

import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				(__A					:						Dict					):
   __lowerCAmelCase  :      Optional[Any]    =						MobileNetVaConfig(layer_norm_eps=0.001					)

   if "_quant" in model_name:
      raise ValueError("""Quantized models are not supported."""					)

   __lowerCAmelCase  :      Optional[Any]    =						re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$""" ,       __A					)
   if matches:
      __lowerCAmelCase  :      Tuple    =						float(matches[1]					)
      __lowerCAmelCase  :      List[str]    =						int(matches[2]					)

   # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
   # the usual 1000. The first class (index 0) is "background".
   __lowerCAmelCase  :      Optional[Any]    =						1_0_0_1
   __lowerCAmelCase  :      Any    =						"""imagenet-1k-id2label.json"""
   __lowerCAmelCase  :      List[str]    =						"""huggingface/label-files"""
   __lowerCAmelCase  :      Union[str, Any]    =						json.load(open(hf_hub_download(__A ,       __A ,       repo_type="""dataset"""					) ,       """r"""					)					)
   __lowerCAmelCase  :      int    =						{int(__A					) + 1: v for k, v in idalabel.items()}
   __lowerCAmelCase  :      Optional[Any]    =						"""background"""
   __lowerCAmelCase  :      int    =						idalabel
   __lowerCAmelCase  :      str    =						{v: k for k, v in idalabel.items()}

   return config





def      snake_case_				():
   __lowerCAmelCase  :      Dict    =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   __lowerCAmelCase  :      Any    =						Image.open(requests.get(__A ,       stream=__A					).raw					)
   return im





@torch.no_grad()
def      snake_case_				(__A					:						Optional[Any] ,       __A					:						Dict ,       __A					:						Optional[int] ,       __A					:						List[Any]=False					):
   __lowerCAmelCase  :      Optional[Any]    =						get_mobilenet_va_config(__A					)

   # Load 🤗 model
   __lowerCAmelCase  :      List[Any]    =						MobileNetVaForImageClassification(__A					).eval()

   # Load weights from TensorFlow checkpoint
   load_tf_weights_in_mobilenet_va(__A ,       __A ,       __A					)

   # Check outputs on an image, prepared by MobileNetV1ImageProcessor
   __lowerCAmelCase  :      Dict    =						MobileNetVaImageProcessor(
       crop_size={"""width""": config.image_size, """height""": config.image_size} ,       size={"""shortest_edge""": config.image_size + 3_2} ,       )
   __lowerCAmelCase  :      Dict    =						image_processor(images=prepare_img() ,       return_tensors="""pt"""					)
   __lowerCAmelCase  :      List[str]    =						model(**__A					)
   __lowerCAmelCase  :      Dict    =						outputs.logits

   assert logits.shape == (1, 1_0_0_1)

   if model_name == "mobilenet_v1_1.0_224":
      __lowerCAmelCase  :      Any    =						torch.tensor([-4.1739, -1.1233, 3.1205]					)
   elif model_name == "mobilenet_v1_0.75_192":
      __lowerCAmelCase  :      int    =						torch.tensor([-3.9440, -2.3141, -0.3333]					)
   else:
      __lowerCAmelCase  :      Any    =						None

   if expected_logits is not None:
      assert torch.allclose(logits[0, :3] ,       __A ,       atol=1e-4					)

   Path(__A					).mkdir(exist_ok=__A					)
   print(f'''Saving model {model_name} 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 to the hub..."""					)
      __lowerCAmelCase  :      Dict    =						"""google/""" + model_name
      image_processor.push_to_hub(__A					)
      model.push_to_hub(__A					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""mobilenet_v1_1.0_224""",
       type=str,
       help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
   )
   parser.add_argument(
       """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )

   __UpperCAmelCase								=			parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )





import numpy as np
import qiskit





def      snake_case_				(__A					:						int = 8 ,       __A					:						int | None = None					)      ->     str:
   __lowerCAmelCase  :      List[Any]    =						np.random.default_rng(seed=__A					)

   # Roughly 25% of the qubits will contribute to the key.
   # So we take more than we need.
   __lowerCAmelCase  :      Tuple    =						6 * key_len
   # Measurement basis for Alice's qubits.
   __lowerCAmelCase  :      List[Any]    =						rng.integers(2 ,       size=__A					)
   # The set of states Alice will prepare.
   __lowerCAmelCase  :      List[str]    =						rng.integers(2 ,       size=__A					)
   # Measurement basis for Bob's qubits.
   __lowerCAmelCase  :      List[Any]    =						rng.integers(2 ,       size=__A					)

   # Quantum Circuit to simulate BB84
   __lowerCAmelCase  :      int    =						qiskit.QuantumCircuit(__A ,       name="""BB84"""					)

   # Alice prepares her qubits according to rules above.
   for index, _ in enumerate(__A					):
      if alice_state[index] == 1:
         bbaa_circ.x(__A					)
      if alice_basis[index] == 1:
         bbaa_circ.h(__A					)
   bbaa_circ.barrier()

   # Bob measures the received qubits according to rules above.
   for index, _ in enumerate(__A					):
      if bob_basis[index] == 1:
         bbaa_circ.h(__A					)

   bbaa_circ.barrier()
   bbaa_circ.measure_all()

   # Simulate the quantum circuit.
   __lowerCAmelCase  :      Optional[Any]    =						qiskit.Aer.get_backend("""aer_simulator"""					)
   # We only need to run one shot because the key is unique.
   # Multiple shots will produce the same key.
   __lowerCAmelCase  :      Optional[Any]    =						qiskit.execute(__A ,       __A ,       shots=1 ,       seed_simulator=__A					)
   # Returns the result of measurement.
   __lowerCAmelCase  :      List[Any]    =						job.result().get_counts(__A					).most_frequent()

   # Extracting the generated key from the simulation results.
   # Only keep measurement results where Alice and Bob chose the same basis.
   __lowerCAmelCase  :      Optional[int]    =						"""""".join(
       [
           result_bit
           for alice_basis_bit, bob_basis_bit, result_bit in zip(
               __A ,       __A ,       __A					)
           if alice_basis_bit == bob_basis_bit
       ]					)

   # Get final key. Pad with 0 if too short, otherwise truncate.
   __lowerCAmelCase  :      Tuple    =						gen_key[:key_len] if len(__A					) >= key_len else gen_key.ljust(__A ,       """0"""					)
   return key


if __name__ == "__main__":
   print(F'The generated key is : {bbaa(8, seed=0)}')
   from doctest import testmod

   testmod()


