



'''simple docstring'''






import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
   import torch
   from torch import nn

   from transformers import YolosForObjectDetection, YolosModel
   from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor



class 							lowercase       :


 """simple docstring"""


 def __init__(							self  ,a_  ,a_=13  ,a_=[30, 30]  ,a_=2  ,a_=3  ,a_=True  ,a_=True  ,a_=32  ,a_=5  ,a_=4  ,a_=37  ,a_="gelu"  ,a_=0.1  ,a_=0.1  ,a_=10  ,a_=0.02  ,a_=3  ,a_=None  ,a_=8  ,a_=10  ,)		->   int:
        _UpperCAmelCase  :						str			     = parent
        _UpperCAmelCase  :						int			     = batch_size
        _UpperCAmelCase  :						Tuple			     = image_size
        _UpperCAmelCase  :						Any			     = patch_size
        _UpperCAmelCase  :						Tuple			     = num_channels
        _UpperCAmelCase  :						Tuple			     = is_training
        _UpperCAmelCase  :						str			     = use_labels
        _UpperCAmelCase  :						Optional[int]			     = hidden_size
        _UpperCAmelCase  :						List[str]			     = num_hidden_layers
        _UpperCAmelCase  :						List[Any]			     = num_attention_heads
        _UpperCAmelCase  :						Dict			     = intermediate_size
        _UpperCAmelCase  :						int			     = hidden_act
        _UpperCAmelCase  :						str			     = hidden_dropout_prob
        _UpperCAmelCase  :						Dict			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						Union[str, Any]			     = type_sequence_label_size
        _UpperCAmelCase  :						Union[str, Any]			     = initializer_range
        _UpperCAmelCase  :						str			     = num_labels
        _UpperCAmelCase  :						Dict			     = scope
        _UpperCAmelCase  :						Any			     = n_targets
        _UpperCAmelCase  :						str			     = num_detection_tokens
        # we set the expected sequence length (which is used in several tests)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
        _UpperCAmelCase  :						str			     = (image_size[1] // patch_size) * (image_size[0] // patch_size)
        _UpperCAmelCase  :						Union[str, Any]			     = num_patches + 1 + self.num_detection_tokens







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						Dict			     = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)

        _UpperCAmelCase  :						List[str]			     = None
        if self.use_labels:
               # labels is a list of Dict (each Dict being the labels for a given example in the batch)
               _UpperCAmelCase  :						List[Any]			     = []
               for i in range(self.batch_size							):
                      _UpperCAmelCase  :						Optional[Any]			     = {}
                      _UpperCAmelCase  :						List[Any]			     = torch.randint(
                          high=self.num_labels  ,size=(self.n_targets,)  ,device=a_							)
                      _UpperCAmelCase  :						Tuple			     = torch.rand(self.n_targets  ,4  ,device=a_							)
                      labels.append(a_							)

        _UpperCAmelCase  :						Tuple			     = self.get_config()

        return config, pixel_values, labels







 def _snake_case (							self							)		->   Tuple:
        return YolosConfig(
            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=a_  ,initializer_range=self.initializer_range  ,num_detection_tokens=self.num_detection_tokens  ,num_labels=self.num_labels  ,)







 def _snake_case (							self  ,a_  ,a_  ,a_							)		->   str:
        _UpperCAmelCase  :						List[str]			     = YolosModel(config=a_							)
        model.to(a_							)
        model.eval()
        _UpperCAmelCase  :						Optional[int]			     = model(a_							)
        self.parent.assertEqual(
            result.last_hidden_state.shape  ,(self.batch_size, self.expected_seq_len, self.hidden_size)							)







 def _snake_case (							self  ,a_  ,a_  ,a_							)		->   Union[str, Any]:
        _UpperCAmelCase  :						int			     = YolosForObjectDetection(a_							)
        model.to(a_							)
        model.eval()

        _UpperCAmelCase  :						List[Any]			     = model(pixel_values=a_							)
        _UpperCAmelCase  :						Optional[Any]			     = model(a_							)

        self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
        self.parent.assertEqual(result.pred_boxes.shape  ,(self.batch_size, self.num_detection_tokens, 4)							)

        _UpperCAmelCase  :						Optional[Any]			     = model(pixel_values=a_  ,labels=a_							)

        self.parent.assertEqual(result.loss.shape  ,()							)
        self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
        self.parent.assertEqual(result.pred_boxes.shape  ,(self.batch_size, self.num_detection_tokens, 4)							)







