

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


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







def _a				( )					->					Any:

 """simple docstring"""






 __lowerCAmelCase:     List[str]     			=   DetaConfig(
     num_queries=9_00					,      encoder_ffn_dim=20_48					,      decoder_ffn_dim=20_48					,      num_feature_levels=5					,      assign_first_stage=SCREAMING_SNAKE_CASE					,      with_box_refine=SCREAMING_SNAKE_CASE					,      two_stage=SCREAMING_SNAKE_CASE					,      )

 # set labels
 __lowerCAmelCase:     Any     			=   91
 __lowerCAmelCase:     Optional[Any]     			=   'huggingface/label-files'
 __lowerCAmelCase:     List[Any]     			=   'coco-detection-id2label.json'
 __lowerCAmelCase:     List[Any]     			=   json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      repo_type='dataset'	)	)					,      'r'	)	)
 __lowerCAmelCase:     Dict     			=   {int(SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
 __lowerCAmelCase:     Tuple     			=   idalabel
 __lowerCAmelCase:     Dict     			=   {v: k for k, v in idalabel.items()}

 return config







def _a				( SCREAMING_SNAKE_CASE	:    str	)					->					Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:     Dict     			=   []

 # stem
 # fmt: off
 rename_keys.append(('backbone.0.body.conv1.weight', 'model.backbone.model.embedder.embedder.convolution.weight')	)
 rename_keys.append(('backbone.0.body.bn1.weight', 'model.backbone.model.embedder.embedder.normalization.weight')	)
 rename_keys.append(('backbone.0.body.bn1.bias', 'model.backbone.model.embedder.embedder.normalization.bias')	)
 rename_keys.append(('backbone.0.body.bn1.running_mean', 'model.backbone.model.embedder.embedder.normalization.running_mean')	)
 rename_keys.append(('backbone.0.body.bn1.running_var', 'model.backbone.model.embedder.embedder.normalization.running_var')	)
 # stages
 for stage_idx in range(len(config.backbone_config.depths	)	):
  for layer_idx in range(config.backbone_config.depths[stage_idx]	):
   # shortcut
   if layer_idx == 0:
    rename_keys.append(
        (
            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
        )	)
    rename_keys.append(
        (
            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
        )	)
    rename_keys.append(
        (
            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
        )	)
    rename_keys.append(
        (
            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
        )	)
    rename_keys.append(
        (
            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
        )	)
   # 3 convs
   for i in range(3	):
    rename_keys.append(
        (
            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
        )	)
    rename_keys.append(
        (
            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
        )	)
    rename_keys.append(
        (
            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
        )	)
    rename_keys.append(
        (
            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
        )	)
    rename_keys.append(
        (
            f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
            f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
        )	)
    # transformer encoder
 for i in range(config.encoder_layers	):
  rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')	)
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')	)

 # transformer decoder
 for i in range(config.decoder_layers	):
  rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')	)
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')	)

 # fmt: on

 return rename_keys







def _a				( SCREAMING_SNAKE_CASE	:    Any					,      SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    int	)					->					Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:     Dict     			=   dct.pop(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     List[str]     			=   val







def _a				( SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    Union[str, Any]	)					->					Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   config.d_model
 for i in range(config.decoder_layers	):
  # read in weights + bias of input projection layer of self-attention
  __lowerCAmelCase:     int     			=   state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
  __lowerCAmelCase:     int     			=   state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''	)
  # next, add query, keys and values (in that order) to the state dict
  __lowerCAmelCase:     Optional[int]     			=   in_proj_weight[:hidden_size, :]
  __lowerCAmelCase:     List[str]     			=   in_proj_bias[:hidden_size]
  __lowerCAmelCase:     List[str]     			=   in_proj_weight[
      hidden_size : hidden_size * 2, :
  ]
  __lowerCAmelCase:     List[str]     			=   in_proj_bias[hidden_size : hidden_size * 2]
  __lowerCAmelCase:     Any     			=   in_proj_weight[-hidden_size:, :]
  __lowerCAmelCase:     Any     			=   in_proj_bias[-hidden_size:]







def _a				( )					->					List[Any]:

 """simple docstring"""






 __lowerCAmelCase:     Optional[Any]     			=   'http://images.cocodataset.org/val2017/000000039769.jpg'
 __lowerCAmelCase:     Any     			=   Image.open(requests.get(SCREAMING_SNAKE_CASE					,      stream=SCREAMING_SNAKE_CASE	).raw	)

 return im







@torch.no_grad()
def _a				( SCREAMING_SNAKE_CASE	:    List[Any]					,      SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    Tuple	)					->					Any:

