


import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       LevitForImageClassification,
       LevitForImageClassificationWithTeacher,
       LevitModel,
   )
   from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor






class   UpperCamelCase       (		_SCREAMING_SNAKE_CASE  ):


   '''simple docstring'''







   def 		UpperCamelCase	(  self		):
        lowercase_							:Optional[Any]			     =							self.config_class(**self.inputs_dict		)
        self.parent.assertTrue(hasattr(lowerCAmelCase_ ,				'''hidden_sizes'''		)		)
        self.parent.assertTrue(hasattr(lowerCAmelCase_ ,				'''num_attention_heads'''		)		)






class   UpperCamelCase       :


   '''simple docstring'''







   def __init__(  self ,				UpperCamelCase_ ,				UpperCamelCase_=13 ,				UpperCamelCase_=64 ,				UpperCamelCase_=3 ,				UpperCamelCase_=3 ,				UpperCamelCase_=2 ,				UpperCamelCase_=1 ,				UpperCamelCase_=16 ,				UpperCamelCase_=[128, 256, 384] ,				UpperCamelCase_=[4, 6, 8] ,				UpperCamelCase_=[2, 3, 4] ,				UpperCamelCase_=[16, 16, 16] ,				UpperCamelCase_=0 ,				UpperCamelCase_=[2, 2, 2] ,				UpperCamelCase_=[2, 2, 2] ,				UpperCamelCase_=0.02 ,				UpperCamelCase_=True ,				UpperCamelCase_=True ,				UpperCamelCase_=2 ,				):
        lowercase_							:Dict			     =							parent
        lowercase_							:Dict			     =							batch_size
        lowercase_							:List[str]			     =							image_size
        lowercase_							:Tuple			     =							num_channels
        lowercase_							:List[str]			     =							kernel_size
        lowercase_							:Union[str, Any]			     =							stride
        lowercase_							:Optional[int]			     =							padding
        lowercase_							:Dict			     =							hidden_sizes
        lowercase_							:Dict			     =							num_attention_heads
        lowercase_							:str			     =							depths
        lowercase_							:Any			     =							key_dim
        lowercase_							:Optional[int]			     =							drop_path_rate
        lowercase_							:List[Any]			     =							patch_size
        lowercase_							:List[Any]			     =							attention_ratio
        lowercase_							:Union[str, Any]			     =							mlp_ratio
        lowercase_							:List[str]			     =							initializer_range
        lowercase_							:Optional[Any]			     =							[
            ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
            ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
        ]
        lowercase_							:Optional[int]			     =							is_training
        lowercase_							:Dict			     =							use_labels
        lowercase_							:List[str]			     =							num_labels
        lowercase_							:Any			     =							initializer_range
   def 		UpperCamelCase	(  self		):
        lowercase_							:int			     =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        lowercase_							:int			     =							None
        if self.use_labels:
             lowercase_							:Tuple			     =							ids_tensor([self.batch_size] ,				self.num_labels		)

        lowercase_							:List[Any]			     =							self.get_config()
        return config, pixel_values, labels
   def 		UpperCamelCase	(  self		):
        return LevitConfig(
            image_size=self.image_size ,				num_channels=self.num_channels ,				kernel_size=self.kernel_size ,				stride=self.stride ,				padding=self.padding ,				patch_size=self.patch_size ,				hidden_sizes=self.hidden_sizes ,				num_attention_heads=self.num_attention_heads ,				depths=self.depths ,				key_dim=self.key_dim ,				drop_path_rate=self.drop_path_rate ,				mlp_ratio=self.mlp_ratio ,				attention_ratio=self.attention_ratio ,				initializer_range=self.initializer_range ,				down_ops=self.down_ops ,				)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_		):
        lowercase_							:str			     =							LevitModel(config=lowerCAmelCase_		)
        model.to(lowerCAmelCase_		)
        model.eval()
        lowercase_							:Dict			     =							model(lowerCAmelCase_		)
        lowercase_							:Union[str, Any]			     =							(self.image_size, self.image_size)
        lowercase_							:List[str]			     =							image_size[0], image_size[1]
        for _ in range(4		):
             lowercase_							:str			     =							floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
             lowercase_							:List[Any]			     =							floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
        self.parent.assertEqual(
            result.last_hidden_state.shape ,				(self.batch_size, ceil(height / 4		) * ceil(width / 4		), self.hidden_sizes[-1]) ,				)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_		):
        lowercase_							:int			     =							self.num_labels
        lowercase_							:int			     =							LevitForImageClassification(lowerCAmelCase_		)
        model.to(lowerCAmelCase_		)
        model.eval()
        lowercase_							:Optional[Any]			     =							model(lowerCAmelCase_ ,				labels=lowerCAmelCase_		)
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)		)