 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						Optional[int]			     = self.prepare_config_and_inputs()
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						List[str]			     = config_and_inputs
        _UpperCAmelCase  :						Tuple			     = {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 							lowercase       (  _lowerCamelCase					,					_lowerCamelCase					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
 UpperCAmelCase							=  (
     {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
 )

 UpperCAmelCase							=  False
 UpperCAmelCase							=  False
 UpperCAmelCase							=  False
 UpperCAmelCase							=  False







 def _snake_case (							self  ,a_  ,a_  ,a_=False							)		->   str:
        _UpperCAmelCase  :						Any			     = super()._prepare_for_class(a_  ,a_  ,return_labels=a_							)

        if return_labels:
               if model_class.__name__ == "YolosForObjectDetection":
                      _UpperCAmelCase  :						Tuple			     = []
                      for i in range(self.model_tester.batch_size							):
                             _UpperCAmelCase  :						Tuple			     = {}
                             _UpperCAmelCase  :						Optional[int]			     = torch.ones(
                                 size=(self.model_tester.n_targets,)  ,device=a_  ,dtype=torch.long							)
                             _UpperCAmelCase  :						Union[str, Any]			     = torch.ones(
                                 self.model_tester.n_targets  ,4  ,device=a_  ,dtype=torch.float							)
                             labels.append(a_							)
                      _UpperCAmelCase  :						Union[str, Any]			     = labels

        return inputs_dict







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						Tuple			     = YolosModelTester(self							)
        _UpperCAmelCase  :						str			     = ConfigTester(self  ,config_class=a_  ,has_text_modality=a_  ,hidden_size=37							)







 def _snake_case (							self							)		->   Union[str, Any]:
        self.config_tester.run_common_tests()







 def _snake_case (							self							)		->   Tuple:
        # YOLOS does not use inputs_embeds
        pass







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _UpperCAmelCase  :						Union[str, Any]			     = model_class(a_							)
               self.assertIsInstance(model.get_input_embeddings()  ,(nn.Module)							)
               _UpperCAmelCase  :						Tuple			     = model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(a_  ,nn.Linear							)							)







 def _snake_case (							self							)		->   int:
        _UpperCAmelCase     ,_UpperCAmelCase  :						Any			     = self.model_tester.prepare_config_and_inputs_for_common()

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

               _UpperCAmelCase  :						Optional[Any]			     = ["""pixel_values"""]
               self.assertListEqual(arg_names[:1]  ,a_							)







 def _snake_case (							self							)		->   int:
        _UpperCAmelCase  :						str			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a_							)







 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase     ,_UpperCAmelCase  :						int			     = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase  :						List[Any]			     = True

        # in YOLOS, the seq_len is different
        _UpperCAmelCase  :						Optional[int]			     = self.model_tester.expected_seq_len
        for model_class in self.all_model_classes:
               _UpperCAmelCase  :						int			     = True
               _UpperCAmelCase  :						List[Any]			     = False
               _UpperCAmelCase  :						int			     = True
               _UpperCAmelCase  :						List[Any]			     = model_class(a_							)
               model.to(a_							)
               model.eval()
               with torch.no_grad():
                      _UpperCAmelCase  :						List[str]			     = model(**self._prepare_for_class(a_  ,a_							)							)
               _UpperCAmelCase  :						Union[str, Any]			     = outputs.attentions
               self.assertEqual(len(a_							)  ,self.model_tester.num_hidden_layers							)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               _UpperCAmelCase  :						Optional[int]			     = True
               _UpperCAmelCase  :						str			     = model_class(a_							)
               model.to(a_							)
               model.eval()
               with torch.no_grad():
                      _UpperCAmelCase  :						Union[str, Any]			     = model(**self._prepare_for_class(a_  ,a_							)							)
               _UpperCAmelCase  :						Dict			     = outputs.attentions
               self.assertEqual(len(a_							)  ,self.model_tester.num_hidden_layers							)

               self.assertListEqual(
                   list(attentions[0].shape[-3:]							)  ,[self.model_tester.num_attention_heads, seq_len, seq_len]  ,)
               _UpperCAmelCase  :						int			     = len(a_							)

               # Check attention is always last and order is fine
               _UpperCAmelCase  :						List[Any]			     = True
               _UpperCAmelCase  :						Tuple			     = True
               _UpperCAmelCase  :						Dict			     = model_class(a_							)
               model.to(a_							)
               model.eval()
               with torch.no_grad():
                      _UpperCAmelCase  :						Union[str, Any]			     = model(**self._prepare_for_class(a_  ,a_							)							)

