






from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Optional[int]:
		for param, grad_param in zip(model_a.parameters()     ,      model_b.parameters()):
				if not param.requires_grad:
						continue
				if not did_step:
						# Grads should not be in sync
						assert (
						    torch.allclose(param.grad     ,      grad_param.grad) is False
						), f'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
				else:
						# Grads should be in sync
						assert (
						    torch.allclose(param.grad     ,      grad_param.grad) is True
						), f'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_=True)							->     Dict:
		model.train()
		UpperCamelCase__				:					Any					=					model(lowerCamelCase_)
		UpperCamelCase__				:					Union[str, Any]					=					F.mse_loss(lowerCamelCase_     ,      target.to(output.device))
		if not do_backward:
				loss /= accelerator.gradient_accumulation_steps
				loss.backward()
		else:
				accelerator.backward(lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=False)							->     int:
		set_seed(42)
		UpperCamelCase__				:					Any					=					RegressionModel()
		UpperCamelCase__				:					Optional[int]					=					deepcopy(lowerCamelCase_)
		UpperCamelCase__				:					Optional[int]					=					RegressionDataset(length=80)
		UpperCamelCase__				:					Tuple					=					DataLoader(lowerCamelCase_     ,      batch_size=16)
		model.to(accelerator.device)
		if sched:
				UpperCamelCase__				:					str					=					AdamW(params=model.parameters()     ,      lr=1e-3)
				UpperCamelCase__				:					str					=					AdamW(params=ddp_model.parameters()     ,      lr=1e-3)
				UpperCamelCase__				:					Any					=					LambdaLR(lowerCamelCase_     ,      lr_lambda=lambda lowerCamelCase_: epoch**0.65)
				UpperCamelCase__				:					str					=					LambdaLR(lowerCamelCase_     ,      lr_lambda=lambda lowerCamelCase_: epoch**0.65)
		# Make a copy of `model`
		if sched:
				UpperCamelCase__				:					Optional[int]					=					accelerator.prepare(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
		else:
				UpperCamelCase__				:					Dict					=					accelerator.prepare(lowerCamelCase_     ,      lowerCamelCase_)
		if sched:
				return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
		return model, ddp_model, dataloader







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
		# Test when on a single CPU or GPU that the context manager does nothing
		UpperCamelCase__				:					Optional[Any]					=					get_training_setup(lowerCamelCase_)
		# Use a single batch
		UpperCamelCase__				:					Union[str, Any]					=					next(iter(lowerCamelCase_)).values()
		for iteration in range(3):
				# Gather the distributed inputs and targs for the base model
				UpperCamelCase__				:					int					=					accelerator.gather((ddp_input, ddp_target))
				UpperCamelCase__				:					Optional[Any]					=					input.to(accelerator.device), target.to(accelerator.device)
				# Perform our initial ground truth step in non "DDP"
				step_model(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
				# Do "gradient accumulation" (noop)
				if iteration % 2 == 0:
						# Accumulate grads locally
						with accelerator.no_sync(lowerCamelCase_):
								step_model(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
				else:
						# Sync grads
						step_model(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)

				# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
				check_model_parameters(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
				for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters()):
						if not param.requires_grad:
								continue
						assert torch.allclose(
						    param.grad     ,      ddp_param.grad), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

				# Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration)
				UpperCamelCase__				:					Union[str, Any]					=					ddp_input[torch.randperm(len(lowerCamelCase_))]







def       __UpperCAmelCase						(  lowerCamelCase_)							->     str:
		# Test on distributed setup that context manager behaves properly
		UpperCamelCase__				:					int					=					get_training_setup(lowerCamelCase_)
		# Use a single batch
		UpperCamelCase__				:					str					=					next(iter(lowerCamelCase_)).values()
		for iteration in range(3):
				# Gather the distributed inputs and targs for the base model
				UpperCamelCase__				:					List[str]					=					accelerator.gather((ddp_input, ddp_target))
				UpperCamelCase__				:					Any					=					input.to(accelerator.device), target.to(accelerator.device)
				# Perform our initial ground truth step in non "DDP"
				step_model(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
				# Do "gradient accumulation" (noop)
				if iteration % 2 == 0:
						# Accumulate grads locally
						with accelerator.no_sync(lowerCamelCase_):
								step_model(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
				else:
						# Sync grads
						step_model(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)

