





"""simple docstring"""


import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
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, _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,
      BeitForImageClassification,
      BeitForMaskedImageModeling,
      BeitForSemanticSegmentation,
      BeitModel,
  )
  from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  import PIL
  from PIL import Image

  from transformers import BeitImageProcessor



class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							List[Any]       , __a :							str       , __a :							Optional[int]=100       , __a :							Tuple=13       , __a :							int=30       , __a :							int=2       , __a :							List[Any]=3       , __a :							Optional[Any]=True       , __a :							List[Any]=True       , __a :							int=32       , __a :							str=4       , __a :							Optional[int]=4       , __a :							Tuple=37       , __a :							str="gelu"       , __a :							Union[str, Any]=0.1       , __a :							Tuple=0.1       , __a :							Dict=10       , __a :							Any=0.02       , __a :							List[str]=3       , __a :							List[str]=None       , __a :							Optional[int]=[0, 1, 2, 3]       , )		->					List[str]:
        _UpperCamelCase   :						Union[str, Any]    =							parent
        _UpperCamelCase   :						Optional[int]    =							100
        _UpperCamelCase   :						Tuple    =							batch_size
        _UpperCamelCase   :						Optional[Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							patch_size
        _UpperCamelCase   :						Any    =							num_channels
        _UpperCamelCase   :						List[str]    =							is_training
        _UpperCamelCase   :						Dict    =							use_labels
        _UpperCamelCase   :						Union[str, Any]    =							hidden_size
        _UpperCamelCase   :						int    =							num_hidden_layers
        _UpperCamelCase   :						Any    =							num_attention_heads
        _UpperCamelCase   :						int    =							intermediate_size
        _UpperCamelCase   :						List[Any]    =							hidden_act
        _UpperCamelCase   :						Optional[int]    =							hidden_dropout_prob
        _UpperCamelCase   :						Optional[Any]    =							attention_probs_dropout_prob
        _UpperCamelCase   :						int    =							type_sequence_label_size
        _UpperCamelCase   :						Union[str, Any]    =							initializer_range
        _UpperCamelCase   :						List[str]    =							scope
        _UpperCamelCase   :						Dict    =							out_indices
        _UpperCamelCase   :						Union[str, Any]    =							num_labels

        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        _UpperCamelCase   :						Tuple    =							(image_size // patch_size) ** 2
        _UpperCamelCase   :						str    =							num_patches + 1

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Any:
        _UpperCamelCase   :						Dict    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        _UpperCamelCase   :						Any    =							None
        _UpperCamelCase   :						List[str]    =							None
        if self.use_labels:
              _UpperCamelCase   :						Dict    =							ids_tensor([self.batch_size]       , self.type_sequence_label_size						)
              _UpperCamelCase   :						Optional[Any]    =							ids_tensor([self.batch_size, self.image_size, self.image_size]       , self.num_labels						)

        _UpperCamelCase   :						int    =							self.get_config()

        return config, pixel_values, labels, pixel_labels

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]						)		->					Optional[int]:
        return BeitConfig(
            vocab_size=self.vocab_size       , 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       , out_indices=self.out_indices       , )

  def  __SCREAMING_SNAKE_CASE					(		self :							str       , __a :							str       , __a :							Union[str, Any]       , __a :							List[Any]       , __a :							Dict						)		->					int:
        _UpperCamelCase   :						Any    =							BeitModel(config=__a						)
        model.to(__a						)
        model.eval()
        _UpperCamelCase   :						Tuple    =							model(__a						)
        self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Any       , __a :							Tuple       , __a :							List[str]       , __a :							Optional[int]       , __a :							Any						)		->					Tuple:
        _UpperCamelCase   :						Union[str, Any]    =							BeitForMaskedImageModeling(config=__a						)
        model.to(__a						)
        model.eval()
        _UpperCamelCase   :						Tuple    =							model(__a						)
        self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length - 1, self.vocab_size)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str       , __a :							Optional[Any]       , __a :							str       , __a :							List[Any]       , __a :							Optional[int]						)		->					str:
        _UpperCamelCase   :						List[Any]    =							self.type_sequence_label_size
        _UpperCamelCase   :						Any    =							BeitForImageClassification(__a						)
        model.to(__a						)
        model.eval()
        _UpperCamelCase   :						List[str]    =							model(__a       , labels=__a						)
        self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.type_sequence_label_size)						)