               _UpperCAmelCase  :						int			     = 1
               self.assertEqual(out_len + added_hidden_states  ,len(a_							)							)

               _UpperCAmelCase  :						Optional[int]			     = outputs.attentions

               self.assertEqual(len(a_							)  ,self.model_tester.num_hidden_layers							)
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]							)  ,[self.model_tester.num_attention_heads, seq_len, seq_len]  ,)







 def _snake_case (							self							)		->   Dict:
        def check_hidden_states_output(a_  ,a_  ,a_							):
               _UpperCAmelCase  :						Union[str, Any]			     = model_class(a_							)
               model.to(a_							)
               model.eval()

               with torch.no_grad():
                      _UpperCAmelCase  :						List[str]			     = model(**self._prepare_for_class(a_  ,a_							)							)

               _UpperCAmelCase  :						List[Any]			     = outputs.hidden_states

               _UpperCAmelCase  :						Union[str, Any]			     = getattr(
                   self.model_tester  ,"""expected_num_hidden_layers"""  ,self.model_tester.num_hidden_layers + 1							)
               self.assertEqual(len(a_							)  ,a_							)

               # YOLOS has a different seq_length
               _UpperCAmelCase  :						Dict			     = self.model_tester.expected_seq_len

               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]							)  ,[seq_length, self.model_tester.hidden_size]  ,)

        _UpperCAmelCase     ,_UpperCAmelCase  :						List[str]			     = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _UpperCAmelCase  :						Tuple			     = True
               check_hidden_states_output(a_  ,a_  ,a_							)

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

               check_hidden_states_output(a_  ,a_  ,a_							)







 def _snake_case (							self							)		->   Union[str, Any]:
        _UpperCAmelCase  :						str			     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*a_							)







 @slow
 def _snake_case (							self							)		->   int:
        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase  :						int			     = YolosModel.from_pretrained(a_							)
               self.assertIsNotNone(a_							)







def     snake_case_					(     )->		List[Any]:





       '''simple docstring'''


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



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


 """simple docstring"""


 @cached_property
 def _snake_case (							self							)		->   int:
        return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""							) if is_vision_available() else None







 @slow
 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""							).to(a_							)

        _UpperCAmelCase  :						str			     = self.default_image_processor
        _UpperCAmelCase  :						Optional[int]			     = prepare_img()
        _UpperCAmelCase  :						Optional[int]			     = image_processor(images=a_  ,return_tensors="""pt"""							).to(a_							)

        # forward pass
        with torch.no_grad():
               _UpperCAmelCase  :						List[str]			     = model(inputs.pixel_values							)

        # verify outputs
        _UpperCAmelCase  :						Union[str, Any]			     = torch.Size((1, 100, 92)							)
        self.assertEqual(outputs.logits.shape  ,a_							)

        _UpperCAmelCase  :						int			     = torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]  ,device=a_  ,)
        _UpperCAmelCase  :						List[str]			     = torch.tensor(
            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]  ,device=a_							)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]  ,a_  ,atol=1E-4							)							)
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]  ,a_  ,atol=1E-4							)							)

        # verify postprocessing
        _UpperCAmelCase  :						List[Any]			     = image_processor.post_process_object_detection(
            a_  ,threshold=0.3  ,target_sizes=[image.size[::-1]]							)[0]
        _UpperCAmelCase  :						List[Any]			     = torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]							).to(a_							)
        _UpperCAmelCase  :						int			     = [75, 75, 17, 63, 17]
        _UpperCAmelCase  :						Tuple			     = torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]							).to(a_							)

        self.assertEqual(len(results["""scores"""]							)  ,5							)
        self.assertTrue(torch.allclose(results["""scores"""]  ,a_  ,atol=1E-4							)							)
        self.assertSequenceEqual(results["""labels"""].tolist()  ,a_							)
        self.assertTrue(torch.allclose(results["""boxes"""][0, :]  ,a_							)							)








