




"""simple docstring"""




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()
a_        =       logging.get_logger(__name__)







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Dict								=       MobileNetVaConfig(layer_norm_eps=0.001	)

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

			__lowercase			:  str								=       re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''	,				__UpperCamelCase	)
			if matches:
						__lowercase			:  Any								=       float(matches[1]	)
						__lowercase			:  Any								=       int(matches[2]	)

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

			return config







def      __UpperCAmelCase     ( ):
			__lowercase			:  Optional[int]								=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__lowercase			:  Tuple								=       Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase	).raw	)
			return im







@torch.no_grad()
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False	):
			__lowercase			:  List[Any]								=       get_mobilenet_va_config(__UpperCamelCase	)

			# Load 🤗 model
			__lowercase			:  Dict								=       MobileNetVaForImageClassification(__UpperCamelCase	).eval()

			# Load weights from TensorFlow checkpoint
			load_tf_weights_in_mobilenet_va(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)

			# Check outputs on an image, prepared by MobileNetV1ImageProcessor
			__lowercase			:  Optional[Any]								=       MobileNetVaImageProcessor(
			    crop_size={'''width''': config.image_size, '''height''': config.image_size}	,				size={'''shortest_edge''': config.image_size + 32}	,				)
			__lowercase			:  Tuple								=       image_processor(images=prepare_img()	,				return_tensors='''pt'''	)
			__lowercase			:  Union[str, Any]								=       model(**__UpperCamelCase	)
			__lowercase			:  Dict								=       outputs.logits

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

			if model_name == "mobilenet_v1_1.0_224":
						__lowercase			:  str								=       torch.tensor([-4.1_739, -1.1_233, 3.1_205]	)
			elif model_name == "mobilenet_v1_0.75_192":
						__lowercase			:  List[Any]								=       torch.tensor([-3.9_440, -2.3_141, -0.3_333]	)
			else:
						__lowercase			:  Optional[int]								=       None

			if expected_logits is not None:
						assert torch.allclose(logits[0, :3]	,				__UpperCamelCase	,				atol=1e-4	)

			Path(__UpperCamelCase	).mkdir(exist_ok=__UpperCamelCase	)
			print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""	)
			model.save_pretrained(__UpperCamelCase	)
			print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
			image_processor.save_pretrained(__UpperCamelCase	)

			if push_to_hub:
						print('''Pushing to the hub...'''	)
						__lowercase			:  Optional[int]								=       '''google/''' + model_name
						image_processor.push_to_hub(__UpperCamelCase	)
						model.push_to_hub(__UpperCamelCase	)


if __name__ == "__main__":
			a_        =       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.'
			)

			a_        =       parser.parse_args()
			convert_movilevit_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)





from __future__ import annotations

_A  =	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def   lowerCamelCase__							(							__lowerCAmelCase    :    list[list[int]]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    list[list[int]]       ,				):




			"""simple docstring"""


			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the reference grid
			lowerCAmelCase_         =      1
			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the action grid

			lowerCAmelCase_         =      init[0]
			lowerCAmelCase_         =      init[1]
			lowerCAmelCase_         =      0
			lowerCAmelCase_         =      g + heuristic[x][y]  # cost from starting cell to destination cell
			lowerCAmelCase_         =      [[f, g, x, y]]

			lowerCAmelCase_         =      False  # flag that is set when search is complete
			lowerCAmelCase_         =      False  # flag set if we can't find expand

			while not found and not resign:
						if len(__lowerCAmelCase							) == 0:
									raise ValueError("Algorithm is unable to find solution"							)
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									lowerCAmelCase_         =      cell.pop()
									lowerCAmelCase_         =      next_cell[2]
									lowerCAmelCase_         =      next_cell[3]
									lowerCAmelCase_         =      next_cell[1]

									if x == goal[0] and y == goal[1]:
												lowerCAmelCase_         =      True
									else:
												for i in range(len(__lowerCAmelCase							)							):  # to try out different valid actions
															lowerCAmelCase_         =      x + DIRECTIONS[i][0]
															lowerCAmelCase_         =      y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(__lowerCAmelCase							) and ya >= 0 and ya < len(grid[0]							):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					lowerCAmelCase_         =      g + cost
																					lowerCAmelCase_         =      ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya]							)
																					lowerCAmelCase_         =      1
																					lowerCAmelCase_         =      i
			lowerCAmelCase_         =      []
			lowerCAmelCase_         =      goal[0]
			lowerCAmelCase_         =      goal[1]
			invpath.append([x, y]							)  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						lowerCAmelCase_         =      x - DIRECTIONS[action[x][y]][0]
						lowerCAmelCase_         =      y - DIRECTIONS[action[x][y]][1]
						lowerCAmelCase_         =      xa
						lowerCAmelCase_         =      ya
						invpath.append([x, y]							)

			lowerCAmelCase_         =      []
			for i in range(len(__lowerCAmelCase							)							):
						path.append(invpath[len(__lowerCAmelCase							) - 1 - i]							)
			return path, action


if __name__ == "__main__":
			_A  =	[
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 1, 0],
			    [0, 0, 0, 0, 1, 0],
			]

			_A  =	[0, 0]
			# all coordinates are given in format [y,x]
			_A  =	[len(grid) - 1, len(grid[0]) - 1]
			_A  =	1

			# the cost map which pushes the path closer to the goal
			_A  =	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
			for i in range(len(grid)):
						for j in range(len(grid[0])):
									_A  =	abs(i - goal[0]) + abs(j - goal[1])
									if grid[i][j] == 1:
												# added extra penalty in the heuristic map
												_A  =	99

			_A, _A  =	search(grid, init, goal, cost, heuristic)

			print("ACTION MAP")
			for i in range(len(action)):
						print(action[i])

			for i in range(len(path)):
						print(path[i])