   def 		UpperCamelCase	(  self		):
        lowercase_							:Tuple			     =							self.prepare_config_and_inputs()
        lowercase_							:List[Any]			     =							config_and_inputs
        lowercase_							:List[str]			     =							{"""pixel_values""": pixel_values}
        return config, inputs_dict






@require_torch
class   UpperCamelCase       (		_SCREAMING_SNAKE_CASE    ,   _SCREAMING_SNAKE_CASE    ,   unittest.TestCase  ):


   '''simple docstring'''







   lowercase       :						List[Any]          =(
       (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
       if is_torch_available()
       else ()
   )
   lowercase       :						Tuple          =(
       {
           """feature-extraction""": LevitModel,
           """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
       }
       if is_torch_available()
       else {}
   )

   lowercase       :						Optional[Any]          =False
   lowercase       :						List[str]          =False
   lowercase       :						List[str]          =False
   lowercase       :						Dict          =False
   lowercase       :						Dict          =False
   def 		UpperCamelCase	(  self		):
        lowercase_							:Any			     =							LevitModelTester(self		)
        lowercase_							:Dict			     =							ConfigTester(self ,				config_class=lowerCAmelCase_ ,				has_text_modality=lowerCAmelCase_ ,				hidden_size=37		)
   def 		UpperCamelCase	(  self		):
        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()
   def 		UpperCamelCase	(  self		):
        return
   @unittest.skip(reason='''Levit does not use inputs_embeds'''		)
   def 		UpperCamelCase	(  self		):
        pass
   @unittest.skip(reason='''Levit does not support input and output embeddings'''		)
   def 		UpperCamelCase	(  self		):
        pass
   @unittest.skip(reason='''Levit does not output attentions'''		)
   def 		UpperCamelCase	(  self		):
        pass
   def 		UpperCamelCase	(  self		):
        lowercase_							:List[Any]			     =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_							:Dict			     =							model_class(lowerCAmelCase_		)
             lowercase_							:Union[str, Any]			     =							inspect.signature(model.forward		)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowercase_							:Optional[int]			     =							[*signature.parameters.keys()]

             lowercase_							:List[str]			     =							["""pixel_values"""]
             self.assertListEqual(arg_names[:1] ,				lowerCAmelCase_		)
   def 		UpperCamelCase	(  self		):
        def check_hidden_states_output(UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_		):
             lowercase_							:str			     =							model_class(lowerCAmelCase_		)
             model.to(lowerCAmelCase_		)
             model.eval()

             with torch.no_grad():
                  lowercase_							:Any			     =							model(**self._prepare_for_class(lowerCAmelCase_ ,				lowerCAmelCase_		)		)

             lowercase_							:Optional[int]			     =							outputs.hidden_states

             lowercase_							:List[Any]			     =							len(self.model_tester.depths		) + 1
             self.assertEqual(len(lowerCAmelCase_		) ,				lowerCAmelCase_		)

             lowercase_							:List[str]			     =							(self.model_tester.image_size, self.model_tester.image_size)
             lowercase_							:Optional[Any]			     =							image_size[0], image_size[1]
             for _ in range(4		):
                  lowercase_							:List[Any]			     =							floor(
                      (
                          (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                          / self.model_tester.stride
                      )
                      + 1		)
                  lowercase_							:str			     =							floor(
                      (
                          (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                          / self.model_tester.stride
                      )
                      + 1		)
             # verify the first hidden states (first block)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]		) ,				[
                     height * width,
                     self.model_tester.hidden_sizes[0],
                 ] ,				)

        lowercase_							:str			     =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_							:List[Any]			     =							True
             check_hidden_states_output(lowerCAmelCase_ ,				lowerCAmelCase_ ,				lowerCAmelCase_		)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_							:Dict			     =							True