'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_							: Optional[Any]						 =	logging.get_logger(__name__)




def     snake_case_					(     lowerCAmelCase_		)->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = DPTConfig()

       if "large" in checkpoint_url:
              _UpperCAmelCase  :						List[Any]			     = 1024
              _UpperCAmelCase  :						Optional[int]			     = 4096
              _UpperCAmelCase  :						Tuple			     = 24
              _UpperCAmelCase  :						List[str]			     = 16
              _UpperCAmelCase  :						str			     = [5, 11, 17, 23]
              _UpperCAmelCase  :						Tuple			     = [256, 512, 1024, 1024]
              _UpperCAmelCase  :						List[str]			     = (1, 384, 384)

       if "ade" in checkpoint_url:
              _UpperCAmelCase  :						Optional[int]			     = True

              _UpperCAmelCase  :						Tuple			     = 150
              _UpperCAmelCase  :						Tuple			     = """huggingface/label-files"""
              _UpperCAmelCase  :						int			     = """ade20k-id2label.json"""
              _UpperCAmelCase  :						List[str]			     = json.load(open(cached_download(hf_hub_url(lowerCAmelCase_       ,   lowerCAmelCase_       ,   repo_type="""dataset"""		)		)       ,   """r"""		)		)
              _UpperCAmelCase  :						List[Any]			     = {int(lowerCAmelCase_		): v for k, v in idalabel.items()}
              _UpperCAmelCase  :						Tuple			     = idalabel
              _UpperCAmelCase  :						Optional[int]			     = {v: k for k, v in idalabel.items()}
              _UpperCAmelCase  :						Optional[int]			     = [1, 150, 480, 480]

       return config, expected_shape




def     snake_case_					(     lowerCAmelCase_		)->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						Tuple			     = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
       for k in ignore_keys:
              state_dict.pop(lowerCAmelCase_       ,   lowerCAmelCase_		)




def     snake_case_					(     lowerCAmelCase_		)->		Any:





       '''simple docstring'''


