





"""simple docstring"""





import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
       from transformers.models.mobilevitva.modeling_mobilevitva import (
           MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
           make_divisible,
       )


if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor

class 		lowercase					(					_UpperCAmelCase					):





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       self.config_class(**self.inputs_dict )
            self.parent.assertTrue(hasattr(lowercase      ,						"""width_multiplier""" ) )







class 		lowercase					:





      def __init__(       self      ,						lowercase      ,						lowercase=13      ,						lowercase=64      ,						lowercase=2      ,						lowercase=3      ,						lowercase="swish"      ,						lowercase=3      ,						lowercase=32      ,						lowercase=0.1      ,						lowercase=0.02      ,						lowercase=True      ,						lowercase=True      ,						lowercase=10      ,						lowercase=None      ,						lowercase=0.25      ,						lowercase=0.0      ,						lowercase=0.0      ,						)		->     List[str]:
            lowerCAmelCase  						=       parent
            lowerCAmelCase  						=       batch_size
            lowerCAmelCase  						=       image_size
            lowerCAmelCase  						=       patch_size
            lowerCAmelCase  						=       num_channels
            lowerCAmelCase  						=       make_divisible(512 * width_multiplier      ,						divisor=8 )
            lowerCAmelCase  						=       hidden_act
            lowerCAmelCase  						=       conv_kernel_size
            lowerCAmelCase  						=       output_stride
            lowerCAmelCase  						=       classifier_dropout_prob
            lowerCAmelCase  						=       use_labels
            lowerCAmelCase  						=       is_training
            lowerCAmelCase  						=       num_labels
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       scope
            lowerCAmelCase  						=       width_multiplier
            lowerCAmelCase  						=       ffn_dropout
            lowerCAmelCase  						=       attn_dropout





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

            lowerCAmelCase  						=       self.get_config()

            return config, pixel_values, labels, pixel_labels





      def     _snake_case			(       self )		->     List[str]:
            return MobileViTVaConfig(
                image_size=self.image_size      ,						patch_size=self.patch_size      ,						num_channels=self.num_channels      ,						hidden_act=self.hidden_act      ,						conv_kernel_size=self.conv_kernel_size      ,						output_stride=self.output_stride      ,						classifier_dropout_prob=self.classifier_dropout_prob      ,						initializer_range=self.initializer_range      ,						width_multiplier=self.width_multiplier      ,						ffn_dropout=self.ffn_dropout_prob      ,						attn_dropout=self.attn_dropout_prob      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase )		->     List[Any]:
            lowerCAmelCase  						=       MobileViTVaModel(config=lowercase )
            model.to(lowercase )
            model.eval()
            lowerCAmelCase  						=       model(lowercase )
            self.parent.assertEqual(
                result.last_hidden_state.shape      ,						(
                    self.batch_size,
                    self.last_hidden_size,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.num_labels
            lowerCAmelCase  						=       MobileViTVaForImageClassification(lowercase )
            model.to(lowercase )
            model.eval()
            lowerCAmelCase  						=       model(lowercase      ,						labels=lowercase )
            self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_labels) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase )		->     Optional[int]:
            lowerCAmelCase  						=       self.num_labels
            lowerCAmelCase  						=       MobileViTVaForSemanticSegmentation(lowercase )
            model.to(lowercase )
            model.eval()
            lowerCAmelCase  						=       model(lowercase )
            self.parent.assertEqual(
                result.logits.shape      ,						(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )      ,						)
            lowerCAmelCase  						=       model(lowercase      ,						labels=lowercase )
            self.parent.assertEqual(
                result.logits.shape      ,						(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )      ,						)





      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       self.prepare_config_and_inputs()
            lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       config_and_inputs
            lowerCAmelCase  						=       {"""pixel_values""": pixel_values}
            return config, inputs_dict







@require_torch
class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      (
          (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )

      _SCREAMING_SNAKE_CASE					=      (
          {
              'feature-extraction': MobileViTVaModel,
              'image-classification': MobileViTVaForImageClassification,
              'image-segmentation': MobileViTVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False





      def     _snake_case			(       self )		->     Dict:
            lowerCAmelCase  						=       MobileViTVaModelTester(self )
            lowerCAmelCase  						=       MobileViTVaConfigTester(self      ,						config_class=lowercase      ,						has_text_modality=lowercase )





      def     _snake_case			(       self )		->     Optional[int]:
            self.config_tester.run_common_tests()





