

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


logging.set_verbosity_info()
lowerCamelCase				:   Tuple      				=  logging.get_logger(__name__)


def  _lowerCAmelCase      (		_UpperCamelCase		:  Dict       )   ->    int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=MobileNetVaConfig(layer_norm_eps=0.0_01       )

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

     _SCREAMING_SNAKE_CASE												=re.match(r'^.*mobilenet_v2_([^_]*)_([^_]*)$'							,				lowercase_       )
     if matches:
          _SCREAMING_SNAKE_CASE												=float(matches[1]       )
          _SCREAMING_SNAKE_CASE												=int(matches[2]       )

     if model_name.startswith('deeplabv3_'       ):
          _SCREAMING_SNAKE_CASE												=8
          _SCREAMING_SNAKE_CASE												=21
          _SCREAMING_SNAKE_CASE												='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".
          _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(lowercase_							,				lowercase_							,				repo_type='dataset'       )							,				'r'       )       )

     if config.num_labels == 10_01:
          _SCREAMING_SNAKE_CASE												={int(lowercase_       ) + 1: v for k, v in idalabel.items()}
          _SCREAMING_SNAKE_CASE												='background'
     else:
          _SCREAMING_SNAKE_CASE												={int(lowercase_       ): v for k, v in idalabel.items()}

     _SCREAMING_SNAKE_CASE												=idalabel
     _SCREAMING_SNAKE_CASE												={v: k for k, v in idalabel.items()}

     return config


def  _lowerCAmelCase      (		)   ->    List[str]:
     """simple docstring"""




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


@torch.no_grad()
def  _lowerCAmelCase      (		_UpperCamelCase		:  int							,				_UpperCamelCase		:  Any							,				_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  Union[str, Any]=False       )   ->    Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=get_mobilenet_va_config(lowercase_       )

     # Load 🤗 model
     if model_name.startswith('deeplabv3_'       ):
          _SCREAMING_SNAKE_CASE												=MobileNetVaForSemanticSegmentation(lowercase_       ).eval()
     else:
          _SCREAMING_SNAKE_CASE												=MobileNetVaForImageClassification(lowercase_       ).eval()

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

     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     _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(**lowercase_       )
     _SCREAMING_SNAKE_CASE												=outputs.logits

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

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               _SCREAMING_SNAKE_CASE												=torch.tensor(
                   [
                       [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                       [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
                       [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
                   ]       )

          else:
               raise ValueError(f"Unknown model name: {model_name}"       )

          assert torch.allclose(logits[0, :3, :3, :3]							,				lowercase_							,				atol=1E-4       )
     else:
          assert logits.shape == (1, 10_01)

          if model_name == "mobilenet_v2_1.4_224":
               _SCREAMING_SNAKE_CASE												=torch.tensor([0.01_81, -1.00_15, 0.46_88]       )
          elif model_name == "mobilenet_v2_1.0_224":
               _SCREAMING_SNAKE_CASE												=torch.tensor([0.24_45, -1.19_93, 0.19_05]       )
          elif model_name == "mobilenet_v2_0.75_160":
               _SCREAMING_SNAKE_CASE												=torch.tensor([0.24_82, 0.41_36, 0.66_69]       )
          elif model_name == "mobilenet_v2_0.35_96":
               _SCREAMING_SNAKE_CASE												=torch.tensor([0.14_51, -0.46_24, 0.71_92]       )
          else:
               _SCREAMING_SNAKE_CASE												=None

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

     Path(lowercase_       ).mkdir(exist_ok=lowercase_       )
     print(f"Saving model {model_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:
          print('Pushing to the hub...'       )
          _SCREAMING_SNAKE_CASE												='google/' + model_name
          image_processor.push_to_hub(lowercase_       )
          model.push_to_hub(lowercase_       )


if __name__ == "__main__":
    lowerCamelCase				:   Union[str, 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."
    )

    lowerCamelCase				:   str      				=  parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )



'''simple docstring'''
def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  Optional[Any]       )   ->    Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=len(_UpperCamelCase       )
     # Create a stack and push index of fist element to it
     _SCREAMING_SNAKE_CASE												=[]
     st.append(0       )

     # Span value of first element is always 1
     _SCREAMING_SNAKE_CASE												=1

     # Calculate span values for rest of the elements
     for i in range(1							,				_UpperCamelCase       ):
          # Pop elements from stack while stack is not
          # empty and top of stack is smaller than price[i]
          while len(_UpperCamelCase       ) > 0 and price[st[0]] <= price[i]:
               st.pop()

          # If stack becomes empty, then price[i] is greater
          # than all elements on left of it, i.e. price[0],
          # price[1], ..price[i-1]. Else the price[i]  is
          # greater than elements after top of stack
          _SCREAMING_SNAKE_CASE												=i + 1 if len(_UpperCamelCase       ) <= 0 else (i - st[0])

          # Push this element to stack
          st.append(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Any							,				_UpperCamelCase		:  str       )   ->    Any:
     """simple docstring"""




     for i in range(0							,				_UpperCamelCase       ):
          print(arr[i]							,				end=' '       )


# Driver program to test above function
lowerCamelCase				:   str      				=  [1_0, 4, 5, 9_0, 1_2_0, 8_0]
lowerCamelCase				:   str      				=  [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)







print_array(S, len(price))