       if (
           "pretrained.model" in name
           and "cls_token" not in name
           and "pos_embed" not in name
           and "patch_embed" not in name
       ):
              _UpperCAmelCase  :						int			     = name.replace("""pretrained.model"""       ,   """dpt.encoder"""		)
       if "pretrained.model" in name:
              _UpperCAmelCase  :						str			     = name.replace("""pretrained.model"""       ,   """dpt.embeddings"""		)
       if "patch_embed" in name:
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""patch_embed"""       ,   """patch_embeddings"""		)
       if "pos_embed" in name:
              _UpperCAmelCase  :						int			     = name.replace("""pos_embed"""       ,   """position_embeddings"""		)
       if "attn.proj" in name:
              _UpperCAmelCase  :						Dict			     = name.replace("""attn.proj"""       ,   """attention.output.dense"""		)
       if "proj" in name and "project" not in name:
              _UpperCAmelCase  :						List[str]			     = name.replace("""proj"""       ,   """projection"""		)
       if "blocks" in name:
              _UpperCAmelCase  :						Dict			     = name.replace("""blocks"""       ,   """layer"""		)
       if "mlp.fc1" in name:
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""mlp.fc1"""       ,   """intermediate.dense"""		)
       if "mlp.fc2" in name:
              _UpperCAmelCase  :						Dict			     = name.replace("""mlp.fc2"""       ,   """output.dense"""		)
       if "norm1" in name:
              _UpperCAmelCase  :						List[str]			     = name.replace("""norm1"""       ,   """layernorm_before"""		)
       if "norm2" in name:
              _UpperCAmelCase  :						Dict			     = name.replace("""norm2"""       ,   """layernorm_after"""		)
       if "scratch.output_conv" in name:
              _UpperCAmelCase  :						Dict			     = name.replace("""scratch.output_conv"""       ,   """head"""		)
       if "scratch" in name:
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""scratch"""       ,   """neck"""		)
       if "layer1_rn" in name:
              _UpperCAmelCase  :						List[Any]			     = name.replace("""layer1_rn"""       ,   """convs.0"""		)
       if "layer2_rn" in name:
              _UpperCAmelCase  :						Optional[int]			     = name.replace("""layer2_rn"""       ,   """convs.1"""		)
       if "layer3_rn" in name:
              _UpperCAmelCase  :						List[str]			     = name.replace("""layer3_rn"""       ,   """convs.2"""		)
       if "layer4_rn" in name:
              _UpperCAmelCase  :						str			     = name.replace("""layer4_rn"""       ,   """convs.3"""		)
       if "refinenet" in name:
              _UpperCAmelCase  :						Union[str, Any]			     = int(name[len("""neck.refinenet"""		) : len("""neck.refinenet"""		) + 1]		)
              # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
              _UpperCAmelCase  :						Tuple			     = name.replace(F'''refinenet{layer_idx}'''       ,   F'''fusion_stage.layers.{abs(layer_idx-4		)}'''		)
       if "out_conv" in name:
              _UpperCAmelCase  :						List[Any]			     = name.replace("""out_conv"""       ,   """projection"""		)
       if "resConfUnit1" in name:
              _UpperCAmelCase  :						Tuple			     = name.replace("""resConfUnit1"""       ,   """residual_layer1"""		)
       if "resConfUnit2" in name:
              _UpperCAmelCase  :						Union[str, Any]			     = name.replace("""resConfUnit2"""       ,   """residual_layer2"""		)
       if "conv1" in name:
              _UpperCAmelCase  :						int			     = name.replace("""conv1"""       ,   """convolution1"""		)
       if "conv2" in name:
              _UpperCAmelCase  :						List[str]			     = name.replace("""conv2"""       ,   """convolution2"""		)
       # readout blocks
       if "pretrained.act_postprocess1.0.project.0" in name:
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""pretrained.act_postprocess1.0.project.0"""       ,   """neck.reassemble_stage.readout_projects.0.0"""		)
       if "pretrained.act_postprocess2.0.project.0" in name:
              _UpperCAmelCase  :						List[str]			     = name.replace("""pretrained.act_postprocess2.0.project.0"""       ,   """neck.reassemble_stage.readout_projects.1.0"""		)
       if "pretrained.act_postprocess3.0.project.0" in name:
              _UpperCAmelCase  :						Tuple			     = name.replace("""pretrained.act_postprocess3.0.project.0"""       ,   """neck.reassemble_stage.readout_projects.2.0"""		)
       if "pretrained.act_postprocess4.0.project.0" in name:
              _UpperCAmelCase  :						List[Any]			     = name.replace("""pretrained.act_postprocess4.0.project.0"""       ,   """neck.reassemble_stage.readout_projects.3.0"""		)
       # resize blocks
       if "pretrained.act_postprocess1.3" in name:
              _UpperCAmelCase  :						List[str]			     = name.replace("""pretrained.act_postprocess1.3"""       ,   """neck.reassemble_stage.layers.0.projection"""		)
       if "pretrained.act_postprocess1.4" in name:
              _UpperCAmelCase  :						Optional[int]			     = name.replace("""pretrained.act_postprocess1.4"""       ,   """neck.reassemble_stage.layers.0.resize"""		)
       if "pretrained.act_postprocess2.3" in name:
              _UpperCAmelCase  :						int			     = name.replace("""pretrained.act_postprocess2.3"""       ,   """neck.reassemble_stage.layers.1.projection"""		)
       if "pretrained.act_postprocess2.4" in name:
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""pretrained.act_postprocess2.4"""       ,   """neck.reassemble_stage.layers.1.resize"""		)
       if "pretrained.act_postprocess3.3" in name:
              _UpperCAmelCase  :						str			     = name.replace("""pretrained.act_postprocess3.3"""       ,   """neck.reassemble_stage.layers.2.projection"""		)
       if "pretrained.act_postprocess4.3" in name:
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""pretrained.act_postprocess4.3"""       ,   """neck.reassemble_stage.layers.3.projection"""		)
       if "pretrained.act_postprocess4.4" in name:
              _UpperCAmelCase  :						Optional[Any]			     = name.replace("""pretrained.act_postprocess4.4"""       ,   """neck.reassemble_stage.layers.3.resize"""		)
       if "pretrained" in name:
              _UpperCAmelCase  :						Any			     = name.replace("""pretrained"""       ,   """dpt"""		)
       if "bn" in name:
              _UpperCAmelCase  :						Tuple			     = name.replace("""bn"""       ,   """batch_norm"""		)
       if "head" in name:
              _UpperCAmelCase  :						Dict			     = name.replace("""head"""       ,   """head.head"""		)
       if "encoder.norm" in name:
              _UpperCAmelCase  :						List[Any]			     = name.replace("""encoder.norm"""       ,   """layernorm"""		)
       if "auxlayer" in name:
              _UpperCAmelCase  :						List[Any]			     = name.replace("""auxlayer"""       ,   """auxiliary_head.head"""		)

       return name




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''simple docstring'''


