

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()
A__						=							logging.get_logger(__name__)




def 					_lowercase     (				a_				)				->	Any:




					'''simple docstring'''







					__magic_name__							=    DPTConfig()

					if "large" in checkpoint_url:
										__magic_name__							=    1_0_2_4
										__magic_name__							=    4_0_9_6
										__magic_name__							=    2_4
										__magic_name__							=    1_6
										__magic_name__							=    [5, 1_1, 1_7, 2_3]
										__magic_name__							=    [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
										__magic_name__							=    (1, 3_8_4, 3_8_4)

					if "ade" in checkpoint_url:
										__magic_name__							=    True

										__magic_name__							=    1_5_0
										__magic_name__							=    'huggingface/label-files'
										__magic_name__							=    'ade20k-id2label.json'
										__magic_name__							=    json.load(open(cached_download(hf_hub_url(lowerCamelCase_					,lowerCamelCase_					,repo_type='dataset'				)				)					,'r'				)				)
										__magic_name__							=    {int(lowerCamelCase_				): v for k, v in idalabel.items()}
										__magic_name__							=    idalabel
										__magic_name__							=    {v: k for k, v in idalabel.items()}
										__magic_name__							=    [1, 1_5_0, 4_8_0, 4_8_0]

					return config, expected_shape




def 					_lowercase     (				a_				)				->	Optional[Any]:




					'''simple docstring'''







					__magic_name__							=    ['pretrained.model.head.weight', 'pretrained.model.head.bias']
					for k in ignore_keys:
										state_dict.pop(lowerCamelCase_					,lowerCamelCase_				)




def 					_lowercase     (				a_				)				->	Optional[Any]:




					'''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
					):
										__magic_name__							=    name.replace('pretrained.model'					,'dpt.encoder'				)
					if "pretrained.model" in name:
										__magic_name__							=    name.replace('pretrained.model'					,'dpt.embeddings'				)
					if "patch_embed" in name:
										__magic_name__							=    name.replace('patch_embed'					,'patch_embeddings'				)
					if "pos_embed" in name:
										__magic_name__							=    name.replace('pos_embed'					,'position_embeddings'				)
					if "attn.proj" in name:
										__magic_name__							=    name.replace('attn.proj'					,'attention.output.dense'				)
					if "proj" in name and "project" not in name:
										__magic_name__							=    name.replace('proj'					,'projection'				)
					if "blocks" in name:
										__magic_name__							=    name.replace('blocks'					,'layer'				)
					if "mlp.fc1" in name:
										__magic_name__							=    name.replace('mlp.fc1'					,'intermediate.dense'				)
					if "mlp.fc2" in name:
										__magic_name__							=    name.replace('mlp.fc2'					,'output.dense'				)
					if "norm1" in name:
										__magic_name__							=    name.replace('norm1'					,'layernorm_before'				)
					if "norm2" in name:
										__magic_name__							=    name.replace('norm2'					,'layernorm_after'				)
					if "scratch.output_conv" in name:
										__magic_name__							=    name.replace('scratch.output_conv'					,'head'				)
					if "scratch" in name:
										__magic_name__							=    name.replace('scratch'					,'neck'				)
					if "layer1_rn" in name:
										__magic_name__							=    name.replace('layer1_rn'					,'convs.0'				)
					if "layer2_rn" in name:
										__magic_name__							=    name.replace('layer2_rn'					,'convs.1'				)
					if "layer3_rn" in name:
										__magic_name__							=    name.replace('layer3_rn'					,'convs.2'				)
					if "layer4_rn" in name:
										__magic_name__							=    name.replace('layer4_rn'					,'convs.3'				)
					if "refinenet" in name:
										__magic_name__							=    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
										__magic_name__							=    name.replace(F'refinenet{layer_idx}'					,F'fusion_stage.layers.{abs(layer_idx-4				)}'				)
					if "out_conv" in name:
										__magic_name__							=    name.replace('out_conv'					,'projection'				)
					if "resConfUnit1" in name:
										__magic_name__							=    name.replace('resConfUnit1'					,'residual_layer1'				)
					if "resConfUnit2" in name:
										__magic_name__							=    name.replace('resConfUnit2'					,'residual_layer2'				)
					if "conv1" in name:
										__magic_name__							=    name.replace('conv1'					,'convolution1'				)
					if "conv2" in name:
										__magic_name__							=    name.replace('conv2'					,'convolution2'				)
					# readout blocks
					if "pretrained.act_postprocess1.0.project.0" in name:
										__magic_name__							=    name.replace('pretrained.act_postprocess1.0.project.0'					,'neck.reassemble_stage.readout_projects.0.0'				)
					if "pretrained.act_postprocess2.0.project.0" in name:
										__magic_name__							=    name.replace('pretrained.act_postprocess2.0.project.0'					,'neck.reassemble_stage.readout_projects.1.0'				)
					if "pretrained.act_postprocess3.0.project.0" in name:
										__magic_name__							=    name.replace('pretrained.act_postprocess3.0.project.0'					,'neck.reassemble_stage.readout_projects.2.0'				)
					if "pretrained.act_postprocess4.0.project.0" in name:
										__magic_name__							=    name.replace('pretrained.act_postprocess4.0.project.0'					,'neck.reassemble_stage.readout_projects.3.0'				)
					# resize blocks
					if "pretrained.act_postprocess1.3" in name:
										__magic_name__							=    name.replace('pretrained.act_postprocess1.3'					,'neck.reassemble_stage.layers.0.projection'				)
					if "pretrained.act_postprocess1.4" in name:
										__magic_name__							=    name.replace('pretrained.act_postprocess1.4'					,'neck.reassemble_stage.layers.0.resize'				)
					if "pretrained.act_postprocess2.3" in name:
										__magic_name__							=    name.replace('pretrained.act_postprocess2.3'					,'neck.reassemble_stage.layers.1.projection'				)
					if "pretrained.act_postprocess2.4" in name:
										__magic_name__							=    name.replace('pretrained.act_postprocess2.4'					,'neck.reassemble_stage.layers.1.resize'				)
					if "pretrained.act_postprocess3.3" in name:
										__magic_name__							=    name.replace('pretrained.act_postprocess3.3'					,'neck.reassemble_stage.layers.2.projection'				)
					if "pretrained.act_postprocess4.3" in name:
										__magic_name__							=    name.replace('pretrained.act_postprocess4.3'					,'neck.reassemble_stage.layers.3.projection'				)
					if "pretrained.act_postprocess4.4" in name:
										__magic_name__							=    name.replace('pretrained.act_postprocess4.4'					,'neck.reassemble_stage.layers.3.resize'				)
					if "pretrained" in name:
										__magic_name__							=    name.replace('pretrained'					,'dpt'				)
					if "bn" in name:
										__magic_name__							=    name.replace('bn'					,'batch_norm'				)
					if "head" in name:
										__magic_name__							=    name.replace('head'					,'head.head'				)
					if "encoder.norm" in name:
										__magic_name__							=    name.replace('encoder.norm'					,'layernorm'				)
					if "auxlayer" in name:
										__magic_name__							=    name.replace('auxlayer'					,'auxiliary_head.head'				)

