






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        __lowerCamelCase			(		snake_case__		)							->  Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  MobileNetVaConfig(layer_norm_eps=0.001		)

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

						_SCREAMING_SNAKE_CASE	      =  re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$"""       ,_A		)
						if matches:
												_SCREAMING_SNAKE_CASE	      =  float(matches[1]		)
												_SCREAMING_SNAKE_CASE	      =  int(matches[2]		)

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						_SCREAMING_SNAKE_CASE	      =  10_01
						_SCREAMING_SNAKE_CASE	      =  "imagenet-1k-id2label.json"
						_SCREAMING_SNAKE_CASE	      =  "huggingface/label-files"
						_SCREAMING_SNAKE_CASE	      =  json.load(open(hf_hub_download(_A       ,_A       ,repo_type="""dataset"""		)       ,"""r"""		)		)
						_SCREAMING_SNAKE_CASE	      =  {int(_A		) + 1: v for k, v in idalabel.items()}
						_SCREAMING_SNAKE_CASE	      =  "background"
						_SCREAMING_SNAKE_CASE	      =  idalabel
						_SCREAMING_SNAKE_CASE	      =  {v: k for k, v in idalabel.items()}

						return config

def        __lowerCamelCase			(		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
						_SCREAMING_SNAKE_CASE	      =  Image.open(requests.get(_A       ,stream=_A		).raw		)
						return im

@torch.no_grad()
def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,snake_case__       ,snake_case__=False		)							->  Any:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  get_mobilenet_va_config(_A		)

						# Load 🤗 model
						_SCREAMING_SNAKE_CASE	      =  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
						_SCREAMING_SNAKE_CASE	      =  MobileNetVaImageProcessor(
						    crop_size={"""width""": config.image_size, """height""": config.image_size}       ,size={"""shortest_edge""": config.image_size + 32}       ,)
						_SCREAMING_SNAKE_CASE	      =  image_processor(images=prepare_img()       ,return_tensors="""pt"""		)
						_SCREAMING_SNAKE_CASE	      =  model(**_A		)
						_SCREAMING_SNAKE_CASE	      =  outputs.logits

						assert logits.shape == (1, 10_01)

						if model_name == "mobilenet_v1_1.0_224":
												_SCREAMING_SNAKE_CASE	      =  torch.tensor([-4.1_739, -1.1_233, 3.1_205]		)
						elif model_name == "mobilenet_v1_0.75_192":
												_SCREAMING_SNAKE_CASE	      =  torch.tensor([-3.9_440, -2.3_141, -0.3_333]		)
						else:
												_SCREAMING_SNAKE_CASE	      =  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..."""		)
												_SCREAMING_SNAKE_CASE	      =  "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 math
import random
from typing import Any

from .hill_climbing import SearchProblem

def       lowercase_		(		_A			:    Optional[int]    ,					_A			:    bool = True    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    bool = False    ,					_A			:    float = 100    ,					_A			:    float = 0.01    ,					_A			:    float = 1    ,					):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  False
      lowerCamelCase__		:				Optional[Any]    =  search_prob
      lowerCamelCase__		:				List[str]    =  start_temperate
      lowerCamelCase__		:				Optional[int]    =  []
      lowerCamelCase__		:				Any    =  0
      lowerCamelCase__		:				Optional[Any]    =  None

      while not search_end:
            lowerCamelCase__		:				Optional[Any]    =  current_state.score()
            if best_state is None or current_score > best_state.score():
                  lowerCamelCase__		:				List[Any]    =  current_state
            scores.append(_A						)
            iterations += 1
            lowerCamelCase__		:				List[Any]    =  None
            lowerCamelCase__		:				Optional[Any]    =  current_state.get_neighbors()
            while (
                next_state is None and neighbors
            ):  # till we do not find a neighbor that we can move to
                  lowerCamelCase__		:				Any    =  random.randint(0    ,					len(_A						) - 1						)  # picking a random neighbor
                  lowerCamelCase__		:				List[Any]    =  neighbors.pop(_A						)
                  lowerCamelCase__		:				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:
                        lowerCamelCase__		:				Dict    =  change * -1  # in case we are finding minimum
                  if change > 0:  # improves the solution
                        lowerCamelCase__		:				Optional[Any]    =  picked_neighbor
                  else:
                        lowerCamelCase__		:				str    =  (math.e) ** (
                            change / current_temp
                        )  # probability generation function
                        if random.random() < probability:  # random number within probability
                              lowerCamelCase__		:				Optional[int]    =  picked_neighbor
            lowerCamelCase__		:				Any    =  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
                  lowerCamelCase__		:				int    =  True
            else:
                  lowerCamelCase__		:				Optional[int]    =  next_state

      if visualization:
            from matplotlib import pyplot as plt

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


if __name__ == "__main__":

    def       lowercase_		(		_A			:    List[str]    ,					_A			:    int						):

          """simple docstring"""




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

    # starting the problem with initial coordinates (12, 47)
    A      :    Any		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    Dict		=					simulated_annealing(
        prob, find_max=False, max_x=100, min_x=5, max_y=50, 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)
    A      :    str		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    int		=					simulated_annealing(
        prob, find_max=True, max_x=100, min_x=5, max_y=50, 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       lowercase_		(		_A			:    int    ,					_A			:    Any						):

          """simple docstring"""




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

    A      :    Optional[int]		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    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()}'
    )

    A      :    Any		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Optional[int]		=					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()}'
    )