				# DDP model and model should only be in sync when not (iteration % 2 == 0)
				for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters()):
						if not param.requires_grad:
								continue
						if iteration % 2 == 0:
								# Grads should not be in sync
								assert (
								    torch.allclose(param.grad     ,      ddp_param.grad) is False
								), f'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
						else:
								# Grads should be in sync
								assert (
								    torch.allclose(param.grad     ,      ddp_param.grad) is True
								), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration)
				UpperCamelCase__				:					Any					=					ddp_input[torch.randperm(len(lowerCamelCase_))]







def       __UpperCAmelCase						(  lowerCamelCase_=False     ,      lowerCamelCase_=False)							->     Dict:
		UpperCamelCase__				:					Optional[Any]					=					Accelerator(
		    split_batches=lowerCamelCase_     ,      dispatch_batches=lowerCamelCase_     ,      gradient_accumulation_steps=2)
		# Test that context manager behaves properly
		UpperCamelCase__				:					Tuple					=					get_training_setup(lowerCamelCase_)
		for iteration, batch in enumerate(lowerCamelCase_):
				UpperCamelCase__				:					Union[str, Any]					=					batch.values()
				# Gather the distributed inputs and targs for the base model
				UpperCamelCase__				:					Optional[Any]					=					accelerator.gather((ddp_input, ddp_target))
				UpperCamelCase__				:					int					=					input.to(accelerator.device), target.to(accelerator.device)
				# Perform our initial ground truth step in non "DDP"
				step_model(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
				# Do "gradient accumulation" (noop)
				with accelerator.accumulate(lowerCamelCase_):
						step_model(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)

				# DDP model and model should only be in sync when not (iteration % 2 == 0)
				for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters()):
						if not param.requires_grad:
								continue
						if ((iteration + 1) % 2 == 0) or (iteration == len(lowerCamelCase_) - 1):
								# Grads should be in sync
								assert (
								    torch.allclose(param.grad     ,      ddp_param.grad) is True
								), f'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
						else:
								# Grads should not be in sync
								assert (
								    torch.allclose(param.grad     ,      ddp_param.grad) is False
								), f'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration)
				UpperCamelCase__				:					int					=					ddp_input[torch.randperm(len(lowerCamelCase_))]
		GradientState._reset_state()







def       __UpperCAmelCase						(  lowerCamelCase_=False     ,      lowerCamelCase_=False)							->     Optional[int]:
		UpperCamelCase__				:					Dict					=					Accelerator(
		    split_batches=lowerCamelCase_     ,      dispatch_batches=lowerCamelCase_     ,      gradient_accumulation_steps=2)
		# Test that context manager behaves properly
		UpperCamelCase__				:					List[str]					=					get_training_setup(lowerCamelCase_     ,      lowerCamelCase_)
		for iteration, batch in enumerate(lowerCamelCase_):
				UpperCamelCase__				:					int					=					batch.values()
				# Gather the distributed inputs and targs for the base model
				UpperCamelCase__				:					List[str]					=					accelerator.gather((ddp_input, ddp_target))
				UpperCamelCase__				:					Optional[int]					=					input.to(accelerator.device), target.to(accelerator.device)
				# Perform our initial ground truth step in non "DDP"
				model.train()
				ddp_model.train()
				step_model(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
				opt.step()

				if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCamelCase_)):
						if split_batches:
								sched.step()
						else:
								for _ in range(accelerator.num_processes):
										sched.step()
				opt.zero_grad()
				# Perform gradient accumulation under wrapper
				with accelerator.accumulate(lowerCamelCase_):
						step_model(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
						ddp_opt.step()
						ddp_sched.step()
						ddp_opt.zero_grad()

				# Learning rates should be the same
				assert (
				    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
				), f'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
				UpperCamelCase__				:					Tuple					=					(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCamelCase_))
				if accelerator.num_processes > 1:
						check_model_parameters(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
				# Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration)
		GradientState._reset_state()







def       __UpperCAmelCase						(  )							->     Optional[Any]:
		UpperCamelCase__				:					List[Any]					=					Accelerator()

		UpperCamelCase__				:					Dict					=					RegressionDataset(length=80)
		UpperCamelCase__				:					List[Any]					=					DataLoader(lowerCamelCase_     ,      batch_size=16)
		UpperCamelCase__				:					int					=					RegressionDataset(length=96)
		UpperCamelCase__				:					str					=					DataLoader(lowerCamelCase_     ,      batch_size=16)
		UpperCamelCase__				:					List[Any]					=					accelerator.prepare(lowerCamelCase_     ,      lowerCamelCase_)
		assert accelerator.gradient_state.active_dataloader is None
		for iteration, _ in enumerate(lowerCamelCase_):
				assert id(accelerator.gradient_state.active_dataloader) == id(lowerCamelCase_)
				if iteration < len(lowerCamelCase_) - 1:
						assert not accelerator.gradient_state.end_of_dataloader
						if iteration == 1:
								for batch_num, _ in enumerate(lowerCamelCase_):
										assert id(accelerator.gradient_state.active_dataloader) == id(lowerCamelCase_)
										if batch_num < len(lowerCamelCase_) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
										else:
												assert accelerator.gradient_state.end_of_dataloader
				else:
						assert accelerator.gradient_state.end_of_dataloader
		assert accelerator.gradient_state.active_dataloader is None







def       __UpperCAmelCase						(  )							->     Optional[int]:
		UpperCamelCase__				:					str					=					Accelerator()
		UpperCamelCase__				:					int					=					accelerator.state
		if state.local_process_index == 0:
				print('**Test `accumulate` gradient accumulation with dataloader break**')
		test_dataloader_break()
		if state.distributed_type == DistributedType.NO:
				if state.local_process_index == 0:
						print('**Test NOOP `no_sync` context manager**')
				test_noop_sync(lowerCamelCase_)
		if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
				if state.local_process_index == 0:
						print('**Test Distributed `no_sync` context manager**')
				test_distributed_sync(lowerCamelCase_)
		if state.distributed_type == DistributedType.MULTI_GPU:
				for split_batch in [True, False]:
						for dispatch_batches in [True, False]:
								if state.local_process_index == 0:
										print(
										    '**Test `accumulate` gradient accumulation, '     ,      f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'     ,      )
								test_gradient_accumulation(lowerCamelCase_     ,      lowerCamelCase_)