					return name




def 					_lowercase     (				a_					,a_				)				->	Any:




					'''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)
										__magic_name__							=    state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'				)
										__magic_name__							=    state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias'				)
										# next, add query, keys and values (in that order) to the state dict
										__magic_name__							=    in_proj_weight[: config.hidden_size, :]
										__magic_name__							=    in_proj_bias[: config.hidden_size]
										__magic_name__							=    in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__magic_name__							=    in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										__magic_name__							=    in_proj_weight[
										    -config.hidden_size :, :
										]
										__magic_name__							=    in_proj_bias[-config.hidden_size :]




def 					_lowercase     (				)				->	Optional[Any]:




					'''simple docstring'''







					__magic_name__							=    'http://images.cocodataset.org/val2017/000000039769.jpg'
					__magic_name__							=    Image.open(requests.get(lowerCamelCase_					,stream=lowerCamelCase_				).raw				)
					return im




@torch.no_grad()
def 					_lowercase     (				a_					,a_					,a_					,a_				)				->	Dict:




					'''simple docstring'''







					__magic_name__							=    get_dpt_config(lowerCamelCase_				)
					# load original state_dict from URL
					__magic_name__							=    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():
										__magic_name__							=    state_dict.pop(lowerCamelCase_				)
										__magic_name__							=    val
					# read in qkv matrices
					read_in_q_k_v(lowerCamelCase_					,lowerCamelCase_				)

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

					# Check outputs on an image
					__magic_name__							=    4_8_0 if 'ade' in checkpoint_url else 3_8_4
					__magic_name__							=    DPTImageProcessor(size=lowerCamelCase_				)

					__magic_name__							=    prepare_img()
					__magic_name__							=    image_processor(lowerCamelCase_					,return_tensors='pt'				)

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

					# Assert logits
					__magic_name__							=    torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]				)
					if "ade" in checkpoint_url:
										__magic_name__							=    torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]				)
					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__":
						A__						=							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.",
						)

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







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  , return_dict=__UpperCamelCase      ).to(__UpperCamelCase      )
						__magic_name__							=    AutoTokenizer.from_pretrained('google/mt5-small'      )

						__magic_name__							=    tokenizer('Hello there'  , return_tensors='pt'      ).input_ids
						__magic_name__							=    tokenizer('Hi I am'  , return_tensors='pt'      ).input_ids

						__magic_name__							=    model(input_ids.to(__UpperCamelCase      )  , labels=labels.to(__UpperCamelCase      )      ).loss
						__magic_name__							=    -(labels.shape[-1] * loss.item())

						__magic_name__							=    -84.9127
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1E-4      )