       for i in range(config.num_hidden_layers		):
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              _UpperCAmelCase  :						Any			     = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''		)
              _UpperCAmelCase  :						Union[str, Any]			     = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''		)
              # next, add query, keys and values (in that order) to the state dict
              _UpperCAmelCase  :						Optional[int]			     = in_proj_weight[: config.hidden_size, :]
              _UpperCAmelCase  :						Optional[Any]			     = in_proj_bias[: config.hidden_size]
              _UpperCAmelCase  :						Optional[Any]			     = in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              _UpperCAmelCase  :						Tuple			     = in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              _UpperCAmelCase  :						Tuple			     = in_proj_weight[
                  -config.hidden_size :, :
              ]
              _UpperCAmelCase  :						Dict			     = in_proj_bias[-config.hidden_size :]




def     snake_case_					(     )->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase  :						List[str]			     = Image.open(requests.get(lowerCAmelCase_       ,   stream=lowerCAmelCase_		).raw		)
       return im




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Dict:





       '''simple docstring'''


       _UpperCAmelCase     ,_UpperCAmelCase  :						List[str]			     = get_dpt_config(lowerCAmelCase_		)
       # load original state_dict from URL
       _UpperCAmelCase  :						Any			     = torch.hub.load_state_dict_from_url(lowerCAmelCase_       ,   map_location="""cpu"""		)
       # remove certain keys
       remove_ignore_keys_(lowerCAmelCase_		)
       # rename keys
       for key in state_dict.copy().keys():
              _UpperCAmelCase  :						List[Any]			     = state_dict.pop(lowerCAmelCase_		)
              _UpperCAmelCase  :						Dict			     = val
       # read in qkv matrices
       read_in_q_k_v(lowerCAmelCase_       ,   lowerCAmelCase_		)

       # load HuggingFace model
       _UpperCAmelCase  :						Optional[int]			     = DPTForSemanticSegmentation(lowerCAmelCase_		) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase_		)
       model.load_state_dict(lowerCAmelCase_		)
       model.eval()

       # Check outputs on an image
       _UpperCAmelCase  :						Tuple			     = 480 if """ade""" in checkpoint_url else 384
       _UpperCAmelCase  :						List[str]			     = DPTImageProcessor(size=lowerCAmelCase_		)

       _UpperCAmelCase  :						Optional[Any]			     = prepare_img()
       _UpperCAmelCase  :						Dict			     = image_processor(lowerCAmelCase_       ,   return_tensors="""pt"""		)

       # forward pass
       _UpperCAmelCase  :						Optional[Any]			     = model(**lowerCAmelCase_		).logits if """ade""" in checkpoint_url else model(**lowerCAmelCase_		).predicted_depth

       # Assert logits
       _UpperCAmelCase  :						Optional[int]			     = torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]		)
       if "ade" in checkpoint_url:
              _UpperCAmelCase  :						str			     = torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]		)
       assert outputs.shape == torch.Size(lowerCAmelCase_		)
       assert (
           torch.allclose(outputs[0, 0, :3, :3]       ,   lowerCAmelCase_       ,   atol=1e-4		)
           if "ade" in checkpoint_url
           else torch.allclose(outputs[0, :3, :3]       ,   lowerCAmelCase_		)
       )

       Path(lowerCAmelCase_		).mkdir(exist_ok=lowerCAmelCase_		)
       print(F'''Saving model to {pytorch_dump_folder_path}'''		)
       model.save_pretrained(lowerCAmelCase_		)
       print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
       image_processor.save_pretrained(lowerCAmelCase_		)

       if push_to_hub:
              print("""Pushing model to hub..."""		)
              model.push_to_hub(
                  repo_path_or_name=Path(lowerCAmelCase_       ,   lowerCAmelCase_		)       ,   organization="""nielsr"""       ,   commit_message="""Add model"""       ,   use_temp_dir=lowerCAmelCase_       ,   )
              image_processor.push_to_hub(
                  repo_path_or_name=Path(lowerCAmelCase_       ,   lowerCAmelCase_		)       ,   organization="""nielsr"""       ,   commit_message="""Add image processor"""       ,   use_temp_dir=lowerCAmelCase_       ,   )


if __name__ == "__main__":
   A_							: Any						 =	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint_url""",
       default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
       type=str,
       help="""URL of the original DPT checkpoint you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument(
       """--push_to_hub""",
       action="""store_true""",
   )
   parser.add_argument(
       """--model_name""",
       default="""dpt-large""",
       type=str,
       help="""Name of the model, in case you're pushing to the hub.""",
   )

   A_							: List[Any]						 =	parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



