_shape     ,					)







    def      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   DPTModel(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     )
        self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.hidden_size)     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.num_labels
        _UpperCAmelCase          =   DPTForDepthEstimation(__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     )
        self.parent.assertEqual(result.predicted_depth.shape     ,					(self.batch_size, self.image_size, self.image_size)     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.num_labels
        _UpperCAmelCase          =   DPTForSemanticSegmentation(__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					labels=__UpperCamelCase     )
        self.parent.assertEqual(
            result.logits.shape     ,					(self.batch_size, self.num_labels, self.image_size, self.image_size)     )







    def      _snake_case					(			self			:       Tuple     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.prepare_config_and_inputs()
        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   config_and_inputs
        _UpperCAmelCase          =   {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				Dict     =				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    a      :				int     =				(
        {
            'depth-estimation': DPTForDepthEstimation,
            'feature-extraction': DPTModel,
            'image-segmentation': DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    a      :				str     =				False
    a      :				List[str]     =				False
    a      :				Dict     =				False







    def      _snake_case					(			self			:       Any     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   DPTModelTester(self     )
        _UpperCAmelCase          =   ConfigTester(self     ,					config_class=__UpperCamelCase     ,					has_text_modality=__UpperCamelCase     ,					hidden_size=37     )







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






        '''simple docstring'''






        self.config_tester.run_common_tests()







    @unittest.skip(reason="""DPT does not use inputs_embeds"""     )
    def      _snake_case					(			self			:       Tuple     )		->Tuple:






        '''simple docstring'''






        pass







    def      _snake_case					(			self			:       int     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

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







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






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

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

            _UpperCAmelCase          =   ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]     ,					__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase     )







    def      _snake_case					(			self			:       str     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase     )







    def      _snake_case					(			self			:       str     )		->Any:






        '''simple docstring'''






        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()
            _UpperCAmelCase          =   True

            if model_class in get_values(__UpperCamelCase     ):
                continue

            _UpperCAmelCase          =   model_class(__UpperCamelCase     )
            model.to(__UpperCamelCase     )
            model.train()
            _UpperCAmelCase          =   self._prepare_for_class(__UpperCamelCase     ,					__UpperCamelCase     ,					return_labels=__UpperCamelCase     )
            _UpperCAmelCase          =   model(**__UpperCamelCase     ).loss
            loss.backward()







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






        '''simple docstring'''






        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()
            _UpperCAmelCase          =   False
            _UpperCAmelCase          =   True

            if model_class in get_values(__UpperCamelCase     ) or not model_class.supports_gradient_checkpointing:
                continue
            _UpperCAmelCase          =   model_class(__UpperCamelCase     )
            model.to(__UpperCamelCase     )
            model.gradient_checkpointing_enable()
            model.train()
            _UpperCAmelCase          =   self._prepare_for_class(__UpperCamelCase     ,					__UpperCamelCase     ,					return_labels=__UpperCamelCase     )
            _UpperCAmelCase          =   model(**__UpperCamelCase     ).loss
            loss.backward()







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






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCAmelCase          =   _config_zero_init(__UpperCamelCase     )
        for model_class in self.all_model_classes:
            _UpperCAmelCase          =   model_class(config=__UpperCamelCase     )
            # Skip the check for the backbone
            _UpperCAmelCase          =   []
            for name, module in model.named_modules():
                if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                    _UpperCAmelCase          =   [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"""     ,					)







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






        '''simple docstring'''






        pass







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






        '''simple docstring'''






        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
            _UpperCAmelCase          =   DPTModel.from_pretrained(__UpperCamelCase     )
            self.assertIsNotNone(__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase          =   """add"""
        with self.assertRaises(__UpperCamelCase     ):
            _UpperCAmelCase          =   DPTForDepthEstimation(__UpperCamelCase     )







def  _UpperCamelCase	(						)    ->  int:





    """simple docstring"""

    _UpperCAmelCase          =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
    return image







@require_torch
@require_vision
@slow
class 		a_						( unittest.TestCase	):







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






        '''simple docstring'''






        _UpperCAmelCase          =   DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""     )
        _UpperCAmelCase          =   DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""     ).to(__UpperCamelCase     )

        _UpperCAmelCase          =   prepare_img()
        _UpperCAmelCase          =   image_processor(images=__UpperCamelCase     ,					return_tensors="""pt"""     ).to(__UpperCamelCase     )

        # forward pass
        with torch.no_grad():
            _UpperCAmelCase          =   model(**__UpperCamelCase     )
            _UpperCAmelCase          =   outputs.predicted_depth

        # verify the predicted depth
        _UpperCAmelCase          =   torch.Size((1, 3_84, 3_84)     )
        self.assertEqual(predicted_depth.shape     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   torch.tensor(
            [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]     ).to(__UpperCamelCase     )

        self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00     ,					__UpperCamelCase     ,					atol=1e-4     )     )