        # test greyscale images
        _UpperCamelCase   :						Dict    =							1
        _UpperCamelCase   :						str    =							BeitForImageClassification(__a						)
        model.to(__a						)
        model.eval()

        _UpperCamelCase   :						int    =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
        _UpperCamelCase   :						List[Any]    =							model(__a       , labels=__a						)
        self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.type_sequence_label_size)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , __a :							Union[str, Any]       , __a :							Optional[Any]       , __a :							List[str]       , __a :							Dict						)		->					Optional[int]:
        _UpperCamelCase   :						List[str]    =							self.num_labels
        _UpperCamelCase   :						Optional[int]    =							BeitForSemanticSegmentation(__a						)
        model.to(__a						)
        model.eval()
        _UpperCamelCase   :						Union[str, Any]    =							model(__a						)
        self.parent.assertEqual(
            result.logits.shape       , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
        _UpperCamelCase   :						Dict    =							model(__a       , labels=__a						)
        self.parent.assertEqual(
            result.logits.shape       , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					str:
        _UpperCamelCase   :						Dict    =							self.prepare_config_and_inputs()
        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Optional[Any]    =							config_and_inputs
        _UpperCamelCase   :						Any    =							{"pixel_values": pixel_values}
        return config, inputs_dict





@require_torch
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	_UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[Any]						  =       (
      (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__				:List[str]						  =       (
      {
          "feature-extraction": BeitModel,
          "image-classification": BeitForImageClassification,
          "image-segmentation": BeitForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE__				:Tuple						  =       False
  SCREAMING_SNAKE_CASE__				:str						  =       False
  SCREAMING_SNAKE_CASE__				:Union[str, Any]						  =       False

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]						)		->					Tuple:
        _UpperCamelCase   :						Optional[Any]    =							BeitModelTester(self						)
        _UpperCamelCase   :						Union[str, Any]    =							ConfigTester(self       , config_class=__a       , has_text_modality=__a       , hidden_size=37						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					str:
        self.config_tester.run_common_tests()

  @unittest.skip(reason="BEiT does not use inputs_embeds"						)
  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Tuple:
        pass

  @require_torch_multi_gpu
  @unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"						)
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					List[str]:
        pass

  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					List[str]:
        _UpperCamelCase,	_UpperCamelCase   :						Optional[int]    =							self.model_tester.prepare_config_and_inputs_for_common()

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

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					Dict:
        _UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							self.model_tester.prepare_config_and_inputs_for_common()

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

              _UpperCamelCase   :						Dict    =							["pixel_values"]
              self.assertListEqual(arg_names[:1]       , __a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					Optional[Any]:
        _UpperCamelCase   :						List[str]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Optional[int]:
        _UpperCamelCase   :						Dict    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					int:
        _UpperCamelCase   :						Any    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					List[Any]:
        _UpperCamelCase   :						int    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]						)		->					str:
        if not self.model_tester.is_training:
              return

        _UpperCamelCase,	_UpperCamelCase   :						Optional[int]    =							self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCamelCase   :						List[Any]    =							True

        for model_class in self.all_model_classes:
              # we don't test BeitForMaskedImageModeling
              if model_class in [*get_values(__a						), BeitForMaskedImageModeling]:
                    continue

              _UpperCamelCase   :						str    =							model_class(__a						)
              model.to(__a						)
              model.train()
              _UpperCamelCase   :						Optional[Any]    =							self._prepare_for_class(__a       , __a       , return_labels=__a						)
              _UpperCamelCase   :						List[str]    =							model(**__a						).loss
              loss.backward()

  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					Dict:
        _UpperCamelCase,	_UpperCamelCase   :						List[Any]    =							self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
              return

        _UpperCamelCase   :						Optional[Any]    =							False
        _UpperCamelCase   :						Any    =							True

        for model_class in self.all_model_classes:
              # we don't test BeitForMaskedImageModeling
              if (
                  model_class in [*get_values(__a						), BeitForMaskedImageModeling]
                  or not model_class.supports_gradient_checkpointing
              ):
                    continue

              _UpperCamelCase   :						Optional[Any]    =							model_class(__a						)
              model.gradient_checkpointing_enable()
              model.to(__a						)
              model.train()
              _UpperCamelCase   :						List[Any]    =							self._prepare_for_class(__a       , __a       , return_labels=__a						)
              _UpperCamelCase   :						List[str]    =							model(**__a						).loss
              loss.backward()

