"""simple docstring"""


import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_:       Tuple			     =       logging.get_logger(__name__)






@dataclass
class  a__     :
   snake_case_				=   42
   snake_case_				=   field(default_factory=_a						)
   snake_case_				=   field(default_factory=_a						)


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				len(list(m.modules()	)	) == 1 or isinstance(_UpperCAmelCase,					nn.Convad	) or isinstance(_UpperCAmelCase,					nn.BatchNormad	)
        if has_not_submodules:
             if not isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
                  self.traced.append(_UpperCAmelCase	)


   def __call__(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook	)	)
        self.module(_UpperCAmelCase	)
        [x.remove() for x in self.handles]
        return self


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return list(filter(lambda _UpperCAmelCase	: len(list(x.state_dict().keys()	)	) > 0,					self.traced	)	)






@dataclass
class  a__     :
   snake_case_				=   42
   snake_case_				=   42
   snake_case_				=   0
   snake_case_				=   field(default_factory=_a						)
   snake_case_				=   field(default_factory=_a						)


   def __call__(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				Tracker(self.dest	)(_UpperCAmelCase	).parametrized
        lowercase__								=				Tracker(self.src	)(_UpperCAmelCase	).parametrized

        lowercase__								=				list(filter(lambda _UpperCAmelCase	: type(_UpperCAmelCase	) not in self.src_skip,					_UpperCAmelCase	)	)
        lowercase__								=				list(filter(lambda _UpperCAmelCase	: type(_UpperCAmelCase	) not in self.dest_skip,					_UpperCAmelCase	)	)

        if len(_UpperCAmelCase	) != len(_UpperCAmelCase	):
             raise Exception(
                 F'''Numbers of operations are different. Source module has {len(_UpperCAmelCase	)} operations while'''
                 F''' destination module has {len(_UpperCAmelCase	)}.'''	)

        for dest_m, src_m in zip(_UpperCAmelCase,					_UpperCAmelCase	):
             dest_m.load_state_dict(src_m.state_dict()	)
             if self.verbose == 1:
                  print(F'''Transfered from={src_m} to={dest_m}'''	)







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				from_model.state_dict()
     lowercase__								=				our_model.state_dict()
     lowercase__								=				our_model.config
     lowercase__								=				[]
     for stage_idx in range(len(config.hidden_sizes   )   ):
          for block_id in range(config.depths[stage_idx]   ):
               lowercase__								=				f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
               lowercase__								=				f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

               all_keys.append((from_key, to_key)   )
               lowercase__								=				f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
               lowercase__								=				f'''van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'''

               all_keys.append((from_key, to_key)   )

     for from_key, to_key in all_keys:
          lowercase__								=				from_state_dict.pop(A   )

     our_model.load_state_dict(A   )
     return our_model







def        __a (		A     ,					A     ,					A     ,					A     ,					A     ,					A = True     ,					):






     '''simple docstring'''



     print(f'''Downloading weights for {name}...'''   )
     lowercase__								=				cached_download(A   )
     print(f'''Converting {name}...'''   )
     lowercase__								=				torch.load(A   )["state_dict"]
     from_model.load_state_dict(A   )
     from_model.eval()
     with torch.no_grad():
          lowercase__								=				VanForImageClassification(A   ).eval()
          lowercase__								=				ModuleTransfer(src=A     ,					dest=A   )
          lowercase__								=				torch.randn((1, 3, 2_24, 2_24)   )
          module_transfer(A   )
          lowercase__								=				copy_parameters(A     ,					A   )

     if not torch.allclose(from_model(A   )     ,					our_model(A   ).logits   ):
          raise ValueError("The model logits don't match the original one."   )

     lowercase__								=				name
     print(A   )

     if push_to_hub:
          our_model.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name     ,					commit_message="Add model"     ,					use_temp_dir=A     ,					)

          # we can use the convnext one
          lowercase__								=				AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"   )
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name     ,					commit_message="Add image processor"     ,					use_temp_dir=A     ,					)

          print(f'''Pushed {checkpoint_name}'''   )







def        __a (		A     ,					A = None     ,					A = True   ):






     '''simple docstring'''



     lowercase__								=				"imagenet-1k-id2label.json"
     lowercase__								=				10_00

     lowercase__								=				"huggingface/label-files"
     lowercase__								=				num_labels
     lowercase__								=				json.load(open(hf_hub_download(A     ,					A     ,					repo_type="dataset"   )     ,					"r"   )   )
     lowercase__								=				{int(A   ): v for k, v in idalabel.items()}

     lowercase__								=				idalabel
     lowercase__								=				{v: k for k, v in idalabel.items()}

     lowercase__								=				partial(A     ,					num_labels=A     ,					idalabel=A     ,					labelaid=A   )

