





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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase			:  Optional[Any]										=				logging.get_logger(__name__)







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       Tuple:
  __snake_case:		List[Any]					    =		MobileNetVaConfig(layer_norm_eps=0.001)

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

  __snake_case:		List[Any]					    =		re.match(r"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""   ,   SCREAMING_SNAKE_CASE__)
  if matches:
    __snake_case:		List[Any]					    =		float(matches[1])
    __snake_case:		Dict					    =		int(matches[2])

  if model_name.startswith("""deeplabv3_"""):
    __snake_case:		List[Any]					    =		8
    __snake_case:		Optional[int]					    =		21
    __snake_case:		Union[str, Any]					    =		"""pascal-voc-id2label.json"""
  else:
    # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
    # of the usual 1000. The first class (index 0) is "background".
    __snake_case:		str					    =		1001
    __snake_case:		str					    =		"""imagenet-1k-id2label.json"""

  __snake_case:		Tuple					    =		"""huggingface/label-files"""
  __snake_case:		str					    =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   repo_type="""dataset""")   ,   """r"""))

  if config.num_labels == 1001:
    __snake_case:		str					    =		{int(SCREAMING_SNAKE_CASE__) + 1: v for k, v in idalabel.items()}
    __snake_case:		str					    =		"""background"""
  else:
    __snake_case:		List[Any]					    =		{int(SCREAMING_SNAKE_CASE__): v for k, v in idalabel.items()}

  __snake_case:		str					    =		idalabel
  __snake_case:		Union[str, Any]					    =		{v: k for k, v in idalabel.items()}

  return config







def      A__				(       )  ->       int:
  __snake_case:		Dict					    =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  __snake_case:		Tuple					    =		Image.open(requests.get(SCREAMING_SNAKE_CASE__   ,   stream=SCREAMING_SNAKE_CASE__).raw)
  return im







@torch.no_grad()
def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__=False)  ->       Tuple:
  __snake_case:		Any					    =		get_mobilenet_va_config(SCREAMING_SNAKE_CASE__)

  # Load 🤗 model
  if model_name.startswith("""deeplabv3_"""):
    __snake_case:		Any					    =		MobileNetVaForSemanticSegmentation(SCREAMING_SNAKE_CASE__).eval()
  else:
    __snake_case:		Optional[int]					    =		MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE__).eval()

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

  # Check outputs on an image, prepared by MobileNetV2ImageProcessor
  __snake_case:		List[str]					    =		MobileNetVaImageProcessor(
      crop_size={"""width""": config.image_size, """height""": config.image_size}   ,   size={"""shortest_edge""": config.image_size + 32}   ,   )
  __snake_case:		str					    =		image_processor(images=prepare_img()   ,   return_tensors="""pt""")
  __snake_case:		Optional[int]					    =		model(**SCREAMING_SNAKE_CASE__)
  __snake_case:		Optional[int]					    =		outputs.logits

  if model_name.startswith("""deeplabv3_"""):
    assert logits.shape == (1, 21, 65, 65)

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      __snake_case:		Dict					    =		torch.tensor(
          [
              [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
              [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
              [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
          ])

    else:
      raise ValueError(F'''Unknown model name: {model_name}''')

    assert torch.allclose(logits[0, :3, :3, :3]   ,   SCREAMING_SNAKE_CASE__   ,   atol=1e-4)
  else:
    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v2_1.4_224":
      __snake_case:		Union[str, Any]					    =		torch.tensor([0.0_181, -1.0_015, 0.4_688])
    elif model_name == "mobilenet_v2_1.0_224":
      __snake_case:		int					    =		torch.tensor([0.2_445, -1.1_993, 0.1_905])
    elif model_name == "mobilenet_v2_0.75_160":
      __snake_case:		Tuple					    =		torch.tensor([0.2_482, 0.4_136, 0.6_669])
    elif model_name == "mobilenet_v2_0.35_96":
      __snake_case:		Any					    =		torch.tensor([0.1_451, -0.4_624, 0.7_192])
    else:
      __snake_case:		Optional[int]					    =		None