      @unittest.skip(reason="""MobileViTV2 does not use inputs_embeds""" )
      def     _snake_case			(       self )		->     List[str]:
            pass





      @unittest.skip(reason="""MobileViTV2 does not support input and output embeddings""" )
      def     _snake_case			(       self )		->     Optional[Any]:
            pass





      @unittest.skip(reason="""MobileViTV2 does not output attentions""" )
      def     _snake_case			(       self )		->     Any:
            pass





      @require_torch_multi_gpu
      @unittest.skip(reason="""Got `CUDA error: misaligned address` for tests after this one being run.""" )
      def     _snake_case			(       self )		->     str:
            pass





      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
      def     _snake_case			(       self )		->     List[Any]:
            pass





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowerCAmelCase  						=       ["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]      ,						lowercase )





      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase )





      def     _snake_case			(       self )		->     List[str]:
            def check_hidden_states_output(lowercase      ,						lowercase      ,						lowercase ):
                  lowerCAmelCase  						=       model_class(lowercase )
                  model.to(lowercase )
                  model.eval()

                  with torch.no_grad():
                        lowerCAmelCase  						=       model(**self._prepare_for_class(lowercase      ,						lowercase ) )

                  lowerCAmelCase  						=       outputs.hidden_states

                  lowerCAmelCase  						=       5
                  self.assertEqual(len(lowercase )      ,						lowercase )

                  # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
                  # with the width and height being successively divided by 2.
                  lowerCAmelCase  						=       2
                  for i in range(len(lowercase ) ):
                        self.assertListEqual(
                            list(hidden_states[i].shape[-2:] )      ,						[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]      ,						)
                        divisor *= 2

                  self.assertEqual(self.model_tester.output_stride      ,						divisor // 2 )

            lowerCAmelCase						,		lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowerCAmelCase  						=       True
                  check_hidden_states_output(lowercase      ,						lowercase      ,						lowercase )

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowerCAmelCase  						=       True

                  check_hidden_states_output(lowercase      ,						lowercase      ,						lowercase )





      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowercase )





      def     _snake_case			(       self )		->     Dict:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*lowercase )





      @slow
      def     _snake_case			(       self )		->     Union[str, Any]:
            for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase  						=       MobileViTVaModel.from_pretrained(lowercase )
                  self.assertIsNotNone(lowercase )







def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




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






@require_torch
@require_vision
class 		lowercase					(					unittest.TestCase					):





      @cached_property
      def     _snake_case			(       self )		->     List[str]:
            return (
                MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256""" )
                if is_vision_available()
                else None
            )





      @slow
      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256""" ).to(
                lowercase )

            lowerCAmelCase  						=       self.default_image_processor
            lowerCAmelCase  						=       prepare_img()
            lowerCAmelCase  						=       image_processor(images=lowercase      ,						return_tensors="""pt""" ).to(lowercase )

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase  						=       model(**lowercase )

            # verify the logits
            lowerCAmelCase  						=       torch.Size((1, 1_000) )
            self.assertEqual(outputs.logits.shape      ,						lowercase )

            lowerCAmelCase  						=       torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01] ).to(lowercase )

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





      @slow
      def     _snake_case			(       self )		->     Dict:
            lowerCAmelCase  						=       MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3""" )
            lowerCAmelCase  						=       model.to(lowercase )

            lowerCAmelCase  						=       MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3""" )

            lowerCAmelCase  						=       prepare_img()
            lowerCAmelCase  						=       image_processor(images=lowercase      ,						return_tensors="""pt""" ).to(lowercase )

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase  						=       model(**lowercase )
            lowerCAmelCase  						=       outputs.logits

            # verify the logits
            lowerCAmelCase  						=       torch.Size((1, 21, 32, 32) )
            self.assertEqual(logits.shape      ,						lowercase )

            lowerCAmelCase  						=       torch.tensor(
                [
                    [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
                    [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
                    [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
                ]      ,						device=lowercase      ,						)

            self.assertTrue(torch.allclose(logits[0, :3, :3, :3]      ,						lowercase      ,						atol=1e-4 ) )