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Optional[int]:
        _UpperCamelCase,	_UpperCamelCase   :						int    =							self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCamelCase   :						Any    =							_config_zero_init(__a						)
        for model_class in self.all_model_classes:
              _UpperCamelCase   :						List[str]    =							model_class(config=__a						)
              for name, param in model.named_parameters():
                    # we skip lambda parameters as these require special initial values
                    # determined by config.layer_scale_init_value
                    if "lambda" in name:
                          continue
                    if param.requires_grad:
                          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'''       , )


  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					str:
        for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _UpperCamelCase   :						Optional[Any]    =							BeitModel.from_pretrained(__a						)
              self.assertIsNotNone(__a						)





def        lowercase__     (       )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
      return image



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







  '''simple docstring'''


  @cached_property
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					List[Any]:
        return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"						) if is_vision_available() else None

  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					Optional[int]:
        _UpperCamelCase   :						int    =							BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"						).to(__a						)

        _UpperCamelCase   :						str    =							self.default_image_processor
        _UpperCamelCase   :						List[str]    =							prepare_img()
        _UpperCamelCase   :						int    =							image_processor(images=__a       , return_tensors="pt"						).pixel_values.to(__a						)

        # prepare bool_masked_pos
        _UpperCamelCase   :						Tuple    =							torch.ones((1, 196)       , dtype=torch.bool						).to(__a						)

        # forward pass
        with torch.no_grad():
              _UpperCamelCase   :						Optional[Any]    =							model(pixel_values=__a       , bool_masked_pos=__a						)
        _UpperCamelCase   :						Optional[int]    =							outputs.logits

        # verify the logits
        _UpperCamelCase   :						Dict    =							torch.Size((1, 196, 8192)						)
        self.assertEqual(logits.shape       , __a						)

        _UpperCamelCase   :						Optional[int]    =							torch.tensor(
            [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]						).to(__a						)

        self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]       , __a       , atol=1e-2						)						)

  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					str:
        _UpperCamelCase   :						str    =							BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"						).to(__a						)

        _UpperCamelCase   :						str    =							self.default_image_processor
        _UpperCamelCase   :						Optional[int]    =							prepare_img()
        _UpperCamelCase   :						List[Any]    =							image_processor(images=__a       , return_tensors="pt"						).to(__a						)

        # forward pass
        with torch.no_grad():
              _UpperCamelCase   :						int    =							model(**__a						)
        _UpperCamelCase   :						int    =							outputs.logits

        # verify the logits
        _UpperCamelCase   :						Tuple    =							torch.Size((1, 1000)						)
        self.assertEqual(logits.shape       , __a						)

        _UpperCamelCase   :						Dict    =							torch.tensor([-1.23_85, -1.09_87, -1.01_08]						).to(__a						)

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

        _UpperCamelCase   :						List[Any]    =							281
        self.assertEqual(logits.argmax(-1						).item()       , __a						)

  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					Tuple:
        _UpperCamelCase   :						Any    =							BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"						).to(
            __a						)

        _UpperCamelCase   :						List[Any]    =							self.default_image_processor
        _UpperCamelCase   :						Union[str, Any]    =							prepare_img()
        _UpperCamelCase   :						Union[str, Any]    =							image_processor(images=__a       , return_tensors="pt"						).to(__a						)

        # forward pass
        with torch.no_grad():
              _UpperCamelCase   :						Any    =							model(**__a						)
        _UpperCamelCase   :						str    =							outputs.logits

        # verify the logits
        _UpperCamelCase   :						List[Any]    =							torch.Size((1, 2_1841)						)
        self.assertEqual(logits.shape       , __a						)

        _UpperCamelCase   :						int    =							torch.tensor([1.68_81, -0.27_87, 0.59_01]						).to(__a						)

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

        _UpperCamelCase   :						str    =							2396
        self.assertEqual(logits.argmax(-1						).item()       , __a						)

  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					List[Any]:
        _UpperCamelCase   :						List[str]    =							BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"						)
        _UpperCamelCase   :						str    =							model.to(__a						)

        _UpperCamelCase   :						Any    =							BeitImageProcessor(do_resize=__a       , size=640       , do_center_crop=__a						)

