




"""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)










"""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)}'''
  )




