
"""simple docstring"""

import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase			:       str									=				logging.get_logger(__name__)






def 							A		(       snake_case   :List[str]	)   ->			Any:
 __UpperCamelCase       						= MobileViTConfig()

 # size of the architecture
 if "mobilevit_s" in mobilevit_name:
  __UpperCamelCase       						= [1_4_4, 1_9_2, 2_4_0]
  __UpperCamelCase       						= [1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
 elif "mobilevit_xs" in mobilevit_name:
  __UpperCamelCase       						= [9_6, 1_2_0, 1_4_4]
  __UpperCamelCase       						= [1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
 elif "mobilevit_xxs" in mobilevit_name:
  __UpperCamelCase       						= [6_4, 8_0, 9_6]
  __UpperCamelCase       						= [1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
  __UpperCamelCase       						= 0.05
  __UpperCamelCase       						= 2.0

 if mobilevit_name.startswith('deeplabv3_'	):
  __UpperCamelCase       						= 5_1_2
  __UpperCamelCase       						= 1_6
  __UpperCamelCase       						= 2_1
  __UpperCamelCase       						= 'pascal-voc-id2label.json'
 else:
  __UpperCamelCase       						= 1_0_0_0
  __UpperCamelCase       						= 'imagenet-1k-id2label.json'

 __UpperCamelCase       						= 'huggingface/label-files'
 __UpperCamelCase       						= json.load(open(hf_hub_download(snake_case       ,      snake_case       ,      repo_type='dataset'	)       ,      'r'	)	)
 __UpperCamelCase       						= {int(snake_case	): v for k, v in idalabel.items()}
 __UpperCamelCase       						= idalabel
 __UpperCamelCase       						= {v: k for k, v in idalabel.items()}

 return config






def 							A		(       snake_case   :List[str]       ,      snake_case   :str=False	)   ->			Tuple:
 for i in range(1       ,      6	):
  if f'layer_{i}.' in name:
   __UpperCamelCase       						= name.replace(f'layer_{i}.'       ,      f'encoder.layer.{i - 1}.'	)

 if "conv_1." in name:
  __UpperCamelCase       						= name.replace('conv_1.'       ,      'conv_stem.'	)
 if ".block." in name:
  __UpperCamelCase       						= name.replace('.block.'       ,      '.'	)

 if "exp_1x1" in name:
  __UpperCamelCase       						= name.replace('exp_1x1'       ,      'expand_1x1'	)
 if "red_1x1" in name:
  __UpperCamelCase       						= name.replace('red_1x1'       ,      'reduce_1x1'	)
 if ".local_rep.conv_3x3." in name:
  __UpperCamelCase       						= name.replace('.local_rep.conv_3x3.'       ,      '.conv_kxk.'	)
 if ".local_rep.conv_1x1." in name:
  __UpperCamelCase       						= name.replace('.local_rep.conv_1x1.'       ,      '.conv_1x1.'	)
 if ".norm." in name:
  __UpperCamelCase       						= name.replace('.norm.'       ,      '.normalization.'	)
 if ".conv." in name:
  __UpperCamelCase       						= name.replace('.conv.'       ,      '.convolution.'	)
 if ".conv_proj." in name:
  __UpperCamelCase       						= name.replace('.conv_proj.'       ,      '.conv_projection.'	)

 for i in range(0       ,      2	):
  for j in range(0       ,      4	):
   if f'.{i}.{j}.' in name:
    __UpperCamelCase       						= name.replace(f'.{i}.{j}.'       ,      f'.{i}.layer.{j}.'	)

 for i in range(2       ,      6	):
  for j in range(0       ,      4	):
   if f'.{i}.{j}.' in name:
    __UpperCamelCase       						= name.replace(f'.{i}.{j}.'       ,      f'.{i}.'	)
    if "expand_1x1" in name:
     __UpperCamelCase       						= name.replace('expand_1x1'       ,      'downsampling_layer.expand_1x1'	)
    if "conv_3x3" in name:
     __UpperCamelCase       						= name.replace('conv_3x3'       ,      'downsampling_layer.conv_3x3'	)
    if "reduce_1x1" in name:
     __UpperCamelCase       						= name.replace('reduce_1x1'       ,      'downsampling_layer.reduce_1x1'	)

 for i in range(2       ,      5	):
  if f'.global_rep.{i}.weight' in name:
   __UpperCamelCase       						= name.replace(f'.global_rep.{i}.weight'       ,      '.layernorm.weight'	)
  if f'.global_rep.{i}.bias' in name:
   __UpperCamelCase       						= name.replace(f'.global_rep.{i}.bias'       ,      '.layernorm.bias'	)

