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				(    lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






       _A=	DPTConfig(embedding_type='hybrid'   )

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

       if "nyu" or "midas" in checkpoint_url:
              _A=	7_68
              _A=	[1, 1, 1, 0.5]
              _A=	[2_56, 5_12, 7_68, 7_68]
              _A=	1_50
              _A=	16
              _A=	(1, 3_84, 3_84)
              _A=	False
              _A=	'project'

       if "ade" in checkpoint_url:
              _A=	True
              _A=	7_68
              _A=	[1, 1, 1, 0.5]
              _A=	1_50
              _A=	16
              _A=	'huggingface/label-files'
              _A=	'ade20k-id2label.json'
              _A=	json.load(open(cached_download(hf_hub_url(lowerCAmelCase_       ,					lowerCAmelCase_       ,					repo_type='dataset'   )   )       ,					'r'   )   )
              _A=	{int(lowerCAmelCase_   ): v for k, v in idalabel.items()}
              _A=	idalabel
              _A=	{v: k for k, v in idalabel.items()}
              _A=	[1, 1_50, 4_80, 4_80]

       return config, expected_shape

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						List[str]:




       '''simple docstring'''






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

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

       if ".." in name:
              _A=	name.replace('..'       ,					'.'   )

       if "stem.conv" in name:
              _A=	name.replace('stem.conv'       ,					'bit.embedder.convolution'   )
       if "blocks" in name:
              _A=	name.replace('blocks'       ,					'layers'   )
       if "convolution" in name and "backbone" in name:
              _A=	name.replace('convolution'       ,					'conv'   )
       if "layer" in name and "backbone" in name:
              _A=	name.replace('layer'       ,					'layers'   )
       if "backbone.bit.encoder.bit" in name:
              _A=	name.replace('backbone.bit.encoder.bit'       ,					'backbone.bit'   )
       if "embedder.conv" in name:
              _A=	name.replace('embedder.conv'       ,					'embedder.convolution'   )
       if "backbone.bit.encoder.stem.norm" in name:
              _A=	name.replace('backbone.bit.encoder.stem.norm'       ,					'backbone.bit.embedder.norm'   )
       return name

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						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)
              _A=	state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"   )
              _A=	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=	in_proj_weight[: config.hidden_size, :]
              _A=	in_proj_bias[: config.hidden_size]
              _A=	in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              _A=	in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              _A=	in_proj_weight[
                  -config.hidden_size :, :
              ]
              _A=	in_proj_bias[-config.hidden_size :]

def 							UpperCamelCase				(    )		->						List[Any]:




       '''simple docstring'''






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

@torch.no_grad()
def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Tuple:




       '''simple docstring'''






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

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

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

       _A=	prepare_img()
       _A=	image_processor(lowerCAmelCase_       ,					return_tensors='pt'   )

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

       if show_prediction:
              _A=	(
                  torch.nn.functional.interpolate(
                      outputs.unsqueeze(1   )       ,					size=(image.size[1], image.size[0])       ,					mode='bicubic'       ,					align_corners=lowerCAmelCase_       ,					)
                  .squeeze()
                  .cpu()
                  .numpy()
              )

              Image.fromarray((prediction / prediction.max()) * 2_55   ).show()

       if pytorch_dump_folder_path is not None:
              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:
              model.push_to_hub('ybelkada/dpt-hybrid-midas'   )
              image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'   )


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=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''',
      )

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





def 							UpperCamelCase				(    )		->						list[list[int]]:




       '''simple docstring'''






       return [list(range(10_00 - i       ,					-10_00 - i       ,					-1   )   ) for i in range(10_00   )]


UpperCAmelCase_								=							generate_large_matrix()
UpperCAmelCase_								=							(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						None:




       '''simple docstring'''






       assert all(row == sorted(lowerCAmelCase_       ,					reverse=lowerCAmelCase_   ) for row in grid   )
       assert all(list(lowerCAmelCase_   ) == sorted(lowerCAmelCase_       ,					reverse=lowerCAmelCase_   ) for col in zip(*lowerCAmelCase_   )   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	0
       _A=	len(lowerCAmelCase_   ) - 1

       # Edge cases such as no values or all numbers are negative.
       if not array or array[0] < 0:
              return 0

       while right + 1 > left:
              _A=	(left + right) // 2
              _A=	array[mid]

              # Num must be negative and the index must be greater than or equal to 0.
              if num < 0 and array[mid - 1] >= 0:
                     return mid

              if num >= 0:
                     _A=	mid + 1
              else:
                     _A=	mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
       return len(lowerCAmelCase_   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	0
       _A=	len(grid[0]   )

       for i in range(len(lowerCAmelCase_   )   ):
              _A=	find_negative_index(grid[i][:bound]   )
              total += bound
       return (len(lowerCAmelCase_   ) * len(grid[0]   )) - total

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       return len([number for row in grid for number in row if number < 0]   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	0
       for row in grid:
              for i, number in enumerate(lowerCAmelCase_   ):
                     if number < 0:
                            total += len(lowerCAmelCase_   ) - i
                            break
       return total

def 							UpperCamelCase				(    )		->						None:




       '''simple docstring'''






       from timeit import timeit

       print('Running benchmarks'   )
       _A=	(
           'from __main__ import count_negatives_binary_search, '
           'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
       )
       for func in (
           "count_negatives_binary_search",  # took 0.7727 seconds
           "count_negatives_brute_force_with_break",  # took 4.6505 seconds
           "count_negatives_brute_force",  # took 12.8160 seconds
       ):
              _A=	timeit(F"{func}(grid=grid)"       ,					setup=lowerCAmelCase_       ,					number=5_00   )
              print(F"{func}() took {time:0.4f} seconds"   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()