    # Currently will break on torch 2.0 +, need to investigate why
		if is_torch_version('<'     ,      '2.0') or state.distributed_type == DistributedType.NO:
				if state.local_process_index == 0:
						print(
						    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      '`split_batches=False`, `dispatch_batches=False`**'     ,      )
				test_gradient_accumulation_with_opt_and_scheduler()
				if state.distributed_type == DistributedType.MULTI_GPU:
						for split_batch in [True, False]:
								for dispatch_batches in [True, False]:
										if not split_batch and not dispatch_batches:
												continue
										if state.local_process_index == 0:
												print(
												    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'     ,      )
										test_gradient_accumulation_with_opt_and_scheduler(lowerCamelCase_     ,      lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Tuple:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
		main()












'''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						(  lowerCamelCase_)							->     Any:
		UpperCamelCase__				:					Dict					=					DPTConfig()

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

		if "ade" in checkpoint_url:
				UpperCamelCase__				:					int					=					True

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

		return config, expected_shape







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[Any]:
		UpperCamelCase__				:					Tuple					=					['pretrained.model.head.weight', 'pretrained.model.head.bias']
		for k in ignore_keys:
				state_dict.pop(lowerCamelCase_     ,      lowerCamelCase_)







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

		return name







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Any:
		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__				:					Optional[int]					=					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight')
				UpperCamelCase__				:					Any					=					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias')
				# next, add query, keys and values (in that order) to the state dict
				UpperCamelCase__				:					List[str]					=					in_proj_weight[: config.hidden_size, :]
				UpperCamelCase__				:					List[Any]					=					in_proj_bias[: config.hidden_size]
				UpperCamelCase__				:					List[Any]					=					in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				UpperCamelCase__				:					List[Any]					=					in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				UpperCamelCase__				:					List[str]					=					in_proj_weight[
				    -config.hidden_size :, :
				]
				UpperCamelCase__				:					int					=					in_proj_bias[-config.hidden_size :]







def       __UpperCAmelCase						(  )							->     Optional[Any]:
		UpperCamelCase__				:					Tuple					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase__				:					List[Any]					=					Image.open(requests.get(lowerCamelCase_     ,      stream=lowerCamelCase_).raw)
		return im







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Dict:
		UpperCamelCase__,	UpperCamelCase__				:					Any					=					get_dpt_config(lowerCamelCase_)
		# load original state_dict from URL
		UpperCamelCase__				:					Tuple					=					torch.hub.load_state_dict_from_url(lowerCamelCase_     ,      map_location='cpu')
		# remove certain keys
		remove_ignore_keys_(lowerCamelCase_)
		# rename keys
		for key in state_dict.copy().keys():
				UpperCamelCase__				:					str					=					state_dict.pop(lowerCamelCase_)
				UpperCamelCase__				:					List[str]					=					val
		# read in qkv matrices
		read_in_q_k_v(lowerCamelCase_     ,      lowerCamelCase_)

		# load HuggingFace model
		UpperCamelCase__				:					str					=					DPTForSemanticSegmentation(lowerCamelCase_) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowerCamelCase_)
		model.load_state_dict(lowerCamelCase_)
		model.eval()

		# Check outputs on an image
		UpperCamelCase__				:					Any					=					480 if 'ade' in checkpoint_url else 384
		UpperCamelCase__				:					List[Any]					=					DPTImageProcessor(size=lowerCamelCase_)

		UpperCamelCase__				:					int					=					prepare_img()
		UpperCamelCase__				:					Optional[Any]					=					image_processor(lowerCamelCase_     ,      return_tensors='pt')

		# forward pass
		UpperCamelCase__				:					Any					=					model(**lowerCamelCase_).logits if 'ade' in checkpoint_url else model(**lowerCamelCase_).predicted_depth

		# Assert logits
		UpperCamelCase__				:					Tuple					=					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:
				UpperCamelCase__				:					List[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(lowerCamelCase_)
		assert (
		    torch.allclose(outputs[0, 0, :3, :3]     ,      lowerCamelCase_     ,      atol=1e-4)
		    if "ade" in checkpoint_url
		    else torch.allclose(outputs[0, :3, :3]     ,      lowerCamelCase_)
		)

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

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


if __name__ == "__main__":
		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)




