


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class   UpperCamelCase       (		_SCREAMING_SNAKE_CASE  ):


   '''simple docstring'''







   lowercase       :						Optional[int]          =(UniPCMultistepScheduler,)
   lowercase       :						Optional[Any]          =(("""num_inference_steps""", 25),)
   def 		UpperCamelCase	(  self ,				**UpperCamelCase_		):
        lowercase_							:str			     =							{
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """solver_order""": 2,
            """solver_type""": """bh2""",
        }

        config.update(**lowerCAmelCase_		)
        return config
   def 		UpperCamelCase	(  self ,				UpperCamelCase_=0 ,				**UpperCamelCase_		):
        lowercase_							:Tuple			     =							dict(self.forward_default_kwargs		)
        lowercase_							:Any			     =							kwargs.pop('''num_inference_steps''' ,				lowerCAmelCase_		)
        lowercase_							:str			     =							self.dummy_sample
        lowercase_							:Dict			     =							0.1 * sample
        lowercase_							:int			     =							[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
             lowercase_							:List[Any]			     =							self.get_scheduler_config(**lowerCAmelCase_		)
             lowercase_							:Dict			     =							scheduler_class(**lowerCAmelCase_		)
             scheduler.set_timesteps(lowerCAmelCase_		)
             # copy over dummy past residuals
             lowercase_							:Tuple			     =							dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(lowerCAmelCase_		)
                  lowercase_							:Any			     =							scheduler_class.from_pretrained(lowerCAmelCase_		)
                  new_scheduler.set_timesteps(lowerCAmelCase_		)
                  # copy over dummy past residuals
                  lowercase_							:Any			     =							dummy_past_residuals[: new_scheduler.config.solver_order]

             lowercase_							:Optional[int]			     =							sample, sample
             for t in range(lowerCAmelCase_ ,				time_step + scheduler.config.solver_order + 1		):
                  lowercase_							:Any			     =							scheduler.step(lowerCAmelCase_ ,				lowerCAmelCase_ ,				lowerCAmelCase_ ,				**lowerCAmelCase_		).prev_sample
                  lowercase_							:Dict			     =							new_scheduler.step(lowerCAmelCase_ ,				lowerCAmelCase_ ,				lowerCAmelCase_ ,				**lowerCAmelCase_		).prev_sample

                  assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"
   def 		UpperCamelCase	(  self ,				UpperCamelCase_=0 ,				**UpperCamelCase_		):
        lowercase_							:Optional[int]			     =							dict(self.forward_default_kwargs		)
        lowercase_							:int			     =							kwargs.pop('''num_inference_steps''' ,				lowerCAmelCase_		)
        lowercase_							:Optional[Any]			     =							self.dummy_sample
        lowercase_							:List[Any]			     =							0.1 * sample
        lowercase_							:List[Any]			     =							[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
             lowercase_							:Dict			     =							self.get_scheduler_config()
             lowercase_							:str			     =							scheduler_class(**lowerCAmelCase_		)
             scheduler.set_timesteps(lowerCAmelCase_		)

             # copy over dummy past residuals (must be after setting timesteps)
             lowercase_							:Union[str, Any]			     =							dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(lowerCAmelCase_		)
                  lowercase_							:Tuple			     =							scheduler_class.from_pretrained(lowerCAmelCase_		)
                  # copy over dummy past residuals
                  new_scheduler.set_timesteps(lowerCAmelCase_		)

                  # copy over dummy past residual (must be after setting timesteps)
                  lowercase_							:Optional[int]			     =							dummy_past_residuals[: new_scheduler.config.solver_order]

             lowercase_							:Tuple			     =							scheduler.step(lowerCAmelCase_ ,				lowerCAmelCase_ ,				lowerCAmelCase_ ,				**lowerCAmelCase_		).prev_sample
             lowercase_							:str			     =							new_scheduler.step(lowerCAmelCase_ ,				lowerCAmelCase_ ,				lowerCAmelCase_ ,				**lowerCAmelCase_		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"
   def 		UpperCamelCase	(  self ,				UpperCamelCase_=None ,				**UpperCamelCase_		):
        if scheduler is None:
             lowercase_							:int			     =							self.scheduler_classes[0]
             lowercase_							:List[str]			     =							self.get_scheduler_config(**lowerCAmelCase_		)
             lowercase_							:Any			     =							scheduler_class(**lowerCAmelCase_		)

