






'''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     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=2     ,						_SCREAMING_SNAKE_CASE=32     ,						_SCREAMING_SNAKE_CASE=16     ,						_SCREAMING_SNAKE_CASE=3     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=32     ,						_SCREAMING_SNAKE_CASE=4     ,						_SCREAMING_SNAKE_CASE=[0, 1, 2, 3]     ,						_SCREAMING_SNAKE_CASE=4     ,						_SCREAMING_SNAKE_CASE=37     ,						_SCREAMING_SNAKE_CASE="gelu"     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0.1     ,						_SCREAMING_SNAKE_CASE=0.0_2     ,						_SCREAMING_SNAKE_CASE=3     ,						_SCREAMING_SNAKE_CASE=[1, 384, 24, 24]     ,						_SCREAMING_SNAKE_CASE=True     ,						_SCREAMING_SNAKE_CASE=None     ,						)    -> Optional[int]:



        """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     A__		( self )    -> 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     A__		( self )    -> Tuple:



        """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, 192, 384, 768],
            """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=SCREAMING_SNAKE_CASE_     ,						initializer_range=self.initializer_range     ,						is_hybrid=self.is_hybrid     ,						backbone_config=SCREAMING_SNAKE_CASE_     ,						backbone_featmap_shape=self.backbone_featmap_shape     ,						)

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE )    -> Optional[Any]:



        """simple docstring"""






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

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE )    -> Optional[Any]:



        """simple docstring"""






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

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE )    -> Union[str, Any]:



        """simple docstring"""






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


  def     A__		( self )    -> Optional[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_		(     __lowercase      ,			__lowercase      ,			unittest.TestCase							):
  lowercase							= (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  lowercase							= (
      {
          """depth-estimation""": DPTForDepthEstimation,
          """feature-extraction""": DPTModel,
          """image-segmentation""": DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  lowercase							= False
  lowercase							= False
  lowercase							= False

  def     A__		( self )    -> Any:



        """simple docstring"""






        UpperCamelCase							    =   DPTModelTester(self )
        UpperCamelCase							    =   ConfigTester(self     ,						config_class=SCREAMING_SNAKE_CASE_     ,						has_text_modality=SCREAMING_SNAKE_CASE_     ,						hidden_size=37 )

  def     A__		( self )    -> Any:



        """simple docstring"""






        self.config_tester.run_common_tests()

  @unittest.skip(reason="""DPT does not use inputs_embeds""" )
  def     A__		( self )    -> List[Any]:



        """simple docstring"""






        pass

  def     A__		( self )    -> Tuple:



        """simple docstring"""






        UpperCamelCase    ,UpperCamelCase							    =   self.model_tester.prepare_config_and_inputs_for_common()

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

  def     A__		( self )    -> Tuple:



        """simple docstring"""






        UpperCamelCase    ,UpperCamelCase							    =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase							    =   model_class(SCREAMING_SNAKE_CASE_ )
              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]     ,						SCREAMING_SNAKE_CASE_ )

  def     A__		( self )    -> str:



        """simple docstring"""






        UpperCamelCase							    =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )

  def     A__		( self )    -> Any:



        """simple docstring"""






        UpperCamelCase							    =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE_ )

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



        """simple docstring"""






        UpperCamelCase							    =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_ )

  def     A__		( self )    -> List[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(SCREAMING_SNAKE_CASE_ ):
                    continue

              UpperCamelCase							    =   model_class(SCREAMING_SNAKE_CASE_ )
              model.to(SCREAMING_SNAKE_CASE_ )
              model.train()
              UpperCamelCase							    =   self._prepare_for_class(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						return_labels=SCREAMING_SNAKE_CASE_ )
              UpperCamelCase							    =   model(**SCREAMING_SNAKE_CASE_ ).loss
              loss.backward()

  def     A__		( self )    -> Union[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							    =   False
              UpperCamelCase							    =   True

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

  def     A__		( self )    -> Optional[int]:



        """simple docstring"""






        UpperCamelCase    ,UpperCamelCase							    =   self.model_tester.prepare_config_and_inputs_for_common()

        UpperCamelCase							    =   _config_zero_init(SCREAMING_SNAKE_CASE_ )
        for model_class in self.all_model_classes:
              UpperCamelCase							    =   model_class(config=SCREAMING_SNAKE_CASE_ )
              # 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     A__		( self )    -> Optional[Any]:



        """simple docstring"""






        pass

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



        """simple docstring"""






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


  def     A__		( self )    -> Any:



        """simple docstring"""






        UpperCamelCase    ,UpperCamelCase							    =   self.model_tester.prepare_config_and_inputs_for_common()
        UpperCamelCase							    =   """add"""
        with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
              UpperCamelCase							    =   DPTForDepthEstimation(SCREAMING_SNAKE_CASE_ )







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






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

  def     A__		( self )    -> List[Any]:



        """simple docstring"""






        UpperCamelCase							    =   DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas""" )
        UpperCamelCase							    =   DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas""" ).to(SCREAMING_SNAKE_CASE_ )