 """simple docstring"""






 __lowerCAmelCase:     Optional[Any]     			=   get_deta_config()

 # load original state dict
 if model_name == "deta-resnet-50":
  __lowerCAmelCase:     Optional[Any]     			=   'adet_checkpoint0011.pth'
 elif model_name == "deta-resnet-50-24-epochs":
  __lowerCAmelCase:     Dict     			=   'adet_2x_checkpoint0023.pth'
 else:
  raise ValueError(f'''Model name {model_name} not supported'''	)
 __lowerCAmelCase:     Union[str, Any]     			=   hf_hub_download(repo_id='nielsr/deta-checkpoints'					,      filename=SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Tuple     			=   torch.load(SCREAMING_SNAKE_CASE					,      map_location='cpu'	)['model']

 # rename keys
 __lowerCAmelCase:     str     			=   create_rename_keys(SCREAMING_SNAKE_CASE	)
 for src, dest in rename_keys:
  rename_key(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	)
 read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	)

 # fix some prefixes
 for key in state_dict.copy().keys():
  if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
   __lowerCAmelCase:     Union[str, Any]     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
   __lowerCAmelCase:     List[Any]     			=   val
  if "input_proj" in key:
   __lowerCAmelCase:     Dict     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
   __lowerCAmelCase:     Union[str, Any]     			=   val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   __lowerCAmelCase:     Union[str, Any]     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
   __lowerCAmelCase:     Optional[Any]     			=   val

    # finally, create HuggingFace model and load state dict
 __lowerCAmelCase:     Union[str, Any]     			=   DetaForObjectDetection(SCREAMING_SNAKE_CASE	)
 model.load_state_dict(SCREAMING_SNAKE_CASE	)
 model.eval()

 __lowerCAmelCase:     Any     			=   'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(SCREAMING_SNAKE_CASE	)

 # load image processor
 __lowerCAmelCase:     int     			=   DetaImageProcessor(format='coco_detection'	)

 # verify our conversion on image
 __lowerCAmelCase:     Tuple     			=   prepare_img()
 __lowerCAmelCase:     Any     			=   processor(images=SCREAMING_SNAKE_CASE					,      return_tensors='pt'	)
 __lowerCAmelCase:     Any     			=   encoding['pixel_values']
 __lowerCAmelCase:     Optional[Any]     			=   model(pixel_values.to(SCREAMING_SNAKE_CASE	)	)

 # verify logits
 if model_name == "deta-resnet-50":
  __lowerCAmelCase:     Tuple     			=   torch.tensor(
      [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]	)
  __lowerCAmelCase:     List[str]     			=   torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]	)
 elif model_name == "deta-resnet-50-24-epochs":
  __lowerCAmelCase:     Tuple     			=   torch.tensor(
      [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]	)
  __lowerCAmelCase:     Union[str, Any]     			=   torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]	)

 assert torch.allclose(outputs.logits[0, :3, :3]					,      expected_logits.to(SCREAMING_SNAKE_CASE	)					,      atol=1E-4	)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,      expected_boxes.to(SCREAMING_SNAKE_CASE	)					,      atol=1E-4	)
 print('Everything ok!'	)

 if pytorch_dump_folder_path:
  # Save model and processor
  logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''	)
  Path(SCREAMING_SNAKE_CASE	).mkdir(exist_ok=SCREAMING_SNAKE_CASE	)
  model.save_pretrained(SCREAMING_SNAKE_CASE	)
  processor.save_pretrained(SCREAMING_SNAKE_CASE	)

 # Push to hub
 if push_to_hub:
  print('Pushing model and processor to hub...'	)
  model.push_to_hub(f'''jozhang97/{model_name}'''	)
  processor.push_to_hub(f'''jozhang97/{model_name}'''	)


if __name__ == "__main__":
      _a	 =     argparse.ArgumentParser()

      parser.add_argument(
          '''--model_name''',
          type=str,
          default='''deta-resnet-50''',
          choices=['''deta-resnet-50''', '''deta-resnet-50-24-epochs'''],
          help='''Name of the model you\'d like to convert.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default=None,
          type=str,
          help='''Path to the folder to output PyTorch model.''',
      )
      parser.add_argument(
          '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
      )
      _a	 =     parser.parse_args()
      convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import math






class 							A_				:


     def __init__(							self    :	Any    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	Any      )							->			Any:
      __lowerCAmelCase:     Optional[Any]     			=   x
      __lowerCAmelCase:     Any     			=   y
      __lowerCAmelCase:     Tuple     			=   step_size
      __lowerCAmelCase:     Dict     			=   function_to_optimize


