code: 
  
  
  
  
  """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()
  __A :		List[Any]      							=    logging.get_logger(__name__)
  @dataclass
  class 							lowerCamelCase		:
     lowercase       :  nn.Module
     lowercase       :  List[nn.Module]      =				field(default_factory=_UpperCAmelCase				)
     lowercase       :  list      =				field(default_factory=_UpperCAmelCase				)
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Union[str, Any]          =							len(list(m.modules() ) ) == 1 or isinstance(SCREAMING_SNAKE_CASE_    ,      nn.Convad ) or isinstance(SCREAMING_SNAKE_CASE_    ,      nn.BatchNormad )
         if has_not_submodules:
             if not isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
                 self.traced.append(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def __call__(							self    ,      SCREAMING_SNAKE_CASE_ ):
         for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook ) )
         self.module(SCREAMING_SNAKE_CASE_ )
         [x.remove() for x in self.handles]
         return self
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         # check the len of the state_dict keys to see if we have learnable params
         return list(filter(lambda SCREAMING_SNAKE_CASE_ : len(list(x.state_dict().keys() ) ) > 0    ,      self.traced ) )
  
  
  
  
  
  
  
  @dataclass
  class 							lowerCamelCase		:
     lowercase       :  nn.Module
     lowercase       :  nn.Module
     lowercase       :  int      =				0
     lowercase       :  List      =				field(default_factory=_UpperCAmelCase				)
     lowercase       :  List      =				field(default_factory=_UpperCAmelCase				)
  
  
  
  
  
  
  
     def __call__(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[int]          =							Tracker(self.dest )(SCREAMING_SNAKE_CASE_ ).parametrized
         UpperCamelCase				:   List[str]          =							Tracker(self.src )(SCREAMING_SNAKE_CASE_ ).parametrized
  
         UpperCamelCase				:   Optional[int]          =							list(filter(lambda SCREAMING_SNAKE_CASE_ : type(SCREAMING_SNAKE_CASE_ ) not in self.src_skip    ,      SCREAMING_SNAKE_CASE_ ) )
         UpperCamelCase				:   List[str]          =							list(filter(lambda SCREAMING_SNAKE_CASE_ : type(SCREAMING_SNAKE_CASE_ ) not in self.dest_skip    ,      SCREAMING_SNAKE_CASE_ ) )
  
         if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
             raise Exception(
                 f'Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE_ )} operations while'
                 f' destination module has {len(SCREAMING_SNAKE_CASE_ )}.' )
  
         for dest_m, src_m in zip(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             dest_m.load_state_dict(src_m.state_dict() )
             if self.verbose == 1:
                 print(f'Transfered from={src_m} to={dest_m}' )
  
  
  
  
  
  
  
  def 					A_			(		snake_case_     :       nn.Module						,snake_case_     :       nn.Module    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
      UpperCamelCase				:   Union[str, Any]          =							from_model.state_dict()
      UpperCamelCase				:   Optional[int]          =							our_model.state_dict()
      UpperCamelCase				:   Optional[int]          =							our_model.config
      UpperCamelCase				:   Tuple          =							[]
      for stage_idx in range(len(config.hidden_sizes    )    ):
          for block_id in range(config.depths[stage_idx]    ):
              UpperCamelCase				:   int          =							f'block{stage_idx + 1}.{block_id}.layer_scale_1'
              UpperCamelCase				:   str          =							f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'
  
              all_keys.append((from_key, to_key)    )
              UpperCamelCase				:   Any          =							f'block{stage_idx + 1}.{block_id}.layer_scale_2'
              UpperCamelCase				:   Union[str, Any]          =							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:
          UpperCamelCase				:   Union[str, Any]          =							from_state_dict.pop(snake_case_    )
  
      our_model.load_state_dict(snake_case_    )
      return our_model
  def 					A_			(		snake_case_     :       str						,snake_case_     :       VanConfig						,snake_case_     :       str						,snake_case_     :       nn.Module						,snake_case_     :       Path						,snake_case_     :       bool = True						,):
  
  
  
  
  
  
  
      '''simple docstring'''
      print(f'Downloading weights for {name}...'    )
      UpperCamelCase				:   Optional[int]          =							cached_download(snake_case_    )
      print(f'Converting {name}...'    )
      UpperCamelCase				:   List[str]          =							torch.load(snake_case_    )["""state_dict"""]
      from_model.load_state_dict(snake_case_    )
      from_model.eval()
      with torch.no_grad():
          UpperCamelCase				:   List[str]          =							VanForImageClassification(snake_case_    ).eval()
          UpperCamelCase				:   Union[str, Any]          =							ModuleTransfer(src=snake_case_						,dest=snake_case_    )
          UpperCamelCase				:   List[str]          =							torch.randn((1, 3, 2_2_4, 2_2_4)    )
          module_transfer(snake_case_    )
          UpperCamelCase				:   Dict          =							copy_parameters(snake_case_						,snake_case_    )
  
      if not torch.allclose(from_model(snake_case_    )						,our_model(snake_case_    ).logits    ):
          raise ValueError("""The model logits don't match the original one."""    )
  