        _UpperCamelCase   :						Tuple    =							load_dataset("hf-internal-testing/fixtures_ade20k"       , split="test"						)
        _UpperCamelCase   :						Dict    =							Image.open(ds[0]["file"]						)
        _UpperCamelCase   :						Optional[Any]    =							image_processor(images=__a       , return_tensors="pt"						).to(__a						)

        # forward pass
        with torch.no_grad():
              _UpperCamelCase   :						Optional[Any]    =							model(**__a						)
        _UpperCamelCase   :						List[Any]    =							outputs.logits

        # verify the logits
        _UpperCamelCase   :						Any    =							torch.Size((1, 150, 160, 160)						)
        self.assertEqual(logits.shape       , __a						)

        _UpperCamelCase   :						int    =							version.parse(PIL.__version__						) < version.parse("9.0.0"						)

        if is_pillow_less_than_a:
              _UpperCamelCase   :						Tuple    =							torch.tensor(
                  [
                      [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
                      [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
                      [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
                  ]       , device=__a       , )
        else:
              _UpperCamelCase   :						List[Any]    =							torch.tensor(
                  [
                      [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
                      [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
                      [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
                  ]       , device=__a       , )

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


  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Union[str, Any]:
        _UpperCamelCase   :						int    =							BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"						)
        _UpperCamelCase   :						List[str]    =							model.to(__a						)

        _UpperCamelCase   :						Union[str, Any]    =							BeitImageProcessor(do_resize=__a       , size=640       , do_center_crop=__a						)

        _UpperCamelCase   :						Union[str, Any]    =							load_dataset("hf-internal-testing/fixtures_ade20k"       , split="test"						)
        _UpperCamelCase   :						Any    =							Image.open(ds[0]["file"]						)
        _UpperCamelCase   :						Optional[Any]    =							image_processor(images=__a       , return_tensors="pt"						).to(__a						)

        # forward pass
        with torch.no_grad():
              _UpperCamelCase   :						Tuple    =							model(**__a						)

        _UpperCamelCase   :						Optional[Any]    =							outputs.logits.detach().cpu()

        _UpperCamelCase   :						str    =							image_processor.post_process_semantic_segmentation(outputs=__a       , target_sizes=[(500, 300)]						)
        _UpperCamelCase   :						str    =							torch.Size((500, 300)						)
        self.assertEqual(segmentation[0].shape       , __a						)

        _UpperCamelCase   :						List[str]    =							image_processor.post_process_semantic_segmentation(outputs=__a						)
        _UpperCamelCase   :						Any    =							torch.Size((160, 160)						)
        self.assertEqual(segmentation[0].shape       , __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()
lowerCamelCase__						= logging.get_logger(__name__)




def        lowercase__     (       lowercase_      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							DPTConfig(embedding_type="hybrid"      )

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

      if "nyu" or "midas" in checkpoint_url:
            _UpperCamelCase   :						str    =							768
            _UpperCamelCase   :						Any    =							[1, 1, 1, 0.5]
            _UpperCamelCase   :						List[Any]    =							[256, 512, 768, 768]
            _UpperCamelCase   :						Tuple    =							150
            _UpperCamelCase   :						int    =							16
            _UpperCamelCase   :						Any    =							(1, 384, 384)
            _UpperCamelCase   :						Any    =							False
            _UpperCamelCase   :						Any    =							"project"

      if "ade" in checkpoint_url:
            _UpperCamelCase   :						Any    =							True
            _UpperCamelCase   :						Optional[Any]    =							768
            _UpperCamelCase   :						Optional[int]    =							[1, 1, 1, 0.5]
            _UpperCamelCase   :						Dict    =							150
            _UpperCamelCase   :						Any    =							16
            _UpperCamelCase   :						Any    =							"huggingface/label-files"
            _UpperCamelCase   :						List[str]    =							"ade20k-id2label.json"
            _UpperCamelCase   :						Any    =							json.load(open(cached_download(hf_hub_url(lowercase_		,lowercase_		,repo_type="dataset"      )      )		,"r"      )      )
            _UpperCamelCase   :						Any    =							{int(lowercase_      ): v for k, v in idalabel.items()}
            _UpperCamelCase   :						int    =							idalabel
            _UpperCamelCase   :						int    =							{v: k for k, v in idalabel.items()}
            _UpperCamelCase   :						Any    =							[1, 150, 480, 480]

      return config, expected_shape




def        lowercase__     (       lowercase_      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							["pretrained.model.head.weight", "pretrained.model.head.bias"]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		int:




