

'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase					 =	{
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase					 =	['''LayoutLMv2TokenizerFast''']

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase					 =	['''LayoutLMv2FeatureExtractor''']
       UpperCAmelCase					 =	['''LayoutLMv2ImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase					 =	[
           '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LayoutLMv2ForQuestionAnswering''',
           '''LayoutLMv2ForSequenceClassification''',
           '''LayoutLMv2ForTokenClassification''',
           '''LayoutLMv2Layer''',
           '''LayoutLMv2Model''',
           '''LayoutLMv2PreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
       from .processing_layoutlmva import LayoutLMvaProcessor
       from .tokenization_layoutlmva import LayoutLMvaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_layoutlmva import (
                  LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LayoutLMvaForQuestionAnswering,
                  LayoutLMvaForSequenceClassification,
                  LayoutLMvaForTokenClassification,
                  LayoutLMvaLayer,
                  LayoutLMvaModel,
                  LayoutLMvaPreTrainedModel,
              )
else:
       import sys

       UpperCAmelCase					 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





'''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()
UpperCAmelCase					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	Dict			):






     '''simple docstring'''





     __lowercase      		=DPTConfig()

     if "large" in checkpoint_url:
          __lowercase      		=10_24
          __lowercase      		=40_96
          __lowercase      		=24
          __lowercase      		=16
          __lowercase      		=[5, 11, 17, 23]
          __lowercase      		=[2_56, 5_12, 10_24, 10_24]
          __lowercase      		=(1, 3_84, 3_84)

     if "ade" in checkpoint_url:
          __lowercase      		=True

          __lowercase      		=1_50
          __lowercase      		='huggingface/label-files'
          __lowercase      		='ade20k-id2label.json'
          __lowercase      		=json.load(open(cached_download(hf_hub_url(lowercase__,       lowercase__,       repo_type='dataset'			)			),       'r'			)			)
          __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
          __lowercase      		=idalabel
          __lowercase      		={v: k for k, v in idalabel.items()}
          __lowercase      		=[1, 1_50, 4_80, 4_80]

     return config, expected_shape

def 					__UpperCamelCase				(						lowercase__    :	Optional[int]			):






     '''simple docstring'''





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

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

     return name

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

def 					__UpperCamelCase				(						):






     '''simple docstring'''





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

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	Optional[Any],       lowercase__    :	str,       lowercase__    :	List[str],       lowercase__    :	List[str]			):






     '''simple docstring'''





     __lowercase		,  __lowercase      		=get_dpt_config(lowercase__			)
     # load original state_dict from URL
     __lowercase      		=torch.hub.load_state_dict_from_url(lowercase__,       map_location='cpu'			)
     # remove certain keys
     remove_ignore_keys_(lowercase__			)
     # rename keys
     for key in state_dict.copy().keys():
          __lowercase      		=state_dict.pop(lowercase__			)
          __lowercase      		=val
     # read in qkv matrices
     read_in_q_k_v(lowercase__,       lowercase__			)

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

     # Check outputs on an image
     __lowercase      		=4_80 if 'ade' in checkpoint_url else 3_84
     __lowercase      		=DPTImageProcessor(size=lowercase__			)

     __lowercase      		=prepare_img()
     __lowercase      		=image_processor(lowercase__,       return_tensors='pt'			)

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

     # Assert logits
     __lowercase      		=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:
          __lowercase      		=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(lowercase__			)
     assert (
         torch.allclose(outputs[0, 0, :3, :3],       lowercase__,       atol=1E-4			)
         if "ade" in checkpoint_url
         else torch.allclose(outputs[0, :3, :3],       lowercase__			)
     )

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


if __name__ == "__main__":
       UpperCAmelCase					 =	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.''',
       )

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