      @slow
      def     _snake_case			(       self )		->     Dict:
            lowerCAmelCase  						=       MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3""" )
            lowerCAmelCase  						=       model.to(lowercase )

            lowerCAmelCase  						=       MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3""" )

            lowerCAmelCase  						=       prepare_img()
            lowerCAmelCase  						=       image_processor(images=lowercase      ,						return_tensors="""pt""" ).to(lowercase )

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase  						=       model(**lowercase )

            lowerCAmelCase  						=       outputs.logits.detach().cpu()

            lowerCAmelCase  						=       image_processor.post_process_semantic_segmentation(outputs=lowercase      ,						target_sizes=[(50, 60)] )
            lowerCAmelCase  						=       torch.Size((50, 60) )
            self.assertEqual(segmentation[0].shape      ,						lowercase )

            lowerCAmelCase  						=       image_processor.post_process_semantic_segmentation(outputs=lowercase )
            lowerCAmelCase  						=       torch.Size((32, 32) )
            self.assertEqual(segmentation[0].shape      ,						lowercase )













"""simple docstring"""





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      lowerCAmelCase  						=       OrderedDict()
      for key, value in state_dict.items():
            if key.startswith("""module.encoder"""			):
                  lowerCAmelCase  						=       key.replace("""module.encoder"""							,						"""glpn.encoder"""			)
            if key.startswith("""module.decoder"""			):
                  lowerCAmelCase  						=       key.replace("""module.decoder"""							,						"""decoder.stages"""			)
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  lowerCAmelCase  						=       key[key.find("""patch_embed"""			) + len("""patch_embed"""			)]
                  lowerCAmelCase  						=       key.replace(F'patch_embed{idx}'							,						F'patch_embeddings.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "norm" in key:
                  lowerCAmelCase  						=       key.replace("""norm"""							,						"""layer_norm"""			)
            if "glpn.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  lowerCAmelCase  						=       key[key.find("""glpn.encoder.layer_norm"""			) + len("""glpn.encoder.layer_norm"""			)]
                  lowerCAmelCase  						=       key.replace(F'layer_norm{idx}'							,						F'layer_norm.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "layer_norm1" in key:
                  lowerCAmelCase  						=       key.replace("""layer_norm1"""							,						"""layer_norm_1"""			)
            if "layer_norm2" in key:
                  lowerCAmelCase  						=       key.replace("""layer_norm2"""							,						"""layer_norm_2"""			)
            if "block" in key:
                  # replace for example block1 by block.0
                  lowerCAmelCase  						=       key[key.find("""block"""			) + len("""block"""			)]
                  lowerCAmelCase  						=       key.replace(F'block{idx}'							,						F'block.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "attn.q" in key:
                  lowerCAmelCase  						=       key.replace("""attn.q"""							,						"""attention.self.query"""			)
            if "attn.proj" in key:
                  lowerCAmelCase  						=       key.replace("""attn.proj"""							,						"""attention.output.dense"""			)
            if "attn" in key:
                  lowerCAmelCase  						=       key.replace("""attn"""							,						"""attention.self"""			)
            if "fc1" in key:
                  lowerCAmelCase  						=       key.replace("""fc1"""							,						"""dense1"""			)
            if "fc2" in key:
                  lowerCAmelCase  						=       key.replace("""fc2"""							,						"""dense2"""			)
            if "linear_pred" in key:
                  lowerCAmelCase  						=       key.replace("""linear_pred"""							,						"""classifier"""			)
            if "linear_fuse" in key:
                  lowerCAmelCase  						=       key.replace("""linear_fuse.conv"""							,						"""linear_fuse"""			)
                  lowerCAmelCase  						=       key.replace("""linear_fuse.bn"""							,						"""batch_norm"""			)
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  lowerCAmelCase  						=       key[key.find("""linear_c"""			) + len("""linear_c"""			)]
                  lowerCAmelCase  						=       key.replace(F'linear_c{idx}'							,						F'linear_c.{int(SCREAMING_SNAKE_CASE			)-1}'			)
            if "bot_conv" in key:
                  lowerCAmelCase  						=       key.replace("""bot_conv"""							,						"""0.convolution"""			)
            if "skip_conv1" in key:
                  lowerCAmelCase  						=       key.replace("""skip_conv1"""							,						"""1.convolution"""			)
            if "skip_conv2" in key:
                  lowerCAmelCase  						=       key.replace("""skip_conv2"""							,						"""2.convolution"""			)
            if "fusion1" in key:
                  lowerCAmelCase  						=       key.replace("""fusion1"""							,						"""1.fusion"""			)
            if "fusion2" in key:
                  lowerCAmelCase  						=       key.replace("""fusion2"""							,						"""2.fusion"""			)
            if "fusion3" in key:
                  lowerCAmelCase  						=       key.replace("""fusion3"""							,						"""3.fusion"""			)
            if "fusion" in key and "conv" in key:
                  lowerCAmelCase  						=       key.replace("""conv"""							,						"""convolutional_layer"""			)
            if key.startswith("""module.last_layer_depth"""			):
                  lowerCAmelCase  						=       key.replace("""module.last_layer_depth"""							,						"""head.head"""			)
            lowerCAmelCase  						=       value