        lowercase_							:int			     =							self.scheduler_classes[0]
        lowercase_							:str			     =							self.get_scheduler_config(**lowerCAmelCase_		)
        lowercase_							:Union[str, Any]			     =							scheduler_class(**lowerCAmelCase_		)

        lowercase_							:Optional[int]			     =							10
        lowercase_							:Optional[Any]			     =							self.dummy_model()
        lowercase_							:Optional[Any]			     =							self.dummy_sample_deter
        scheduler.set_timesteps(lowerCAmelCase_		)

        for i, t in enumerate(scheduler.timesteps		):
             lowercase_							:Optional[int]			     =							model(lowerCAmelCase_ ,				lowerCAmelCase_		)
             lowercase_							:Optional[int]			     =							scheduler.step(lowerCAmelCase_ ,				lowerCAmelCase_ ,				lowerCAmelCase_		).prev_sample

        return sample
   def 		UpperCamelCase	(  self		):
        lowercase_							:Any			     =							dict(self.forward_default_kwargs		)

        lowercase_							:Any			     =							kwargs.pop('''num_inference_steps''' ,				lowerCAmelCase_		)

        for scheduler_class in self.scheduler_classes:
             lowercase_							:Dict			     =							self.get_scheduler_config()
             lowercase_							:List[Any]			     =							scheduler_class(**lowerCAmelCase_		)

             lowercase_							:int			     =							self.dummy_sample
             lowercase_							:str			     =							0.1 * sample

             if num_inference_steps is not None and hasattr(lowerCAmelCase_ ,				'''set_timesteps'''		):
                  scheduler.set_timesteps(lowerCAmelCase_		)
             elif num_inference_steps is not None and not hasattr(lowerCAmelCase_ ,				'''set_timesteps'''		):
                  lowercase_							:Tuple			     =							num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             lowercase_							:List[Any]			     =							[residual + 0.2, residual + 0.15, residual + 0.10]
             lowercase_							:Dict			     =							dummy_past_residuals[: scheduler.config.solver_order]

             lowercase_							:str			     =							scheduler.timesteps[5]
             lowercase_							:Any			     =							scheduler.timesteps[6]

             lowercase_							:Tuple			     =							scheduler.step(lowerCAmelCase_ ,				lowerCAmelCase_ ,				lowerCAmelCase_ ,				**lowerCAmelCase_		).prev_sample
             lowercase_							:Dict			     =							scheduler.step(lowerCAmelCase_ ,				lowerCAmelCase_ ,				lowerCAmelCase_ ,				**lowerCAmelCase_		).prev_sample

             self.assertEqual(output_a.shape ,				sample.shape		)
             self.assertEqual(output_a.shape ,				output_a.shape		)
   def 		UpperCamelCase	(  self		):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        lowercase_							:Tuple			     =							UniPCMultistepScheduler(**self.get_scheduler_config()		)
        lowercase_							:List[str]			     =							self.full_loop(scheduler=lowerCAmelCase_		)
        lowercase_							:int			     =							torch.mean(torch.abs(lowerCAmelCase_		)		)

        assert abs(result_mean.item() - 0.2464		) < 1E-3

        lowercase_							:List[str]			     =							DPMSolverSinglestepScheduler.from_config(scheduler.config		)
        lowercase_							:List[str]			     =							DEISMultistepScheduler.from_config(scheduler.config		)
        lowercase_							:Optional[int]			     =							DPMSolverMultistepScheduler.from_config(scheduler.config		)
        lowercase_							:str			     =							UniPCMultistepScheduler.from_config(scheduler.config		)

        lowercase_							:Dict			     =							self.full_loop(scheduler=lowerCAmelCase_		)
        lowercase_							:List[Any]			     =							torch.mean(torch.abs(lowerCAmelCase_		)		)

