
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()
lowerCamelCase__    :		Optional[int]          =   logging.get_logger(__name__)

def 		UpperCamelCase	(      lowercase_   )		->	int:


  '''simple docstring'''






  lowercase__  :   Tuple    		=      MobileViTConfig()

  # size of the architecture
  if "mobilevit_s" in mobilevit_name:
    lowercase__  :   Optional[int]    		=      [1_44, 1_92, 2_40]
    lowercase__  :   str    		=      [16, 32, 64, 96, 1_28, 1_60, 6_40]
  elif "mobilevit_xs" in mobilevit_name:
    lowercase__  :   Any    		=      [96, 1_20, 1_44]
    lowercase__  :   Union[str, Any]    		=      [16, 32, 48, 64, 80, 96, 3_84]
  elif "mobilevit_xxs" in mobilevit_name:
    lowercase__  :   Optional[Any]    		=      [64, 80, 96]
    lowercase__  :   Dict    		=      [16, 16, 24, 48, 64, 80, 3_20]
    lowercase__  :   Tuple    		=      0.05
    lowercase__  :   int    		=      2.0

  if mobilevit_name.startswith("""deeplabv3_"""   ):
    lowercase__  :   Optional[int]    		=      5_12
    lowercase__  :   List[str]    		=      16
    lowercase__  :   Any    		=      21
    lowercase__  :   Any    		=      """pascal-voc-id2label.json"""
  else:
    lowercase__  :   Optional[int]    		=      10_00
    lowercase__  :   int    		=      """imagenet-1k-id2label.json"""

  lowercase__  :   Union[str, Any]    		=      """huggingface/label-files"""
  lowercase__  :   Dict    		=      json.load(open(hf_hub_download(lowercase_ , lowercase_ , repo_type="""dataset"""   ) , """r"""   )   )
  lowercase__  :   Dict    		=      {int(lowercase_   ): v for k, v in idalabel.items()}
  lowercase__  :   Optional[Any]    		=      idalabel
  lowercase__  :   str    		=      {v: k for k, v in idalabel.items()}

  return config

def 		UpperCamelCase	(      lowercase_ , lowercase_=False   )		->	List[Any]:


  '''simple docstring'''






  for i in range(1 , 6   ):
    if F'layer_{i}.' in name:
      lowercase__  :   Optional[int]    		=      name.replace(F'layer_{i}.' , F'encoder.layer.{i - 1}.'   )

  if "conv_1." in name:
    lowercase__  :   int    		=      name.replace("""conv_1.""" , """conv_stem."""   )
  if ".block." in name:
    lowercase__  :   List[Any]    		=      name.replace(""".block.""" , """."""   )

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

  for i in range(0 , 2   ):
    for j in range(0 , 4   ):
      if F'.{i}.{j}.' in name:
        lowercase__  :   Tuple    		=      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:
        lowercase__  :   List[Any]    		=      name.replace(F'.{i}.{j}.' , F'.{i}.'   )
        if "expand_1x1" in name:
          lowercase__  :   Any    		=      name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1"""   )
        if "conv_3x3" in name:
          lowercase__  :   Dict    		=      name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3"""   )
        if "reduce_1x1" in name:
          lowercase__  :   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:
      lowercase__  :   List[Any]    		=      name.replace(F'.global_rep.{i}.weight' , """.layernorm.weight"""   )
    if F'.global_rep.{i}.bias' in name:
      lowercase__  :   Any    		=      name.replace(F'.global_rep.{i}.bias' , """.layernorm.bias"""   )

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

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

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

  return name

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_=False   )		->	List[Any]:


  '''simple docstring'''






  if base_model:
    lowercase__  :   Any    		=      """"""
  else:
    lowercase__  :   Union[str, Any]    		=      """mobilevit."""

  for key in orig_state_dict.copy().keys():
    lowercase__  :   Optional[int]    		=      orig_state_dict.pop(lowercase_   )

    if key[:8] == "encoder.":
      lowercase__  :   Tuple    		=      key[8:]

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

  return orig_state_dict

def 		UpperCamelCase	(      )		->	Dict:


  '''simple docstring'''






  lowercase__  :   Tuple    		=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase__  :   Any    		=      Image.open(requests.get(lowercase_ , stream=lowercase_   ).raw   )
  return im

@torch.no_grad()
def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_=False   )		->	List[str]:


  '''simple docstring'''






  lowercase__  :   Tuple    		=      get_mobilevit_config(lowercase_   )

  # load original state_dict
  lowercase__  :   List[Any]    		=      torch.load(lowercase_ , map_location="""cpu"""   )

  # load 🤗 model
  if mobilevit_name.startswith("""deeplabv3_"""   ):
    lowercase__  :   Any    		=      MobileViTForSemanticSegmentation(lowercase_   ).eval()
  else:
    lowercase__  :   Dict    		=      MobileViTForImageClassification(lowercase_   ).eval()

  lowercase__  :   List[Any]    		=      convert_state_dict(lowercase_ , lowercase_   )
  model.load_state_dict(lowercase_   )

