

import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
				import torch
				from torch import nn

				from transformers import (
				    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
				    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    MODEL_MAPPING,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				)
				from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import DeiTImageProcessor

class     __UpperCamelCase     :




	def __init__(		self						:    Dict		,						lowerCAmelCase						:    Tuple		,						lowerCAmelCase						:    Any=13		,						lowerCAmelCase						:    Union[str, Any]=30		,						lowerCAmelCase						:    Optional[Any]=2		,						lowerCAmelCase						:    List[Any]=3		,						lowerCAmelCase						:    Any=True		,						lowerCAmelCase						:    Optional[Any]=True		,						lowerCAmelCase						:    Union[str, Any]=32		,						lowerCAmelCase						:    Tuple=5		,						lowerCAmelCase						:    Dict=4		,						lowerCAmelCase						:    List[str]=37		,						lowerCAmelCase						:    Optional[Any]="gelu"		,						lowerCAmelCase						:    List[Any]=0.1		,						lowerCAmelCase						:    Dict=0.1		,						lowerCAmelCase						:    Optional[int]=10		,						lowerCAmelCase						:    str=0.02		,						lowerCAmelCase						:    List[str]=3		,						lowerCAmelCase						:    List[Any]=None		,						lowerCAmelCase						:    Optional[Any]=2		,						):


		'''simple docstring'''







		UpperCAmelCase_												=      parent
		UpperCAmelCase_												=      batch_size
		UpperCAmelCase_												=      image_size
		UpperCAmelCase_												=      patch_size
		UpperCAmelCase_												=      num_channels
		UpperCAmelCase_												=      is_training
		UpperCAmelCase_												=      use_labels
		UpperCAmelCase_												=      hidden_size
		UpperCAmelCase_												=      num_hidden_layers
		UpperCAmelCase_												=      num_attention_heads
		UpperCAmelCase_												=      intermediate_size
		UpperCAmelCase_												=      hidden_act
		UpperCAmelCase_												=      hidden_dropout_prob
		UpperCAmelCase_												=      attention_probs_dropout_prob
		UpperCAmelCase_												=      type_sequence_label_size
		UpperCAmelCase_												=      initializer_range
		UpperCAmelCase_												=      scope
		UpperCAmelCase_												=      encoder_stride

		# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
		UpperCAmelCase_												=      (image_size // patch_size) ** 2
		UpperCAmelCase_												=      num_patches + 2




	def    __A       (		self						:    List[Any]				):


		'''simple docstring'''







		UpperCAmelCase_												=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

		UpperCAmelCase_												=      None
		if self.use_labels:
			UpperCAmelCase_												=      ids_tensor([self.batch_size]		,						self.type_sequence_label_size				)

		UpperCAmelCase_												=      self.get_config()

		return config, pixel_values, labels




	def    __A       (		self						:    Tuple				):


		'''simple docstring'''







		return DeiTConfig(
		    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		,						encoder_stride=self.encoder_stride		,						)




	def    __A       (		self						:    str		,						lowerCAmelCase						:    Dict		,						lowerCAmelCase						:    List[Any]		,						lowerCAmelCase						:    Tuple				):


		'''simple docstring'''







		UpperCAmelCase_												=      DeiTModel(config=__a				)
		model.to(__a				)
		model.eval()
		UpperCAmelCase_												=      model(__a				)
		self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)				)




	def    __A       (		self						:    List[str]		,						lowerCAmelCase						:    str		,						lowerCAmelCase						:    List[str]		,						lowerCAmelCase						:    Optional[int]				):


		'''simple docstring'''







		UpperCAmelCase_												=      DeiTForMaskedImageModeling(config=__a				)
		model.to(__a				)
		model.eval()
		UpperCAmelCase_												=      model(__a				)
		self.parent.assertEqual(
		    result.reconstruction.shape		,						(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

		# test greyscale images
		UpperCAmelCase_												=      1
		UpperCAmelCase_												=      DeiTForMaskedImageModeling(__a				)
		model.to(__a				)
		model.eval()

		UpperCAmelCase_												=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
		UpperCAmelCase_												=      model(__a				)
		self.parent.assertEqual(result.reconstruction.shape		,						(self.batch_size, 1, self.image_size, self.image_size)				)




	def    __A       (		self						:    Optional[Any]		,						lowerCAmelCase						:    str		,						lowerCAmelCase						:    Any		,						lowerCAmelCase						:    Optional[Any]				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.type_sequence_label_size
		UpperCAmelCase_												=      DeiTForImageClassification(__a				)
		model.to(__a				)
		model.eval()
		UpperCAmelCase_												=      model(__a		,						labels=__a				)
		self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.type_sequence_label_size)				)

		# test greyscale images
		UpperCAmelCase_												=      1
		UpperCAmelCase_												=      DeiTForImageClassification(__a				)
		model.to(__a				)
		model.eval()

		UpperCAmelCase_												=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
		UpperCAmelCase_												=      model(__a		,						labels=__a				)
		self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.type_sequence_label_size)				)




	def    __A       (		self						:    int				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.prepare_config_and_inputs()
		(
		    UpperCAmelCase_	
		)												=      config_and_inputs
		UpperCAmelCase_												=      {"pixel_values": pixel_values}
		return config, inputs_dict


@require_torch
class     __UpperCamelCase     (       _UpperCamelCase  ,  _UpperCamelCase  ,  unittest.TestCase    ):
	SCREAMING_SNAKE_CASE__     					=  (
	    (
	        DeiTModel,
	        DeiTForImageClassification,
	        DeiTForImageClassificationWithTeacher,
	        DeiTForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE__     					=  (
	    {
	        "feature-extraction": DeiTModel,
	        "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	SCREAMING_SNAKE_CASE__     					=  False
	SCREAMING_SNAKE_CASE__     					=  False
	SCREAMING_SNAKE_CASE__     					=  False




	def    __A       (		self						:    Any				):