 if ".global_rep." in name:
  __UpperCamelCase       						= name.replace('.global_rep.'       ,      '.transformer.'	)
 if ".pre_norm_mha.0." in name:
  __UpperCamelCase       						= name.replace('.pre_norm_mha.0.'       ,      '.layernorm_before.'	)
 if ".pre_norm_mha.1.out_proj." in name:
  __UpperCamelCase       						= name.replace('.pre_norm_mha.1.out_proj.'       ,      '.attention.output.dense.'	)
 if ".pre_norm_ffn.0." in name:
  __UpperCamelCase       						= name.replace('.pre_norm_ffn.0.'       ,      '.layernorm_after.'	)
 if ".pre_norm_ffn.1." in name:
  __UpperCamelCase       						= name.replace('.pre_norm_ffn.1.'       ,      '.intermediate.dense.'	)
 if ".pre_norm_ffn.4." in name:
  __UpperCamelCase       						= name.replace('.pre_norm_ffn.4.'       ,      '.output.dense.'	)
 if ".transformer." in name:
  __UpperCamelCase       						= name.replace('.transformer.'       ,      '.transformer.layer.'	)

 if ".aspp_layer." in name:
  __UpperCamelCase       						= name.replace('.aspp_layer.'       ,      '.'	)
 if ".aspp_pool." in name:
  __UpperCamelCase       						= name.replace('.aspp_pool.'       ,      '.'	)
 if "seg_head." in name:
  __UpperCamelCase       						= name.replace('seg_head.'       ,      'segmentation_head.'	)
 if "segmentation_head.classifier.classifier." in name:
  __UpperCamelCase       						= name.replace('segmentation_head.classifier.classifier.'       ,      'segmentation_head.classifier.'	)

 if "classifier.fc." in name:
  __UpperCamelCase       						= name.replace('classifier.fc.'       ,      'classifier.'	)
 elif (not base_model) and ("segmentation_head." not in name):
  __UpperCamelCase       						= 'mobilevit.' + name

 return name






def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[int]       ,      snake_case   :List[str]=False	)   ->			Tuple:
 if base_model:
  __UpperCamelCase       						= ''
 else:
  __UpperCamelCase       						= 'mobilevit.'

 for key in orig_state_dict.copy().keys():
  __UpperCamelCase       						= orig_state_dict.pop(snake_case	)

  if key[:8] == "encoder.":
   __UpperCamelCase       						= key[8:]

  if "qkv" in key:
   __UpperCamelCase       						= key.split('.'	)
   __UpperCamelCase       						= int(key_split[0][6:]	) - 1
   __UpperCamelCase       						= int(key_split[3]	)
   __UpperCamelCase       						= model.get_submodule(f'{model_prefix}encoder.layer.{layer_num}'	)
   __UpperCamelCase       						= layer.transformer.layer[transformer_num].attention.attention.all_head_size
   __UpperCamelCase       						= (
       f'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
   )
   if "weight" in key:
    __UpperCamelCase       						= val[:dim, :]
    __UpperCamelCase       						= val[dim : dim * 2, :]
    __UpperCamelCase       						= val[-dim:, :]
   else:
    __UpperCamelCase       						= val[:dim]
    __UpperCamelCase       						= val[dim : dim * 2]
    __UpperCamelCase       						= val[-dim:]
  else:
   __UpperCamelCase       						= val

 return orig_state_dict






def 							A		(       )   ->			List[Any]:
 __UpperCamelCase       						= 'http://images.cocodataset.org/val2017/000000039769.jpg'
 __UpperCamelCase       						= Image.open(requests.get(snake_case       ,      stream=snake_case	).raw	)
 return im






@torch.no_grad()
def 							A		(       snake_case   :List[Any]       ,      snake_case   :Any       ,      snake_case   :List[str]       ,      snake_case   :Tuple=False	)   ->			int:
 __UpperCamelCase       						= get_mobilevit_config(snake_case	)

 # load original state_dict
 __UpperCamelCase       						= torch.load(snake_case       ,      map_location='cpu'	)

 # load 🤗 model
 if mobilevit_name.startswith('deeplabv3_'	):
  __UpperCamelCase       						= MobileViTForSemanticSegmentation(snake_case	).eval()
 else:
  __UpperCamelCase       						= MobileViTForImageClassification(snake_case	).eval()

 __UpperCamelCase       						= convert_state_dict(snake_case       ,      snake_case	)
 model.load_state_dict(snake_case	)

