






"""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    :   Tuple   			=						logging.get_logger(__name__)




def       snake_case  (A_				:List[str]				):







  '''simple docstring'''

  a					:      Optional[Any]    =       MobileViTConfig()

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

  if mobilevit_name.startswith('deeplabv3_'				):
    a					:      Dict    =       5_1_2
    a					:      Union[str, Any]    =       1_6
    a					:      Optional[Any]    =       2_1
    a					:      Optional[Any]    =       'pascal-voc-id2label.json'
  else:
    a					:      List[str]    =       1_0_0_0
    a					:      Union[str, Any]    =       'imagenet-1k-id2label.json'

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

  return config




def       snake_case  (A_				:Dict     ,      A_				:Dict=False				):







  '''simple docstring'''

  for i in range(1     ,      6				):
    if f'''layer_{i}.''' in name:
      a					:      Union[str, Any]    =       name.replace(f'''layer_{i}.'''     ,      f'''encoder.layer.{i - 1}.'''				)

  if "conv_1." in name:
    a					:      Dict    =       name.replace('conv_1.'     ,      'conv_stem.'				)
  if ".block." in name:
    a					:      int    =       name.replace('.block.'     ,      '.'				)

  if "exp_1x1" in name:
    a					:      Dict    =       name.replace('exp_1x1'     ,      'expand_1x1'				)
  if "red_1x1" in name:
    a					:      str    =       name.replace('red_1x1'     ,      'reduce_1x1'				)
  if ".local_rep.conv_3x3." in name:
    a					:      Dict    =       name.replace('.local_rep.conv_3x3.'     ,      '.conv_kxk.'				)
  if ".local_rep.conv_1x1." in name:
    a					:      int    =       name.replace('.local_rep.conv_1x1.'     ,      '.conv_1x1.'				)
  if ".norm." in name:
    a					:      Optional[int]    =       name.replace('.norm.'     ,      '.normalization.'				)
  if ".conv." in name:
    a					:      Tuple    =       name.replace('.conv.'     ,      '.convolution.'				)
  if ".conv_proj." in name:
    a					:      Any    =       name.replace('.conv_proj.'     ,      '.conv_projection.'				)

  for i in range(0     ,      2				):
    for j in range(0     ,      4				):
      if f'''.{i}.{j}.''' in name:
        a					:      Dict    =       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:
        a					:      int    =       name.replace(f'''.{i}.{j}.'''     ,      f'''.{i}.'''				)
        if "expand_1x1" in name:
          a					:      Dict    =       name.replace('expand_1x1'     ,      'downsampling_layer.expand_1x1'				)
        if "conv_3x3" in name:
          a					:      Dict    =       name.replace('conv_3x3'     ,      'downsampling_layer.conv_3x3'				)
        if "reduce_1x1" in name:
          a					:      Union[str, Any]    =       name.replace('reduce_1x1'     ,      'downsampling_layer.reduce_1x1'				)

  for i in range(2     ,      5				):
    if f'''.global_rep.{i}.weight''' in name:
      a					:      List[str]    =       name.replace(f'''.global_rep.{i}.weight'''     ,      '.layernorm.weight'				)
    if f'''.global_rep.{i}.bias''' in name:
      a					:      List[Any]    =       name.replace(f'''.global_rep.{i}.bias'''     ,      '.layernorm.bias'				)

  if ".global_rep." in name:
    a					:      str    =       name.replace('.global_rep.'     ,      '.transformer.'				)
  if ".pre_norm_mha.0." in name:
    a					:      Any    =       name.replace('.pre_norm_mha.0.'     ,      '.layernorm_before.'				)
  if ".pre_norm_mha.1.out_proj." in name:
    a					:      str    =       name.replace('.pre_norm_mha.1.out_proj.'     ,      '.attention.output.dense.'				)
  if ".pre_norm_ffn.0." in name:
    a					:      Optional[Any]    =       name.replace('.pre_norm_ffn.0.'     ,      '.layernorm_after.'				)
  if ".pre_norm_ffn.1." in name:
    a					:      int    =       name.replace('.pre_norm_ffn.1.'     ,      '.intermediate.dense.'				)
  if ".pre_norm_ffn.4." in name:
    a					:      Dict    =       name.replace('.pre_norm_ffn.4.'     ,      '.output.dense.'				)
  if ".transformer." in name:
    a					:      Tuple    =       name.replace('.transformer.'     ,      '.transformer.layer.'				)

  if ".aspp_layer." in name:
    a					:      Union[str, Any]    =       name.replace('.aspp_layer.'     ,      '.'				)
  if ".aspp_pool." in name:
    a					:      Any    =       name.replace('.aspp_pool.'     ,      '.'				)
  if "seg_head." in name:
    a					:      List[str]    =       name.replace('seg_head.'     ,      'segmentation_head.'				)
  if "segmentation_head.classifier.classifier." in name:
    a					:      List[Any]    =       name.replace('segmentation_head.classifier.classifier.'     ,      'segmentation_head.classifier.'				)

  if "classifier.fc." in name:
    a					:      Dict    =       name.replace('classifier.fc.'     ,      'classifier.'				)
  elif (not base_model) and ("segmentation_head." not in name):
    a					:      List[Any]    =       'mobilevit.' + name

  return name




def       snake_case  (A_				:List[Any]     ,      A_				:Optional[int]     ,      A_				:List[Any]=False				):