      return new_state_dict


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						List[str]			):







      '''simple docstring'''




      for i in range(config.num_encoder_blocks			):
            for j in range(config.depths[i]			):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  lowerCAmelCase  						=       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'			)
                  lowerCAmelCase  						=       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias'			)
                  # next, add keys and values (in that order) to the state dict
                  lowerCAmelCase  						=       kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCAmelCase  						=       kv_bias[: config.hidden_sizes[i]]
                  lowerCAmelCase  						=       kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCAmelCase  						=       kv_bias[config.hidden_sizes[i] :]


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCAmelCase  						=       Image.open(requests.get(SCREAMING_SNAKE_CASE							,						stream=SCREAMING_SNAKE_CASE			).raw			)

      return image


@torch.no_grad()
def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=False							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=None			):







      '''simple docstring'''




      lowerCAmelCase  						=       GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]							,						decoder_hidden_size=64							,						depths=[3, 8, 27, 3]			)

      # load image processor (only resize + rescale)
      lowerCAmelCase  						=       GLPNImageProcessor()

      # prepare image
      lowerCAmelCase  						=       prepare_img()
      lowerCAmelCase  						=       image_processor(images=SCREAMING_SNAKE_CASE							,						return_tensors="""pt"""			).pixel_values

      logger.info("""Converting model..."""			)

      # load original state dict
      lowerCAmelCase  						=       torch.load(SCREAMING_SNAKE_CASE							,						map_location=torch.device("""cpu"""			)			)

      # rename keys
      lowerCAmelCase  						=       rename_keys(SCREAMING_SNAKE_CASE			)

      # key and value matrices need special treatment
      read_in_k_v(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      # create HuggingFace model and load state dict
      lowerCAmelCase  						=       GLPNForDepthEstimation(SCREAMING_SNAKE_CASE			)
      model.load_state_dict(SCREAMING_SNAKE_CASE			)
      model.eval()

      # forward pass
      lowerCAmelCase  						=       model(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]			)
            elif "kitti" in model_name:
                  lowerCAmelCase  						=       torch.tensor(
                      [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]			)
            else:
                  raise ValueError(F'Unknown model name: {model_name}'			)

            lowerCAmelCase  						=       torch.Size([1, 4_80, 6_40]			)

            assert predicted_depth.shape == expected_shape
            assert torch.allclose(predicted_depth[0, :3, :3]							,						SCREAMING_SNAKE_CASE							,						atol=1e-4			)
            print("""Looks ok!"""			)

      # finally, push to hub if required
      if push_to_hub:
            logger.info("""Pushing model and image processor to the hub..."""			)
            model.push_to_hub(
                repo_path_or_name=Path(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)							,						organization="""nielsr"""							,						commit_message="""Add model"""							,						use_temp_dir=SCREAMING_SNAKE_CASE							,						)
            image_processor.push_to_hub(
                repo_path_or_name=Path(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)							,						organization="""nielsr"""							,						commit_message="""Add image processor"""							,						use_temp_dir=SCREAMING_SNAKE_CASE							,						)


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   argparse.ArgumentParser()

       parser.add_argument(
           "--checkpoint_path",
           default=None,
           type=str,
           help="Path to the original PyTorch checkpoint (.pth file).",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
       )
       parser.add_argument(
           "--model_name",
           default="glpn-kitti",
           type=str,
           help="Name of the model in case you're pushing to the hub.",
       )
       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






