
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()
__SCREAMING_SNAKE_CASE    :   Tuple       =logging.get_logger(__name__)

def 			lowerCAmelCase_			(  lowerCamelCase:       Union[str, Any]						)					->			Optional[Any]:


      '''simple docstring'''




      lowerCamelCase__        =				MobileNetVaConfig(layer_norm_eps=0.001						)

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

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

      if model_name.startswith('deeplabv3_'						):
            lowerCamelCase__        =				8
            lowerCamelCase__        =				21
            lowerCamelCase__        =				'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".
            lowerCamelCase__        =				10_01
            lowerCamelCase__        =				'imagenet-1k-id2label.json'

      lowerCamelCase__        =				'huggingface/label-files'
      lowerCamelCase__        =				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				repo_type='dataset'						)     ,				'r'						)						)

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

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

      return config

def 			lowerCAmelCase_			(  )					->			Optional[int]:


      '''simple docstring'''




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

@torch.no_grad()
def 			lowerCAmelCase_			(  lowerCamelCase:       Dict     ,				lowerCamelCase:       List[Any]     ,				lowerCamelCase:       Union[str, Any]     ,				lowerCamelCase:       List[Any]=False						)					->			Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__        =				get_mobilenet_va_config(_SCREAMING_SNAKE_CASE						)

      # Load 🤗 model
      if model_name.startswith('deeplabv3_'						):
            lowerCamelCase__        =				MobileNetVaForSemanticSegmentation(_SCREAMING_SNAKE_CASE						).eval()
      else:
            lowerCamelCase__        =				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
      lowerCamelCase__        =				MobileNetVaImageProcessor(
          crop_size={'width': config.image_size, 'height': config.image_size}     ,				size={'shortest_edge': config.image_size + 32}     ,				)
      lowerCamelCase__        =				image_processor(images=prepare_img()     ,				return_tensors='pt'						)
      lowerCamelCase__        =				model(**_SCREAMING_SNAKE_CASE						)
      lowerCamelCase__        =				outputs.logits

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

            if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                  lowerCamelCase__        =				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]],
                          [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                          [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                      ]						)

            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, 10_01)

            if model_name == "mobilenet_v2_1.4_224":
                  lowerCamelCase__        =				torch.tensor([0.0181, -1.0015, 0.4688]						)
            elif model_name == "mobilenet_v2_1.0_224":
                  lowerCamelCase__        =				torch.tensor([0.2445, -1.1993, 0.1905]						)
            elif model_name == "mobilenet_v2_0.75_160":
                  lowerCamelCase__        =				torch.tensor([0.2482, 0.4136, 0.6669]						)
            elif model_name == "mobilenet_v2_0.35_96":
                  lowerCamelCase__        =				torch.tensor([0.1451, -0.4624, 0.7192]						)
            else:
                  lowerCamelCase__        =				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...'						)
            lowerCamelCase__        =				'google/' + model_name
            image_processor.push_to_hub(_SCREAMING_SNAKE_CASE						)
            model.push_to_hub(_SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE    :   Tuple       =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."""
      )

      __SCREAMING_SNAKE_CASE    :   Any       =parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )





import shutil

import requests

def 			lowerCAmelCase_			(  lowerCamelCase:       str     ,				lowerCamelCase:       bool = False     ,				lowerCamelCase:       str = "."						)					->			dict:


      '''simple docstring'''




      lowerCamelCase__        =				'https://api.nasa.gov/planetary/apod'
      return requests.get(lowerCamelCase     ,				params={'api_key': api_key}						).json()

def 			lowerCAmelCase_			(  lowerCamelCase:       str     ,				lowerCamelCase:       str = "."						)					->			dict:


      '''simple docstring'''




      lowerCamelCase__        =				get_apod_data(lowerCamelCase						)
      lowerCamelCase__        =				apod_data['url']
      lowerCamelCase__        =				img_url.split('/'						)[-1]
      lowerCamelCase__        =				requests.get(lowerCamelCase     ,				stream=lowerCamelCase						)

      with open(f"""{path}/{img_name}"""     ,				'wb+'						) as img_file:
            shutil.copyfileobj(response.raw     ,				lowerCamelCase						)
      del response
      return apod_data

def 			lowerCAmelCase_			(  lowerCamelCase:       str						)					->			dict:


      '''simple docstring'''




      lowerCamelCase__        =				'https://images-api.nasa.gov/search'
      return requests.get(lowerCamelCase     ,				params={'q': query}						).json()


if __name__ == "__main__":
      print(save_apod("""YOUR API KEY"""))
      __SCREAMING_SNAKE_CASE    :   Tuple       =get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
      print(apollo_aaaa_items[0]["""data"""][0]["""description"""])