     def 						UpperCAmelCase						(							self    :	Dict      )							->			int:
      return self.function(self.x    ,						self.y      )


     def 						UpperCAmelCase						(							self    :	str      )							->			int:
      __lowerCAmelCase:     Union[str, Any]     			=   self.step_size
      return [
          SearchProblem(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						self.function      )
          for x, y in (
              (self.x - step_size, self.y - step_size),
              (self.x - step_size, self.y),
              (self.x - step_size, self.y + step_size),
              (self.x, self.y - step_size),
              (self.x, self.y + step_size),
              (self.x + step_size, self.y - step_size),
              (self.x + step_size, self.y),
              (self.x + step_size, self.y + step_size),
          )
      ]


     def __hash__(							self    :	Optional[Any]      )							->			int:
      return hash(str(self      )      )


     def __eq__(							self    :	Dict    ,						UpperCAmelCase    :	Tuple      )							->			Any:
      if isinstance(UpperCAmelCase    ,						UpperCAmelCase      ):
       return hash(str(self      )      ) == hash(str(UpperCAmelCase      )      )
      return False


     def __str__(							self    :	Optional[Any]      )							->			Dict:
      return F'''x: {self.x} y: {self.y}'''







def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    bool = True					,      SCREAMING_SNAKE_CASE	:    float = math.inf					,      SCREAMING_SNAKE_CASE	:    float = -math.inf					,      SCREAMING_SNAKE_CASE	:    float = math.inf					,      SCREAMING_SNAKE_CASE	:    float = -math.inf					,      SCREAMING_SNAKE_CASE	:    bool = False					,      SCREAMING_SNAKE_CASE	:    int = 1_00_00					,      )					->					SearchProblem:

 """simple docstring"""






 __lowerCAmelCase:     Dict     			=   search_prob
 __lowerCAmelCase:     str     			=   []  # list to store the current score at each iteration
 __lowerCAmelCase:     Optional[int]     			=   0
 __lowerCAmelCase:     int     			=   False
 __lowerCAmelCase:     Any     			=   set()
 while not solution_found and iterations < max_iter:
  visited.add(SCREAMING_SNAKE_CASE	)
  iterations += 1
  __lowerCAmelCase:     int     			=   current_state.score()
  scores.append(SCREAMING_SNAKE_CASE	)
  __lowerCAmelCase:     Any     			=   current_state.get_neighbors()
  __lowerCAmelCase:     Tuple     			=   -math.inf
  __lowerCAmelCase:     Any     			=   math.inf
  __lowerCAmelCase:     Dict     			=   None  # to hold the next best neighbor
  for neighbor in neighbors:
   if neighbor in visited:
    continue  # do not want to visit the same state again
   if (
       neighbor.x > max_x
       or neighbor.x < min_x
       or neighbor.y > max_y
       or neighbor.y < min_y
   ):
    continue  # neighbor outside our bounds
   __lowerCAmelCase:     List[str]     			=   neighbor.score() - current_score
   if find_max:  # finding max
    # going to direction with greatest ascent
    if change > max_change and change > 0:
     __lowerCAmelCase:     int     			=   change
     __lowerCAmelCase:     int     			=   neighbor
   else:  # finding min
    # to direction with greatest descent
    if change < min_change and change < 0:
     __lowerCAmelCase:     str     			=   change
     __lowerCAmelCase:     str     			=   neighbor
  if next_state is not None:
   # we found at least one neighbor which improved the current state
   __lowerCAmelCase:     Optional[Any]     			=   next_state
  else:
   # since we have no neighbor that improves the solution we stop the search
   __lowerCAmelCase:     Dict     			=   True

 if visualization:
  from matplotlib import pyplot as plt

  plt.plot(range(SCREAMING_SNAKE_CASE	)					,      SCREAMING_SNAKE_CASE	)
  plt.xlabel('Iterations'	)
  plt.ylabel('Function values'	)
  plt.show()

 return current_state


if __name__ == "__main__":
  import doctest

  doctest.testmod()







  def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    List[str]	)					->					Optional[Any]:

   """simple docstring"""






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

  # starting the problem with initial coordinates (3, 4)
  _a	 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(prob, find_max=False)
  print(
      '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
      f"{local_min.score()}"
  )

  # starting the problem with initial coordinates (12, 47)
  _a	 =     SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(
      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()}"
  )







  def _a				( SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    List[Any]	)					->					Dict:

   """simple docstring"""






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

  _a	 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(prob, find_max=True)



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





