



'''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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  str											= DPTConfig()

    if "large" in checkpoint_url:
        lowerCAmelCase__			:  Tuple											= 1024
        lowerCAmelCase__			:  Union[str, Any]											= 4096
        lowerCAmelCase__			:  List[Any]											= 24
        lowerCAmelCase__			:  Optional[int]											= 16
        lowerCAmelCase__			:  List[str]											= [5, 11, 17, 23]
        lowerCAmelCase__			:  Union[str, Any]											= [256, 512, 1024, 1024]
        lowerCAmelCase__			:  Optional[Any]											= (1, 384, 384)

    if "ade" in checkpoint_url:
        lowerCAmelCase__			:  List[Any]											= True

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

    return config, expected_shape
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= ['pretrained.model.head.weight', 'pretrained.model.head.bias']
    for k in ignore_keys:
        state_dict.pop(_A							,   _A      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



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

    return name
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



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



    """simple docstring"""





    lowerCAmelCase__			:  Optional[int]											= 'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCAmelCase__			:  Tuple											= Image.open(requests.get(_A							,   stream=_A      ).raw      )
    return im
@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[Any]											= get_dpt_config(_A      )
    # load original state_dict from URL
    lowerCAmelCase__			:  Tuple											= torch.hub.load_state_dict_from_url(_A							,   map_location="""cpu"""      )
    # remove certain keys
    remove_ignore_keys_(_A      )
    # rename keys
    for key in state_dict.copy().keys():
        lowerCAmelCase__			:  Any											= state_dict.pop(_A      )
        lowerCAmelCase__			:  Any											= val
    # read in qkv matrices
    read_in_q_k_v(_A							,   _A      )

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

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

    lowerCAmelCase__			:  List[Any]											= prepare_img()
    lowerCAmelCase__			:  Dict											= image_processor(_A							,   return_tensors="""pt"""      )

    # forward pass
    lowerCAmelCase__			:  Union[str, Any]											= model(**_A      ).logits if 'ade' in checkpoint_url else model(**_A      ).predicted_depth

    # Assert logits
    lowerCAmelCase__			:  Optional[Any]											= 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:
        lowerCAmelCase__			:  Union[str, Any]											= 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(_A      )
    assert (
        torch.allclose(outputs[0, 0, :3, :3]							,   _A							,   atol=1e-4      )
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3]							,   _A      )
    )

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

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


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)







'''simple docstring'''





import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase = True							,   UpperCamelCase = math.inf							,   UpperCamelCase = -math.inf							,   UpperCamelCase = math.inf							,   UpperCamelCase = -math.inf							,   UpperCamelCase = False							,   UpperCamelCase = 100							,   UpperCamelCase = 0.01							,   UpperCamelCase = 1							,   ):



    """simple docstring"""





    lowerCAmelCase__			:  int											= False
    lowerCAmelCase__			:  Optional[Any]											= search_prob
    lowerCAmelCase__			:  Tuple											= start_temperate
    lowerCAmelCase__			:  Optional[Any]											= []
    lowerCAmelCase__			:  str											= 0
    lowerCAmelCase__			:  Tuple											= None

    while not search_end:
        lowerCAmelCase__			:  List[str]											= current_state.score()
        if best_state is None or current_score > best_state.score():
            lowerCAmelCase__			:  List[str]											= current_state
        scores.append(UpperCamelCase      )
        iterations += 1
        lowerCAmelCase__			:  Dict											= None
        lowerCAmelCase__			:  List[Any]											= current_state.get_neighbors()
        while (
            next_state is None and neighbors
        ):  # till we do not find a neighbor that we can move to
            lowerCAmelCase__			:  Tuple											= random.randint(0							,   len(UpperCamelCase      ) - 1      )  # picking a random neighbor
            lowerCAmelCase__			:  str											= neighbors.pop(UpperCamelCase      )
            lowerCAmelCase__			:  Optional[Any]											= picked_neighbor.score() - current_score

            if (
                picked_neighbor.x > max_x
                or picked_neighbor.x < min_x
                or picked_neighbor.y > max_y
                or picked_neighbor.y < min_y
            ):
                continue  # neighbor outside our bounds

            if not find_max:
                lowerCAmelCase__			:  Union[str, Any]											= change * -1  # in case we are finding minimum
            if change > 0:  # improves the solution
                lowerCAmelCase__			:  Tuple											= picked_neighbor
            else:
                lowerCAmelCase__			:  List[Any]											= (math.e) ** (
                    change / current_temp
                )  # probability generation function
                if random.random() < probability:  # random number within probability
                    lowerCAmelCase__			:  List[Any]											= picked_neighbor
        lowerCAmelCase__			:  Optional[int]											= current_temp - (current_temp * rate_of_decrease)

        if current_temp < threshold_temp or next_state is None:
            # temperature below threshold, or could not find a suitable neighbor
            lowerCAmelCase__			:  str											= True
        else:
            lowerCAmelCase__			:  Optional[int]											= next_state

    if visualization:
        from matplotlib import pyplot as plt

        plt.plot(range(UpperCamelCase      )							,   UpperCamelCase      )
        plt.xlabel("""Iterations"""      )
        plt.ylabel("""Function values"""      )
        plt.show()
    return best_state


if __name__ == "__main__":
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



     """simple docstring"""





     return (x**2) + (y**2)

 # starting the problem with initial coordinates (12, 47)
 _lowerCAmelCase										=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(
     prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
     F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )

 # starting the problem with initial coordinates (12, 47)
 _lowerCAmelCase										=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(
     prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     '''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
     F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



     """simple docstring"""





     return (3 * x**2) - (6 * y)

 _lowerCAmelCase										=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(prob, find_max=False, visualization=True)
 print(
     '''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
     F"""{local_min.score()}"""
 )

 _lowerCAmelCase										=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(prob, find_max=True, visualization=True)




 print(
     '''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
     F"""{local_min.score()}"""
 )