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

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

  if push_to_hub:
    print("""Pushing to the hub...""")
    __snake_case:		Union[str, Any]					    =		"""google/""" + model_name
    image_processor.push_to_hub(SCREAMING_SNAKE_CASE__)
    model.push_to_hub(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      __UpperCAmelCase			:  Optional[Any]										=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default="mobilenet_v2_1.0_224",
          type=str,
          help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<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			:  str										=				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

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize







class        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	1
       lowerCAmelCase__ 						=	2
       lowerCAmelCase__ 						=	3




       def __repr__(    self					:       Optional[int]						):
         return f'''{self.__class__.__name__}.{self.name}'''







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       float:
  __snake_case    ,  __snake_case:		Union[str, Any]					    =		point
  return degrees(atana(y - miny   ,   x - minx))







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Direction:
  __snake_case    ,  __snake_case:		Dict					    =		starting
  __snake_case    ,  __snake_case:		List[str]					    =		via
  __snake_case    ,  __snake_case:		Optional[int]					    =		target
  __snake_case:		int					    =		degrees(atana(ya - ya   ,   xa - xa))
  via_angle %= 360
  __snake_case:		List[Any]					    =		degrees(atana(ya - ya   ,   xa - xa))
  target_angle %= 360
  # t-
  #  \ \
  #   \ v
  #    \|
  #     s
  # via_angle is always lower than target_angle, if direction is left.
  # If they are same, it means they are on a same line of convex hull.
  if target_angle > via_angle:
    return Direction.left
  elif target_angle == via_angle:
    return Direction.straight
  else:
    return Direction.right







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       list[tuple[int, int]]:

  if len(SCREAMING_SNAKE_CASE__) <= 2:
    # There is no convex hull
    raise ValueError("""graham_scan: argument must contain more than 3 points.""")
  if len(SCREAMING_SNAKE_CASE__) == 3:
    return points
  # find the lowest and the most left point
  __snake_case:		Optional[int]					    =		0
  __snake_case    ,  __snake_case:		Optional[int]					    =		maxsize, maxsize
  for i, point in enumerate(SCREAMING_SNAKE_CASE__):
    __snake_case:		Tuple					    =		point[0]
    __snake_case:		Optional[Any]					    =		point[1]
    if y < miny:
      __snake_case:		Optional[Any]					    =		y
      __snake_case:		Union[str, Any]					    =		x
      __snake_case:		Any					    =		i
    if y == miny and x < minx:
      __snake_case:		Optional[Any]					    =		x
      __snake_case:		str					    =		i

    # remove the lowest and the most left point from points for preparing for sort
  points.pop(SCREAMING_SNAKE_CASE__)

  __snake_case:		Optional[int]					    =		sorted(SCREAMING_SNAKE_CASE__   ,   key=lambda SCREAMING_SNAKE_CASE__: angle_comparer(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__))
  # This insert actually costs complexity,
  # and you should instead add (minx, miny) into stack later.
  # I'm using insert just for easy understanding.
  sorted_points.insert(0   ,   (minx, miny))

  __snake_case:		deque[tuple[int, int]]					    =		deque()
  stack.append(sorted_points[0])
  stack.append(sorted_points[1])
  stack.append(sorted_points[2])
  # In any ways, the first 3 points line are towards left.
  # Because we sort them the angle from minx, miny.
  __snake_case:		Optional[Any]					    =		Direction.left

  for i in range(3   ,   len(SCREAMING_SNAKE_CASE__)):
    while True:
      __snake_case:		Optional[Any]					    =		stack[-2]
      __snake_case:		List[Any]					    =		stack[-1]
      __snake_case:		int					    =		sorted_points[i]
      __snake_case:		Union[str, Any]					    =		check_direction(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

      if next_direction == Direction.left:
        __snake_case:		Dict					    =		Direction.left
        break
      if next_direction == Direction.straight:
        if current_direction == Direction.left:
          # We keep current_direction as left.
          # Because if the straight line keeps as straight,
          # we want to know if this straight line is towards left.
          break
        elif current_direction == Direction.right:
          # If the straight line is towards right,
          # every previous points on those straigh line is not convex hull.
          stack.pop()
      if next_direction == Direction.right:
        stack.pop()
    stack.append(sorted_points[i])
  return list(SCREAMING_SNAKE_CASE__)