        assert abs(result_mean.item() - 0.2464		) < 1E-3
   def 		UpperCamelCase	(  self		):
        for timesteps in [25, 50, 100, 999, 1000]:
             self.check_over_configs(num_train_timesteps=lowerCAmelCase_		)
   def 		UpperCamelCase	(  self		):
        self.check_over_configs(thresholding=lowerCAmelCase_		)
        for order in [1, 2, 3]:
             for solver_type in ["bh1", "bh2"]:
                  for threshold in [0.5, 1.0, 2.0]:
                       for prediction_type in ["epsilon", "sample"]:
                            self.check_over_configs(
                                thresholding=lowerCAmelCase_ ,				prediction_type=lowerCAmelCase_ ,				sample_max_value=lowerCAmelCase_ ,				solver_order=lowerCAmelCase_ ,				solver_type=lowerCAmelCase_ ,				)
   def 		UpperCamelCase	(  self		):
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=lowerCAmelCase_		)
   def 		UpperCamelCase	(  self		):
        for solver_type in ["bh1", "bh2"]:
             for order in [1, 2, 3]:
                  for prediction_type in ["epsilon", "sample"]:
                       self.check_over_configs(
                           solver_order=lowerCAmelCase_ ,				solver_type=lowerCAmelCase_ ,				prediction_type=lowerCAmelCase_ ,				)
                       lowercase_							:Union[str, Any]			     =							self.full_loop(
                           solver_order=lowerCAmelCase_ ,				solver_type=lowerCAmelCase_ ,				prediction_type=lowerCAmelCase_ ,				)
                       assert not torch.isnan(lowerCAmelCase_		).any(), "Samples have nan numbers"
   def 		UpperCamelCase	(  self		):
        self.check_over_configs(lower_order_final=lowerCAmelCase_		)
        self.check_over_configs(lower_order_final=lowerCAmelCase_		)
   def 		UpperCamelCase	(  self		):
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
             self.check_over_forward(num_inference_steps=lowerCAmelCase_ ,				time_step=0		)
   def 		UpperCamelCase	(  self		):
        lowercase_							:Optional[int]			     =							self.full_loop()
        lowercase_							:Optional[int]			     =							torch.mean(torch.abs(lowerCAmelCase_		)		)

        assert abs(result_mean.item() - 0.2464		) < 1E-3
   def 		UpperCamelCase	(  self		):
        lowercase_							:str			     =							self.full_loop(prediction_type='''v_prediction'''		)
        lowercase_							:Optional[Any]			     =							torch.mean(torch.abs(lowerCAmelCase_		)		)

        assert abs(result_mean.item() - 0.1014		) < 1E-3
   def 		UpperCamelCase	(  self		):
        lowercase_							:List[str]			     =							self.scheduler_classes[0]
        lowercase_							:str			     =							self.get_scheduler_config(thresholding=lowerCAmelCase_ ,				dynamic_thresholding_ratio=0		)
        lowercase_							:Union[str, Any]			     =							scheduler_class(**lowerCAmelCase_		)

        lowercase_							:Any			     =							10
        lowercase_							:int			     =							self.dummy_model()
        lowercase_							:Optional[Any]			     =							self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowerCAmelCase_		)

        for i, t in enumerate(scheduler.timesteps		):
             lowercase_							:Dict			     =							model(lowerCAmelCase_ ,				lowerCAmelCase_		)
             lowercase_							:Tuple			     =							scheduler.step(lowerCAmelCase_ ,				lowerCAmelCase_ ,				lowerCAmelCase_		).prev_sample

        assert sample.dtype == torch.floataa




   def 		UpperCamelCase	(  self ,				**UpperCamelCase_		):
        for scheduler_class in self.scheduler_classes:
             lowercase_							:Tuple			     =							self.get_scheduler_config(**lowerCAmelCase_		)
             lowercase_							:int			     =							scheduler_class(**lowerCAmelCase_		)

             scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
             assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps









"""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)