  # Check outputs on an image, prepared by MobileViTImageProcessor
  lowercase__  :   Dict    		=      MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32   )
  lowercase__  :   List[str]    		=      image_processor(images=prepare_img() , return_tensors="""pt"""   )
  lowercase__  :   Dict    		=      model(**lowercase_   )
  lowercase__  :   List[str]    		=      outputs.logits

  if mobilevit_name.startswith("""deeplabv3_"""   ):
    assert logits.shape == (1, 21, 32, 32)

    if mobilevit_name == "deeplabv3_mobilevit_s":
      lowercase__  :   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":
      lowercase__  :   Any    		=      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":
      lowercase__  :   Optional[int]    		=      torch.tensor(
          [
              [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
              [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
              [[-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] , lowercase_ , atol=1E-4   )
  else:
    assert logits.shape == (1, 10_00)

    if mobilevit_name == "mobilevit_s":
      lowercase__  :   Optional[int]    		=      torch.tensor([-0.9866, 0.2392, -1.1241]   )
    elif mobilevit_name == "mobilevit_xs":
      lowercase__  :   str    		=      torch.tensor([-2.4761, -0.9399, -1.9587]   )
    elif mobilevit_name == "mobilevit_xxs":
      lowercase__  :   List[Any]    		=      torch.tensor([-1.9364, -1.2327, -0.4653]   )
    else:
      raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'   )

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

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

  if push_to_hub:
    lowercase__  :   Dict    		=      {
        """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..."""   )
    lowercase__  :   Union[str, Any]    		=      model_mapping[mobilevit_name]
    image_processor.push_to_hub(lowercase_ , organization="""apple"""   )
    model.push_to_hub(lowercase_ , organization="""apple"""   )


if __name__ == "__main__":
     lowerCamelCase__    :		Any          =   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."""
     )

     lowerCamelCase__    :		str          =   parser.parse_args()
     convert_movilevit_checkpoint(
         args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )








import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 		UpperCamelCase	(      lowercase_ , lowercase_ = True , lowercase_ = math.inf , lowercase_ = -math.inf , lowercase_ = math.inf , lowercase_ = -math.inf , lowercase_ = False , lowercase_ = 1_00 , lowercase_ = 0.01 , lowercase_ = 1 , )		->	Any:


  '''simple docstring'''






  lowercase__  :   Any    		=      False
  lowercase__  :   Union[str, Any]    		=      search_prob
  lowercase__  :   int    		=      start_temperate
  lowercase__  :   Union[str, Any]    		=      []
  lowercase__  :   List[str]    		=      0
  lowercase__  :   Optional[int]    		=      None

  while not search_end:
    lowercase__  :   int    		=      current_state.score()
    if best_state is None or current_score > best_state.score():
      lowercase__  :   str    		=      current_state
    scores.append(lowercase_   )
    iterations += 1
    lowercase__  :   Union[str, Any]    		=      None
    lowercase__  :   int    		=      current_state.get_neighbors()
    while (
        next_state is None and neighbors
    ):  # till we do not find a neighbor that we can move to
      lowercase__  :   List[str]    		=      random.randint(0 , len(lowercase_   ) - 1   )  # picking a random neighbor
      lowercase__  :   Optional[int]    		=      neighbors.pop(lowercase_   )
      lowercase__  :   Dict    		=      picked_neighbor.score() - current_score

      if (
          picked_neighbor.x > max_x
          or picked_neighbor.x < min_x
          or picked_neighbor.y > max_y
          or picked_neighbor.y < min_y
      ):
        continue  # neighbor outside our bounds

      if not find_max:
        lowercase__  :   Any    		=      change * -1  # in case we are finding minimum
      if change > 0:  # improves the solution
        lowercase__  :   Tuple    		=      picked_neighbor
      else:
        lowercase__  :   Union[str, Any]    		=      (math.e) ** (
            change / current_temp
        )  # probability generation function
        if random.random() < probability:  # random number within probability
          lowercase__  :   Optional[int]    		=      picked_neighbor
    lowercase__  :   Dict    		=      current_temp - (current_temp * rate_of_decrease)

    if current_temp < threshold_temp or next_state is None:
      # temperature below threshold, or could not find a suitable neighbor
      lowercase__  :   Optional[Any]    		=      True
    else:
      lowercase__  :   List[Any]    		=      next_state

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(lowercase_   ) , lowercase_   )
    plt.xlabel("""Iterations"""   )
    plt.ylabel("""Function values"""   )
    plt.show()
  return best_state


if __name__ == "__main__":

     def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	str:


       '''simple docstring'''






       return (x**2) + (y**2)

     # starting the problem with initial coordinates (12, 47)
     lowerCamelCase__    :		str          =   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		List[Any]          =   simulated_annealing(
         prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
     )
     print(
         """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
         f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
     )

     # starting the problem with initial coordinates (12, 47)
     lowerCamelCase__    :		List[Any]          =   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Optional[int]          =   simulated_annealing(
         prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
     )
     print(
         """The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
         f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
     )

     def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Union[str, Any]:


       '''simple docstring'''






       return (3 * x**2) - (6 * y)

     lowerCamelCase__    :		Tuple          =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Union[str, Any]          =   simulated_annealing(prob, find_max=False, visualization=True)
     print(
         """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
         f'''{local_min.score()}'''
     )

     lowerCamelCase__    :		Dict          =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Tuple          =   simulated_annealing(prob, find_max=True, visualization=True)



     print(
         """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
         f'''{local_min.score()}'''
     )