 # Check outputs on an image, prepared by MobileViTImageProcessor
 __UpperCamelCase       						= MobileViTImageProcessor(crop_size=config.image_size       ,      size=config.image_size + 3_2	)
 __UpperCamelCase       						= image_processor(images=prepare_img()       ,      return_tensors='pt'	)
 __UpperCamelCase       						= model(**snake_case	)
 __UpperCamelCase       						= outputs.logits

 if mobilevit_name.startswith('deeplabv3_'	):
  assert logits.shape == (1, 2_1, 3_2, 3_2)

  if mobilevit_name == "deeplabv3_mobilevit_s":
   __UpperCamelCase       						= torch.tensor(
       [
           [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
           [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
           [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
       ]	)
  elif mobilevit_name == "deeplabv3_mobilevit_xs":
   __UpperCamelCase       						= torch.tensor(
       [
           [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
           [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
           [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
       ]	)
  elif mobilevit_name == "deeplabv3_mobilevit_xxs":
   __UpperCamelCase       						= torch.tensor(
       [
           [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
           [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
           [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
       ]	)
  else:
   raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}'	)

  assert torch.allclose(logits[0, :3, :3, :3]       ,      snake_case       ,      atol=1e-4	)
 else:
  assert logits.shape == (1, 1_0_0_0)

  if mobilevit_name == "mobilevit_s":
   __UpperCamelCase       						= torch.tensor([-0.9_866, 0.2_392, -1.1_241]	)
  elif mobilevit_name == "mobilevit_xs":
   __UpperCamelCase       						= torch.tensor([-2.4_761, -0.9_399, -1.9_587]	)
  elif mobilevit_name == "mobilevit_xxs":
   __UpperCamelCase       						= torch.tensor([-1.9_364, -1.2_327, -0.4_653]	)
  else:
   raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}'	)

  assert torch.allclose(logits[0, :3]       ,      snake_case       ,      atol=1e-4	)

 Path(snake_case	).mkdir(exist_ok=snake_case	)
 print(f'Saving model {mobilevit_name} to {pytorch_dump_folder_path}'	)
 model.save_pretrained(snake_case	)
 print(f'Saving image processor to {pytorch_dump_folder_path}'	)
 image_processor.save_pretrained(snake_case	)

 if push_to_hub:
  __UpperCamelCase       						= {
      'mobilevit_s': 'mobilevit-small',
      'mobilevit_xs': 'mobilevit-x-small',
      'mobilevit_xxs': 'mobilevit-xx-small',
      'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
      'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
      'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
  }

  print('Pushing to the hub...'	)
  __UpperCamelCase       						= model_mapping[mobilevit_name]
  image_processor.push_to_hub(snake_case       ,      organization='apple'	)
  model.push_to_hub(snake_case       ,      organization='apple'	)


if __name__ == "__main__":
      UpperCamelCase			:       Optional[int]									=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--mobilevit_name",
          default="mobilevit_s",
          type=str,
          help=(
              "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
              " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
          ),
      )
      parser.add_argument(
          "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt 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			:       Optional[int]									=				parser.parse_args()
      convert_movilevit_checkpoint(
          args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )





"""simple docstring"""

import numpy as np
import qiskit






def 							A		(       snake_case   :int = 8       ,      snake_case   :int | None = None	)   ->			str:
 __UpperCamelCase       						= np.random.default_rng(seed=snake_case	)

 # Roughly 25% of the qubits will contribute to the key.
 # So we take more than we need.
 __UpperCamelCase       						= 6 * key_len
 # Measurement basis for Alice's qubits.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)
 # The set of states Alice will prepare.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)
 # Measurement basis for Bob's qubits.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)

 # Quantum Circuit to simulate BB84
 __UpperCamelCase       						= qiskit.QuantumCircuit(snake_case       ,      name='BB84'	)

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

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

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 __UpperCamelCase       						= 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.
 __UpperCamelCase       						= qiskit.execute(snake_case       ,      snake_case       ,      shots=1       ,      seed_simulator=snake_case	)
 # Returns the result of measurement.
 __UpperCamelCase       						= job.result().get_counts(snake_case	).most_frequent()

 # Extracting the generated key from the simulation results.
 # Only keep measurement results where Alice and Bob chose the same basis.
 __UpperCamelCase       						= ''.join(
     [
         result_bit
         for alice_basis_bit, bob_basis_bit, result_bit in zip(
             snake_case       ,      snake_case       ,      snake_case	)
         if alice_basis_bit == bob_basis_bit
     ]	)

 # Get final key. Pad with 0 if too short, otherwise truncate.
 __UpperCamelCase       						= gen_key[:key_len] if len(snake_case	) >= key_len else gen_key.ljust(snake_case       ,      '0'	)
 return key


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

      testmod()