        UpperCamelCase							    =   prepare_img()
        UpperCamelCase							    =   image_processor(images=SCREAMING_SNAKE_CASE_     ,						return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE_ )

        # forward pass
        with torch.no_grad():
              UpperCamelCase							    =   model(**SCREAMING_SNAKE_CASE_ )
              UpperCamelCase							    =   outputs.predicted_depth

        # verify the predicted depth
        UpperCamelCase							    =   torch.Size((1, 384, 384) )
        self.assertEqual(predicted_depth.shape     ,						SCREAMING_SNAKE_CASE_ )

        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(SCREAMING_SNAKE_CASE_ )

        self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100     ,						SCREAMING_SNAKE_CASE_     ,						atol=1e-4 ) )


"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			'''huggingface/label-files'''
		SCREAMING_SNAKE_CASE_    			=			'''imagenet-1k-id2label.json'''
		SCREAMING_SNAKE_CASE_    			=			json.load(open(hf_hub_download(__a,				__a,				repo_type='''dataset'''     ),				'''r'''     )     )
		SCREAMING_SNAKE_CASE_    			=			{int(__a     ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_    			=			{v: k for k, v in idalabel.items()}

		SCREAMING_SNAKE_CASE_    			=			'''std_conv''' if '''bit''' in model_name else False

		# note that when using BiT as backbone for ViT-hybrid checkpoints,
		# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
		# config.conv_layer = "std_conv_same"
		SCREAMING_SNAKE_CASE_    			=			BitConfig(
		    conv_layer=__a,				num_labels=1_000,				idalabel=__a,				labelaid=__a,				)

		return config



def 							_lowerCamelCase			(			__a     ):
		if "stem.conv" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''stem.conv''',				'''bit.embedder.convolution'''     )
		if "blocks" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''blocks''',				'''layers'''     )
		if "head.fc" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''head.fc''',				'''classifier.1'''     )
		if name.startswith('''norm'''     ):
				SCREAMING_SNAKE_CASE_    			=			'''bit.''' + name
		if "bit" not in name and "classifier" not in name:
				SCREAMING_SNAKE_CASE_    			=			'''bit.encoder.''' + name

		return name



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     )
		return im



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a,				__a=False     ):
		SCREAMING_SNAKE_CASE_    			=			get_config(__a     )

		# load original model from timm
		SCREAMING_SNAKE_CASE_    			=			create_model(__a,				pretrained=__a     )
		timm_model.eval()

		# load state_dict of original model
		SCREAMING_SNAKE_CASE_    			=			timm_model.state_dict()
		for key in state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			val.squeeze() if '''head''' in key else val

		# load HuggingFace model
		SCREAMING_SNAKE_CASE_    			=			BitForImageClassification(__a     )
		model.eval()
		model.load_state_dict(__a     )

		# create image processor
		SCREAMING_SNAKE_CASE_    			=			create_transform(**resolve_data_config({},				model=__a     )     )
		SCREAMING_SNAKE_CASE_    			=			transform.transforms

		SCREAMING_SNAKE_CASE_    			=			{
		    '''bilinear''': PILImageResampling.BILINEAR,
		    '''bicubic''': PILImageResampling.BICUBIC,
		    '''nearest''': PILImageResampling.NEAREST,
		}

		SCREAMING_SNAKE_CASE_    			=			BitImageProcessor(
		    do_resize=__a,				size={'''shortest_edge''': timm_transforms[0].size},				resample=pillow_resamplings[timm_transforms[0].interpolation.value],				do_center_crop=__a,				crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]},				do_normalize=__a,				image_mean=timm_transforms[-1].mean.tolist(),				image_std=timm_transforms[-1].std.tolist(),				)

		SCREAMING_SNAKE_CASE_    			=			prepare_img()
		SCREAMING_SNAKE_CASE_    			=			transform(__a     ).unsqueeze(0     )
		SCREAMING_SNAKE_CASE_    			=			processor(__a,				return_tensors='''pt'''     ).pixel_values

		# verify pixel values
		assert torch.allclose(__a,				__a     )

		# verify logits
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_    			=			model(__a     )
				SCREAMING_SNAKE_CASE_    			=			outputs.logits

		print('''Logits:''',				logits[0, :3]     )
		print('''Predicted class:''',				model.config.idalabel[logits.argmax(-1     ).item()]     )
		SCREAMING_SNAKE_CASE_    			=			timm_model(__a     )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(__a,				outputs.logits,				atol=1E-3     )
		print('''Looks ok!'''     )

		if pytorch_dump_folder_path is not None:
				Path(__a     ).mkdir(exist_ok=__a     )
				print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'     )
				model.save_pretrained(__a     )
				processor.save_pretrained(__a     )

		if push_to_hub:
				print(F'Pushing model {model_name} and processor to the hub'     )
				model.push_to_hub(F'ybelkada/{model_name}'     )
				processor.push_to_hub(F'ybelkada/{model_name}'     )


if __name__ == "__main__":
						lowerCAmelCase__				=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='resnetv2_50x1_bitm',
						    type=str,
						    help='Name of the BiT timm model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model to the hub.',
						)

						lowerCAmelCase__				=					parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)