


'''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 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]		)					->	int:







     """simple docstring"""
     _A				=							DPTConfig()

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

     if "ade" in checkpoint_url:
          _A				=							True

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

     return config, expected_shape

def 					lowerCamelCase_ ( __UpperCamelCase					:       Tuple		)					->	Dict:







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

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[int]		)					->	List[str]:







     """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'							,    'patch_embeddings'		)
     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:
          _A				=							name.replace('norm1'							,    'layernorm_before'		)
     if "norm2" 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'		)

     return name

def 					lowerCamelCase_ ( __UpperCamelCase					:       Dict							,    __UpperCamelCase					:       int		)					->	str:







     """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 					lowerCamelCase_ ( )					->	List[Any]:







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

@torch.no_grad()
def 					lowerCamelCase_ ( __UpperCamelCase					:       Any							,    __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       Optional[int]							,    __UpperCamelCase					:       Dict		)					->	Optional[int]:







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

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

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

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

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

     # Assert logits
     _A				=							torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]		)
     if "ade" in checkpoint_url:
          _A				=							torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]		)
     assert outputs.shape == torch.Size(__UpperCamelCase		)
     assert (
         torch.allclose(outputs[0, 0, :3, :3]							,    __UpperCamelCase							,    atol=1e-4		)
         if "ade" in checkpoint_url
         else torch.allclose(outputs[0, :3, :3]							,    __UpperCamelCase		)
     )

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

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


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 qiskit

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1		)					->	qiskit.result.counts.Counts:







     """simple docstring"""
     if (
         isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
     ):
          raise TypeError('inputs must be integers.'		)

     if (input_a < 0) or (input_a < 0) or (carry_in < 0):
          raise ValueError('inputs must be positive.'		)

     if (
         (math.floor(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != carry_in)
     ):
          raise ValueError('inputs must be exact integers.'		)

     if (input_a > 2) or (input_a > 2) or (carry_in > 2):
          raise ValueError('inputs must be less or equal to 2.'		)

     # build registers
     _A				=							qiskit.QuantumRegister(4							,    'qr'		)
     _A				=							qiskit.ClassicalRegister(2							,    'cr'		)
     # list the entries
     _A				=							[input_a, input_a, carry_in]

     _A				=							qiskit.QuantumCircuit(__UpperCamelCase							,    __UpperCamelCase		)

     for i in range(0							,    3		):
          if entry[i] == 2:
               quantum_circuit.h(__UpperCamelCase		)  # for hadamard entries
          elif entry[i] == 1:
               quantum_circuit.x(__UpperCamelCase		)  # for 1 entries
          elif entry[i] == 0:
               quantum_circuit.i(__UpperCamelCase		)  # for 0 entries

    # build the circuit
     quantum_circuit.ccx(0							,    1							,    3		)  # ccx = toffoli gate
     quantum_circuit.cx(0							,    1		)
     quantum_circuit.ccx(1							,    2							,    3		)
     quantum_circuit.cx(1							,    2		)
     quantum_circuit.cx(0							,    1		)

     quantum_circuit.measure([2, 3]							,    __UpperCamelCase		)  # measure the last two qbits

     _A				=							qiskit.Aer.get_backend('aer_simulator'		)
     _A				=							qiskit.execute(__UpperCamelCase							,    __UpperCamelCase							,    shots=1_0_0_0		)

     return job.result().get_counts(__UpperCamelCase		)


if __name__ == "__main__":
    print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')