             check_hidden_states_output(lowerCAmelCase_ ,				lowerCAmelCase_ ,				lowerCAmelCase_		)
   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
   def 		UpperCamelCase	(  self		):
        pass
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_=False		):
        lowercase_							:Optional[int]			     =							super()._prepare_for_class(lowerCAmelCase_ ,				lowerCAmelCase_ ,				return_labels=lowerCAmelCase_		)

        if return_labels:
             if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                  del inputs_dict["labels"]

        return inputs_dict
   def 		UpperCamelCase	(  self		):
        lowercase_							:List[str]			     =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_		)
   def 		UpperCamelCase	(  self		):
        lowercase_							:Any			     =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_		)
   def 		UpperCamelCase	(  self		):
        if not self.model_tester.is_training:
             return

        lowercase_							:Optional[int]			     =							self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_							:Dict			     =							True

        for model_class in self.all_model_classes:
             # LevitForImageClassificationWithTeacher supports inference-only
             if (
                 model_class in get_values(lowerCAmelCase_		)
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                  continue
             lowercase_							:int			     =							model_class(lowerCAmelCase_		)
             model.to(lowerCAmelCase_		)
             model.train()
             lowercase_							:Dict			     =							self._prepare_for_class(lowerCAmelCase_ ,				lowerCAmelCase_ ,				return_labels=lowerCAmelCase_		)
             lowercase_							:Union[str, Any]			     =							model(**lowerCAmelCase_		).loss
             loss.backward()
   def 		UpperCamelCase	(  self		):
        lowercase_							:List[str]			     =							self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
             return

        lowercase_							:List[Any]			     =							False
        lowercase_							:List[Any]			     =							True

        for model_class in self.all_model_classes:
             if model_class in get_values(lowerCAmelCase_		) or not model_class.supports_gradient_checkpointing:
                  continue
             # LevitForImageClassificationWithTeacher supports inference-only
             if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                  continue
             lowercase_							:str			     =							model_class(lowerCAmelCase_		)
             model.gradient_checkpointing_enable()
             model.to(lowerCAmelCase_		)
             model.train()
             lowercase_							:Union[str, Any]			     =							self._prepare_for_class(lowerCAmelCase_ ,				lowerCAmelCase_ ,				return_labels=lowerCAmelCase_		)
             lowercase_							:Optional[int]			     =							model(**lowerCAmelCase_		).loss
             loss.backward()
   def 		UpperCamelCase	(  self		):
        lowercase_							:Dict			     =							self.model_tester.prepare_config_and_inputs_for_common()

        lowercase_							:Optional[int]			     =							[
            {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
            {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
            {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
        ]

        for model_class in self.all_model_classes:
             if (
                 model_class
                 not in [
                     *get_values(lowerCAmelCase_		),
                 ]
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                  continue

             for problem_type in problem_types:
                  with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"		):
                       lowercase_							:str			     =							problem_type["""title"""]
                       lowercase_							:Optional[int]			     =							problem_type["""num_labels"""]

                       lowercase_							:str			     =							model_class(lowerCAmelCase_		)
                       model.to(lowerCAmelCase_		)
                       model.train()

                       lowercase_							:int			     =							self._prepare_for_class(lowerCAmelCase_ ,				lowerCAmelCase_ ,				return_labels=lowerCAmelCase_		)

                       if problem_type["num_labels"] > 1:
                            lowercase_							:List[str]			     =							inputs["""labels"""].unsqueeze(1		).repeat(1 ,				problem_type['''num_labels''']		)

                       lowercase_							:Union[str, Any]			     =							inputs["""labels"""].to(problem_type['''dtype''']		)

                       # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                       # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                       # they have the same size." which is a symptom something in wrong for the regression problem.
                       # See https://github.com/huggingface/transformers/issues/11780
                       with warnings.catch_warnings(record=lowerCAmelCase_		) as warning_list:
                            lowercase_							:List[str]			     =							model(**lowerCAmelCase_		).loss
                       for w in warning_list:
                            if "Using a target size that is different to the input size" in str(w.message		):
                                 raise ValueError(
                                     f"Something is going wrong in the regression problem: intercepted {w.message}"		)

                       loss.backward()




   @slow
   def 		UpperCamelCase	(  self		):
        for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_							:Any			     =							LevitModel.from_pretrained(lowerCAmelCase_		)
             self.assertIsNotNone(lowerCAmelCase_		)




def      UpperCamelCase     (   )       ->				int:
     '''simple docstring'''





     lowercase_							:str			     =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
     return image