     lowercase__								=				{
         "van-tiny": ImageNetPreTrainedConfig(
             hidden_sizes=[32, 64, 1_60, 2_56]     ,					depths=[3, 3, 5, 2]     ,					mlp_ratios=[8, 8, 4, 4]     ,					),
         "van-small": ImageNetPreTrainedConfig(
             hidden_sizes=[64, 1_28, 3_20, 5_12]     ,					depths=[2, 2, 4, 2]     ,					mlp_ratios=[8, 8, 4, 4]     ,					),
         "van-base": ImageNetPreTrainedConfig(
             hidden_sizes=[64, 1_28, 3_20, 5_12]     ,					depths=[3, 3, 12, 3]     ,					mlp_ratios=[8, 8, 4, 4]     ,					),
         "van-large": ImageNetPreTrainedConfig(
             hidden_sizes=[64, 1_28, 3_20, 5_12]     ,					depths=[3, 5, 27, 3]     ,					mlp_ratios=[8, 8, 4, 4]     ,					),
     }

     lowercase__								=				{
         "van-tiny": van_tiny,
         "van-small": van_small,
         "van-base": van_base,
         "van-large": van_large,
     }

     lowercase__								=				{
         "van-tiny": (
             "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
         ),
         "van-small": (
             "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
         ),
         "van-base": (
             "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
         ),
         "van-large": (
             "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
         ),
     }

     if model_name:
          convert_weight_and_push(
              A     ,					names_to_config[model_name]     ,					checkpoint=names_to_original_checkpoints[model_name]     ,					from_model=names_to_original_models[model_name]()     ,					save_directory=A     ,					push_to_hub=A     ,					)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(
                   A     ,					A     ,					checkpoint=names_to_original_checkpoints[model_name]     ,					from_model=names_to_original_models[model_name]()     ,					save_directory=A     ,					push_to_hub=A     ,					)


if __name__ == "__main__":
 lowerCAmelCase_:       Any			     =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model-name",
     default=None,
     type=str,
     help=(
         "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
         " currently: van-tiny/small/base/large. If `None`, all of them will the converted."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default=None,
     type=Path,
     required=True,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument(
     "--van_dir",
     required=True,
     type=Path,
     help=(
         "A path to VAN's original implementation directory. You can download from here:"
         " https://github.com/Visual-Attention-Network/VAN-Classification"
     ),
 )
 parser.add_argument(
     "--push_to_hub",
     default=True,
     type=bool,
     required=False,
     help="If True, push model and image processor to the hub.",
 )

 lowerCAmelCase_:       Any			     =       parser.parse_args()
 lowerCAmelCase_:       Path			     =       args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 lowerCAmelCase_:       Any			     =       args.van_dir
 # append the path to the parents to maskformer dir
 sys.path.append(str(van_dir.parent))
 from van.models.van import van_base, van_large, van_small, van_tiny

 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





"""simple docstring"""


import qiskit
from qiskit.providers import Backend







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__			,    lowercase__								=				bin(A   )[2:], bin(A   )[2:]  # Remove leading '0b'

     # Ensure that both strings are of the same length
     if len(A   ) > len(A   ):
          lowercase__								=				y.zfill(len(A   )   )
     else:
          lowercase__								=				x.zfill(len(A   )   )

     # We need (3 * number of bits in the larger number)+1 qBits
     # The second parameter is the number of classical registers, to measure the result
     lowercase__								=				qiskit.QuantumCircuit((len(A   ) * 3) + 1     ,					len(A   ) + 1   )

     # We are essentially "not-ing" the bits that are 1
     # Reversed because it's easier to perform ops on more significant bits
     for i in range(len(A   )   ):
          if x[::-1][i] == "1":
               circuit.x(A   )
     for j in range(len(A   )   ):
          if y[::-1][j] == "1":
               circuit.x(len(A   ) + j   )

     return circuit, x, y







def        __a (		A     ,					A     ,					A     ,					A     ,					A     ,					):






     '''simple docstring'''



     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.cx(A     ,					A   )







def        __a (		A     ,					A     ,					A = qiskit.Aer.get_backend("aer_simulator"   )     ,					):






     '''simple docstring'''



     if vala < 0 or vala < 0:
          raise ValueError("Both Integers must be positive!"   )

     # Store the Integers
     lowercase__			,    lowercase__			,    lowercase__								=				store_two_classics(A     ,					A   )
     for i in range(len(A   )   ):
          full_adder(A     ,					A     ,					len(A   ) + i     ,					len(A   ) + len(A   ) + i     ,					len(A   ) + len(A   ) + i + 1   )
          circuit.barrier()  # Optional, just for aesthetics

     # Measure the resultant qBits
     for i in range(len(A   ) + 1   ):
          circuit.measure([(len(A   ) * 2) + i]     ,					[i]   )

     lowercase__								=				qiskit.execute(A     ,					A     ,					shots=1   ).result()

     # The result is in binary. Convert it back to int
     return int(list(res.get_counts()   )[0]     ,					2   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