  '''simple docstring'''

  if base_model:
    a					:      Dict    =       ''
  else:
    a					:      str    =       'mobilevit.'

  for key in orig_state_dict.copy().keys():
    a					:      Any    =       orig_state_dict.pop(__snake_case				)

    if key[:8] == "encoder.":
      a					:      List[Any]    =       key[8:]

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

  return orig_state_dict




def       snake_case  ():







  '''simple docstring'''

  a					:      str    =       'http://images.cocodataset.org/val2017/000000039769.jpg'
  a					:      Any    =       Image.open(requests.get(__snake_case     ,      stream=__snake_case				).raw				)
  return im




@torch.no_grad()
def       snake_case  (A_				:Optional[Any]     ,      A_				:List[str]     ,      A_				:Dict     ,      A_				:int=False				):







  '''simple docstring'''

  a					:      Tuple    =       get_mobilevit_config(__snake_case				)

  # load original state_dict
  a					:      Optional[Any]    =       torch.load(__snake_case     ,      map_location='cpu'				)

  # load 🤗 model
  if mobilevit_name.startswith('deeplabv3_'				):
    a					:      Union[str, Any]    =       MobileViTForSemanticSegmentation(__snake_case				).eval()
  else:
    a					:      Union[str, Any]    =       MobileViTForImageClassification(__snake_case				).eval()

  a					:      Dict    =       convert_state_dict(__snake_case     ,      __snake_case				)
  model.load_state_dict(__snake_case				)

  # Check outputs on an image, prepared by MobileViTImageProcessor
  a					:      List[str]    =       MobileViTImageProcessor(crop_size=config.image_size     ,      size=config.image_size + 3_2				)
  a					:      List[Any]    =       image_processor(images=prepare_img()     ,      return_tensors='pt'				)
  a					:      List[Any]    =       model(**__snake_case				)
  a					:      Optional[Any]    =       outputs.logits

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

    if mobilevit_name == "deeplabv3_mobilevit_s":
      a					:      Union[str, Any]    =       torch.tensor(
          [
              [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
              [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
              [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
          ]				)
    elif mobilevit_name == "deeplabv3_mobilevit_xs":
      a					:      Tuple    =       torch.tensor(
          [
              [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
              [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
              [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
          ]				)
    elif mobilevit_name == "deeplabv3_mobilevit_xxs":
      a					:      Dict    =       torch.tensor(
          [
              [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
              [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8624, -9.5964], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
              [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
          ]				)
    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":
      a					:      Dict    =       torch.tensor([-0.9866, 0.2392, -1.1241]				)
    elif mobilevit_name == "mobilevit_xs":
      a					:      List[Any]    =       torch.tensor([-2.4761, -0.9399, -1.9587]				)
    elif mobilevit_name == "mobilevit_xxs":
      a					:      Any    =       torch.tensor([-1.9364, -1.2327, -0.4653]				)
    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:
    a					:      Optional[Any]    =       {
        '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...'				)
    a					:      Tuple    =       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    :   Tuple   			=						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    :   str   			=						parser.parse_args()
   convert_movilevit_checkpoint(
       args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )












"""simple docstring"""


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

  return math.sqrt(sum(pow(a - b     ,      2				) for a, b in zip(A_     ,      A_				)				)				)




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

  if dataset.ndim != value_array.ndim:
    a					:      Optional[Any]    =       (
        'Wrong input data\'s dimensions... '
        f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
    )
    raise ValueError(A_				)

  try:
    if dataset.shape[1] != value_array.shape[1]:
      a					:      Optional[int]    =       (
          'Wrong input data\'s shape... '
          f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
      )
      raise ValueError(A_				)
  except IndexError:
    if dataset.ndim != value_array.ndim:
      raise TypeError('Wrong shape'				)

  if dataset.dtype != value_array.dtype:
    a					:      Optional[Any]    =       (
        'Input data have different datatype... '
        f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
    )
    raise TypeError(A_				)

  a					:      Tuple    =       []

  for value in value_array:
    a					:      List[Any]    =       euclidean(A_     ,      dataset[0]				)
    a					:      int    =       dataset[0].tolist()

    for dataset_value in dataset[1:]:
      a					:      Optional[int]    =       euclidean(A_     ,      A_				)

      if dist > temp_dist:
        a					:      List[str]    =       temp_dist
        a					:      int    =       dataset_value.tolist()

    answer.append([vector, dist]				)

  return answer




def       snake_case  (A_				:np.ndarray     ,      A_				:np.ndarray				):







  '''simple docstring'''

  return np.dot(A_     ,      A_				) / (norm(A_				) * norm(A_				))


if __name__ == "__main__":
   import doctest

   doctest.testmod()