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

      if ".." in name:
            _UpperCamelCase   :						List[str]    =							name.replace(".."		,"."      )

      if "stem.conv" in name:
            _UpperCamelCase   :						Optional[int]    =							name.replace("stem.conv"		,"bit.embedder.convolution"      )
      if "blocks" in name:
            _UpperCamelCase   :						Optional[int]    =							name.replace("blocks"		,"layers"      )
      if "convolution" in name and "backbone" in name:
            _UpperCamelCase   :						Any    =							name.replace("convolution"		,"conv"      )
      if "layer" in name and "backbone" in name:
            _UpperCamelCase   :						Optional[Any]    =							name.replace("layer"		,"layers"      )
      if "backbone.bit.encoder.bit" in name:
            _UpperCamelCase   :						Dict    =							name.replace("backbone.bit.encoder.bit"		,"backbone.bit"      )
      if "embedder.conv" in name:
            _UpperCamelCase   :						Union[str, Any]    =							name.replace("embedder.conv"		,"embedder.convolution"      )
      if "backbone.bit.encoder.stem.norm" in name:
            _UpperCamelCase   :						Dict    =							name.replace("backbone.bit.encoder.stem.norm"		,"backbone.bit.embedder.norm"      )
      return name




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Dict:




      """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   :						Tuple    =							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   :						Tuple    =							in_proj_weight[: config.hidden_size, :]
            _UpperCamelCase   :						Dict    =							in_proj_bias[: config.hidden_size]
            _UpperCamelCase   :						Optional[int]    =							in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _UpperCamelCase   :						Union[str, Any]    =							in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _UpperCamelCase   :						Dict    =							in_proj_weight[
                -config.hidden_size :, :
            ]
            _UpperCamelCase   :						List[Any]    =							in_proj_bias[-config.hidden_size :]




def        lowercase__     (       )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							"http://images.cocodataset.org/val2017/000000039769.jpg"
      _UpperCamelCase   :						Dict    =							Image.open(requests.get(lowercase_		,stream=lowercase_      ).raw      )
      return im




@torch.no_grad()
def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						str    =							get_dpt_config(lowercase_      )
      # load original state_dict from URL
      # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
      _UpperCamelCase   :						Optional[int]    =							torch.load(lowercase_		,map_location="cpu"      )
      # remove certain keys
      remove_ignore_keys_(lowercase_      )
      # rename keys
      for key in state_dict.copy().keys():
            _UpperCamelCase   :						Union[str, Any]    =							state_dict.pop(lowercase_      )
            _UpperCamelCase   :						Tuple    =							val
      # read in qkv matrices
      read_in_q_k_v(lowercase_		,lowercase_      )

      # load HuggingFace model
      _UpperCamelCase   :						Dict    =							DPTForSemanticSegmentation(lowercase_      ) if "ade" in checkpoint_url else DPTForDepthEstimation(lowercase_      )
      model.load_state_dict(lowercase_      )
      model.eval()

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

      _UpperCamelCase   :						int    =							prepare_img()
      _UpperCamelCase   :						List[str]    =							image_processor(lowercase_		,return_tensors="pt"      )

      # forward pass
      _UpperCamelCase   :						Any    =							model(**lowercase_      ).logits if "ade" in checkpoint_url else model(**lowercase_      ).predicted_depth

      if show_prediction:
            _UpperCamelCase   :						Optional[int]    =							(
                torch.nn.functional.interpolate(
                    outputs.unsqueeze(1      )		,size=(image.size[1], image.size[0])		,mode="bicubic"		,align_corners=lowercase_		,)
                .squeeze()
                .cpu()
                .numpy()
            )

            Image.fromarray((prediction / prediction.max()) * 255      ).show()

      if pytorch_dump_folder_path is not None:
            Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
            print(F'''Saving model to {pytorch_dump_folder_path}'''      )
            model.save_pretrained(lowercase_      )
            print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
            image_processor.save_pretrained(lowercase_      )

      if push_to_hub:
            model.push_to_hub("ybelkada/dpt-hybrid-midas"      )
            image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"      )


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=False,
      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.",
  )
  parser.add_argument(
      "--show_prediction",
      action="store_true",
  )

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