
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()
_A							=       logging.get_logger(__name__)




def 					lowerCamelCase__					(    a__	:	Optional[int]  )		->						Any:
       UpperCamelCase_											=							MobileNetVaConfig(layer_norm_eps=0.001  )

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

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

       if model_name.startswith("""deeplabv3_"""  ):
              UpperCamelCase_											=							8
              UpperCamelCase_											=							21
              UpperCamelCase_											=							"""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".
              UpperCamelCase_											=							1001
              UpperCamelCase_											=							"""imagenet-1k-id2label.json"""

       UpperCamelCase_											=							"""huggingface/label-files"""
       UpperCamelCase_											=							json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__						,   SCREAMING_SNAKE_CASE__						,   repo_type="""dataset"""  )						,   """r"""  )  )

       if config.num_labels == 1001:
              UpperCamelCase_											=							{int(SCREAMING_SNAKE_CASE__  ) + 1: v for k, v in idalabel.items()}
              UpperCamelCase_											=							"""background"""
       else:
              UpperCamelCase_											=							{int(SCREAMING_SNAKE_CASE__  ): v for k, v in idalabel.items()}

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

       return config




def 					lowerCamelCase__					(    )		->						List[str]:
       UpperCamelCase_											=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       UpperCamelCase_											=							Image.open(requests.get(SCREAMING_SNAKE_CASE__						,   stream=SCREAMING_SNAKE_CASE__  ).raw  )
       return im




@torch.no_grad()
def 					lowerCamelCase__					(    a__	:	Any						,   a__	:	Union[str, Any]						,   a__	:	Tuple						,   a__	:	int=False  )		->						List[Any]:
       UpperCamelCase_											=							get_mobilenet_va_config(SCREAMING_SNAKE_CASE__  )

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

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

              if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                     UpperCamelCase_											=							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":
                     UpperCamelCase_											=							torch.tensor([0.0_181, -1.0_015, 0.4_688]  )
              elif model_name == "mobilenet_v2_1.0_224":
                     UpperCamelCase_											=							torch.tensor([0.2_445, -1.1_993, 0.1_905]  )
              elif model_name == "mobilenet_v2_0.75_160":
                     UpperCamelCase_											=							torch.tensor([0.2_482, 0.4_136, 0.6_669]  )
              elif model_name == "mobilenet_v2_0.35_96":
                     UpperCamelCase_											=							torch.tensor([0.1_451, -0.4_624, 0.7_192]  )
              else:
                     UpperCamelCase_											=							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..."""  )
              UpperCamelCase_											=							"""google/""" + model_name
              image_processor.push_to_hub(SCREAMING_SNAKE_CASE__  )
              model.push_to_hub(SCREAMING_SNAKE_CASE__  )


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

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








import bsa
import requests







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						return {
						    "name": soup.ha.a.text,
						    "genre": soup.find("""span"""			,class_="""genre"""  ).text.strip(),
						    "rating": soup.strong.text,
						    "page_link": F"""https://www.imdb.com{soup.a.get('href'  )}""",
						}







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__ = 5  ):


						'''simple docstring'''




						lowerCAmelCase	:   Dict					     =					int(float(SCREAMING_SNAKE_CASE__  )  )
						if num_movies < 1:
												return ()
						lowerCAmelCase	:   List[str]					     =					(
						    """https://www.imdb.com/search/title?title_type="""
						    F"""feature&sort=num_votes,desc&count={num_movies}"""
						)
						lowerCAmelCase	:   Optional[Any]					     =					bsa.BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__  ).content			,"""html.parser"""  )
						return tuple(
						    get_movie_data_from_soup(SCREAMING_SNAKE_CASE__  )
						    for movie in source.find_all("""div"""			,class_="""lister-item mode-advanced"""  )  )


if __name__ == "__main__":
		import json

		lowerCAmelCase					:				Union[str, Any]   	=int(input('How many movies would you like to see? '))
		print(
		    ', '.join(
		        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
		    )
		)