      UpperCamelCase				:   List[str]          =							name
      print(snake_case_    )
  
      if push_to_hub:
          our_model.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name						,commit_message="""Add model"""						,use_temp_dir=snake_case_						,)
  
          # we can use the convnext one
          UpperCamelCase				:   Optional[Any]          =							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=snake_case_						,)
  
          print(f'Pushed {checkpoint_name}'    )
  def 					A_			(		snake_case_     :       Path						,snake_case_     :       str = None						,snake_case_     :       bool = True    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Tuple          =							"""imagenet-1k-id2label.json"""
      UpperCamelCase				:   str          =							1_0_0_0
  
      UpperCamelCase				:   Optional[Any]          =							"""huggingface/label-files"""
      UpperCamelCase				:   List[str]          =							num_labels
      UpperCamelCase				:   Optional[Any]          =							json.load(open(hf_hub_download(snake_case_						,snake_case_						,repo_type="""dataset"""    )						,"""r"""    )    )
      UpperCamelCase				:   List[str]          =							{int(snake_case_    ): v for k, v in idalabel.items()}
  
      UpperCamelCase				:   int          =							idalabel
      UpperCamelCase				:   List[Any]          =							{v: k for k, v in idalabel.items()}
  
      UpperCamelCase				:   Optional[Any]          =							partial(snake_case_						,num_labels=snake_case_						,idalabel=snake_case_						,labelaid=snake_case_    )
  
      UpperCamelCase				:   str          =							{
          """van-tiny""": ImageNetPreTrainedConfig(
              hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]						,depths=[3, 3, 5, 2]						,mlp_ratios=[8, 8, 4, 4]						,),
          """van-small""": ImageNetPreTrainedConfig(
              hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]						,depths=[2, 2, 4, 2]						,mlp_ratios=[8, 8, 4, 4]						,),
          """van-base""": ImageNetPreTrainedConfig(
              hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]						,depths=[3, 3, 1_2, 3]						,mlp_ratios=[8, 8, 4, 4]						,),
          """van-large""": ImageNetPreTrainedConfig(
              hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]						,depths=[3, 5, 2_7, 3]						,mlp_ratios=[8, 8, 4, 4]						,),
      }
  
      UpperCamelCase				:   Optional[Any]          =							{
          """van-tiny""": van_tiny,
          """van-small""": van_small,
          """van-base""": van_base,
          """van-large""": van_large,
      }
  
      UpperCamelCase				:   List[str]          =							{
          """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(
              snake_case_						,names_to_config[model_name]						,checkpoint=names_to_original_checkpoints[model_name]						,from_model=names_to_original_models[model_name]()						,save_directory=snake_case_						,push_to_hub=snake_case_						,)
      else:
          for model_name, config in names_to_config.items():
              convert_weight_and_push(
                  snake_case_						,snake_case_						,checkpoint=names_to_original_checkpoints[model_name]						,from_model=names_to_original_models[model_name]()						,save_directory=snake_case_						,push_to_hub=snake_case_						,)
  
  
  if __name__ == "__main__":
    __A :		Dict      							=    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.''',
    )
  
    __A :		Optional[int]      							=    parser.parse_args()
    __A :		Path      							=    args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    __A :		str      							=    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)
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import qiskit
  from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
  def 					A_			(		snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Union[str, Any]          =							QuantumRegister(3						,"""qr"""    )  # Define the number of quantum bits
      UpperCamelCase				:   Any          =							ClassicalRegister(1						,"""cr"""    )  # Define the number of classical bits
  
      UpperCamelCase				:   Any          =							QuantumCircuit(snake_case_						,snake_case_    )  # Define the quantum circuit.
  
      # Build the circuit
      quantum_circuit.u(snake_case_						,snake_case_						,snake_case_						,0    )  # Quantum State to teleport
      quantum_circuit.h(1    )  # add hadamard gate
      quantum_circuit.cx(
          1						,2    )  # add control gate with qubit 1 as control and 2 as target.
      quantum_circuit.cx(0						,1    )
      quantum_circuit.h(0    )
      quantum_circuit.cz(0						,2    )  # add control z gate.
      quantum_circuit.cx(1						,2    )
  
      quantum_circuit.measure([2]						,[0]    )  # measure the qubit.
  
      # Simulate the circuit using qasm simulator
      UpperCamelCase				:   Tuple          =							Aer.get_backend("""aer_simulator"""    )
      UpperCamelCase				:   Any          =							execute(snake_case_						,snake_case_						,shots=1_0_0_0    )
  
      return job.result().get_counts(snake_case_    )
  
  
  if __name__ == "__main__":
    print(
        '''Total count for teleported state is: '''
        F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
    )
  
  
  
  
  