		'''simple docstring'''







		UpperCAmelCase_												=      DeiTModelTester(self				)
		UpperCAmelCase_												=      ConfigTester(self		,						config_class=__a		,						has_text_modality=__a		,						hidden_size=37				)




	def    __A       (		self						:    str				):


		'''simple docstring'''







		self.config_tester.run_common_tests()




	@unittest.skip(reason="DeiT does not use inputs_embeds"				)
	def    __A       (		self						:    str				):


		'''simple docstring'''







		pass




	def    __A       (		self						:    List[Any]				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.model_tester.prepare_config_and_inputs_for_common()

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




	def    __A       (		self						:    Tuple				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.model_tester.prepare_config_and_inputs_for_common()

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

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




	def    __A       (		self						:    Any				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__a				)




	def    __A       (		self						:    Dict				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*__a				)




	def    __A       (		self						:    Tuple				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*__a				)




	def    __A       (		self						:    Optional[Any]		,						lowerCAmelCase						:    Tuple		,						lowerCAmelCase						:    Union[str, Any]		,						lowerCAmelCase						:    List[Any]=False				):


		'''simple docstring'''







		UpperCAmelCase_												=      super()._prepare_for_class(__a		,						__a		,						return_labels=__a				)

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

		return inputs_dict




	def    __A       (		self						:    Any				):


		'''simple docstring'''







		if not self.model_tester.is_training:
			return

		UpperCAmelCase_												=      self.model_tester.prepare_config_and_inputs_for_common()
		UpperCAmelCase_												=      True

		for model_class in self.all_model_classes:
			# DeiTForImageClassificationWithTeacher supports inference-only
			if (
			    model_class in get_values(__a				)
			    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
			):
				continue
			UpperCAmelCase_												=      model_class(__a				)
			model.to(__a				)
			model.train()
			UpperCAmelCase_												=      self._prepare_for_class(__a		,						__a		,						return_labels=__a				)
			UpperCAmelCase_												=      model(**__a				).loss
			loss.backward()




	def    __A       (		self						:    Union[str, Any]				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.model_tester.prepare_config_and_inputs_for_common()
		if not self.model_tester.is_training:
			return

		UpperCAmelCase_												=      False
		UpperCAmelCase_												=      True

		for model_class in self.all_model_classes:
			if model_class in get_values(__a				) or not model_class.supports_gradient_checkpointing:
				continue
			# DeiTForImageClassificationWithTeacher supports inference-only
			if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
				continue
			UpperCAmelCase_												=      model_class(__a				)
			model.gradient_checkpointing_enable()
			model.to(__a				)
			model.train()
			UpperCAmelCase_												=      self._prepare_for_class(__a		,						__a		,						return_labels=__a				)
			UpperCAmelCase_												=      model(**__a				).loss
			loss.backward()




	def    __A       (		self						:    List[str]				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.model_tester.prepare_config_and_inputs_for_common()

		UpperCAmelCase_												=      [
		    {"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(__a				),
			        *get_values(__a				),
			    ]
			    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
			):
				continue

			for problem_type in problem_types:
				with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"				):
					UpperCAmelCase_												=      problem_type["title"]
					UpperCAmelCase_												=      problem_type["num_labels"]

					UpperCAmelCase_												=      model_class(__a				)
					model.to(__a				)
					model.train()

					UpperCAmelCase_												=      self._prepare_for_class(__a		,						__a		,						return_labels=__a				)

					if problem_type["num_labels"] > 1:
						UpperCAmelCase_												=      inputs["labels"].unsqueeze(1				).repeat(1		,						problem_type["num_labels"]				)

					UpperCAmelCase_												=      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=__a				) as warning_list:
						UpperCAmelCase_												=      model(**__a				).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    __A       (		self						:    List[str]				):


		'''simple docstring'''







		for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			UpperCAmelCase_												=      DeiTModel.from_pretrained(__a				)
			self.assertIsNotNone(__a				)



def    __lowerCAmelCase   (    ):
	UpperCAmelCase_												=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
	return image




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




	@cached_property
	def    __A       (		self						:    Union[str, Any]				):


		'''simple docstring'''







		return (
		    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"				)
		    if is_vision_available()
		    else None
		)




	@slow
	def    __A       (		self						:    Union[str, Any]				):


		'''simple docstring'''







		UpperCAmelCase_												=      DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"				).to(
		    __a				)

		UpperCAmelCase_												=      self.default_image_processor
		UpperCAmelCase_												=      prepare_img()
		UpperCAmelCase_												=      image_processor(images=__a		,						return_tensors="pt"				).to(__a				)

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

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

		UpperCAmelCase_												=      torch.tensor([-1.0_266, 0.1_912, -1.2_861]				).to(__a				)

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




	@slow
	@require_accelerate
	@require_torch_gpu
	def    __A       (		self						:    Optional[int]				):


		'''simple docstring'''







		UpperCAmelCase_												=      DeiTModel.from_pretrained(
		    "facebook/deit-base-distilled-patch16-224"		,						torch_dtype=torch.floataa		,						device_map="auto"				)
		UpperCAmelCase_												=      self.default_image_processor

		UpperCAmelCase_												=      prepare_img()
		UpperCAmelCase_												=      image_processor(images=__a		,						return_tensors="pt"				)
		UpperCAmelCase_												=      inputs.pixel_values.to(__a				)

		# forward pass to make sure inference works in fp16
		with torch.no_grad():
			UpperCAmelCase_												=      model(__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
  )