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


   '''simple docstring'''







   @cached_property
   def 		UpperCamelCase	(  self		):
        return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)




   @slow
   def 		UpperCamelCase	(  self		):
        lowercase_							:Optional[Any]			     =							LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
            lowerCAmelCase_		)

        lowercase_							:str			     =							self.default_image_processor
        lowercase_							:Tuple			     =							prepare_img()
        lowercase_							:Tuple			     =							image_processor(images=lowerCAmelCase_ ,				return_tensors='''pt'''		).to(lowerCAmelCase_		)

        # forward pass
        with torch.no_grad():
             lowercase_							:Any			     =							model(**lowerCAmelCase_		)

        # verify the logits
        lowercase_							:Union[str, Any]			     =							torch.Size((1, 1000)		)
        self.assertEqual(outputs.logits.shape ,				lowerCAmelCase_		)

        lowercase_							:Optional[Any]			     =							torch.tensor([1.0448, -0.3745, -1.8317]		).to(lowerCAmelCase_		)

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









"""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()
_lowerCAmelCase       =				logging.get_logger(__name__)






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = DPTConfig()

	if "large" in checkpoint_url:
		A_		:    List[Any]						     = 1_024
		A_		:    int						     = 4_096
		A_		:    List[str]						     = 24
		A_		:    List[str]						     = 16
		A_		:    int						     = [5, 11, 17, 23]
		A_		:    str						     = [256, 512, 1_024, 1_024]
		A_		:    str						     = (1, 384, 384)

	if "ade" in checkpoint_url:
		A_		:    Tuple						     = True

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

	return config, expected_shape






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
	for k in ignore_keys:
		state_dict.pop(snake_case__ , snake_case__       )






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

	return name






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	for i in range(config.num_hidden_layers       ):
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		A_		:    Union[str, Any]						     = state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""       )
		A_		:    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
		A_		:    Tuple						     = in_proj_weight[: config.hidden_size, :]
		A_		:    str						     = in_proj_bias[: config.hidden_size]
		A_		:    List[Any]						     = in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		A_		:    List[Any]						     = in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		A_		:    int						     = in_proj_weight[
		    -config.hidden_size :, :
		]
		A_		:    List[Any]						     = in_proj_bias[-config.hidden_size :]






def       __UpperCamelCase						(		):
	A_		:    Optional[Any]						     = """http://images.cocodataset.org/val2017/000000039769.jpg"""
	A_		:    Optional[Any]						     = Image.open(requests.get(snake_case__ , stream=snake_case__       ).raw       )
	return im






@torch.no_grad()
def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	A_     ,  A_		:    int						     = get_dpt_config(snake_case__       )
	# load original state_dict from URL
	A_		:    Dict						     = torch.hub.load_state_dict_from_url(snake_case__ , map_location="""cpu"""       )
	# remove certain keys
	remove_ignore_keys_(snake_case__       )
	# rename keys
	for key in state_dict.copy().keys():
		A_		:    str						     = state_dict.pop(snake_case__       )
		A_		:    str						     = val
	# read in qkv matrices
	read_in_q_k_v(snake_case__ , snake_case__       )

	# load HuggingFace model
	A_		:    Union[str, Any]						     = DPTForSemanticSegmentation(snake_case__       ) if """ade""" in checkpoint_url else DPTForDepthEstimation(snake_case__       )
	model.load_state_dict(snake_case__       )
	model.eval()

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

	A_		:    Dict						     = prepare_img()
	A_		:    List[Any]						     = image_processor(snake_case__ , return_tensors="""pt"""       )

	# forward pass
	A_		:    Tuple						     = model(**snake_case__       ).logits if """ade""" in checkpoint_url else model(**snake_case__       ).predicted_depth

	# Assert logits
	A_		:    List[str]						     = torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]       )
	if "ade" in checkpoint_url:
		A_		:    str						     = torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]       )
	assert outputs.shape == torch.Size(snake_case__       )
	assert (
	    torch.allclose(outputs[0, 0, :3, :3] , snake_case__ , atol=1E-4       )
	    if "ade" in checkpoint_url
	    else torch.allclose(outputs[0, :3, :3] , snake_case__       )
	)

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

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


if __name__ == "__main__":
							_lowerCAmelCase       =				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.",
							)

							_lowerCAmelCase       =				parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

