





"""simple docstring"""





import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__					=   logging.get_logger()


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						LevitConfig							,						SCREAMING_SNAKE_CASE     :						Path							,						SCREAMING_SNAKE_CASE     :						bool = True			):







      '''simple docstring'''




      print(F'Converting {name}...'			)

      with torch.no_grad():
            if hidden_sizes == 1_28:
                  if name[-1] == "S":
                        lowerCAmelCase  						=       timm.create_model("""levit_128s"""							,						pretrained=SCREAMING_SNAKE_CASE			)
                  else:
                        lowerCAmelCase  						=       timm.create_model("""levit_128"""							,						pretrained=SCREAMING_SNAKE_CASE			)
            if hidden_sizes == 1_92:
                  lowerCAmelCase  						=       timm.create_model("""levit_192"""							,						pretrained=SCREAMING_SNAKE_CASE			)
            if hidden_sizes == 2_56:
                  lowerCAmelCase  						=       timm.create_model("""levit_256"""							,						pretrained=SCREAMING_SNAKE_CASE			)
            if hidden_sizes == 3_84:
                  lowerCAmelCase  						=       timm.create_model("""levit_384"""							,						pretrained=SCREAMING_SNAKE_CASE			)

            from_model.eval()
            lowerCAmelCase  						=       LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE			).eval()
            lowerCAmelCase  						=       OrderedDict()

            lowerCAmelCase  						=       from_model.state_dict()
            lowerCAmelCase  						=       list(from_model.state_dict().keys()			)
            lowerCAmelCase  						=       list(our_model.state_dict().keys()			)
            print(len(SCREAMING_SNAKE_CASE			)							,						len(SCREAMING_SNAKE_CASE			)			)
            for i in range(len(SCREAMING_SNAKE_CASE			)			):
                  lowerCAmelCase  						=       weights[og_keys[i]]
            our_model.load_state_dict(SCREAMING_SNAKE_CASE			)

            lowerCAmelCase  						=       torch.randn((2, 3, 2_24, 2_24)			)
            lowerCAmelCase  						=       from_model(SCREAMING_SNAKE_CASE			)
            lowerCAmelCase  						=       our_model(SCREAMING_SNAKE_CASE			).logits

      assert torch.allclose(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			), "The model logits don't match the original one."

      lowerCAmelCase  						=       name
      print(SCREAMING_SNAKE_CASE			)

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name			)
            lowerCAmelCase  						=       LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name			)

            print(F'Pushed {checkpoint_name}'			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Path							,						SCREAMING_SNAKE_CASE     :						str = None							,						SCREAMING_SNAKE_CASE     :						bool = True			):







      '''simple docstring'''




      lowerCAmelCase  						=       """imagenet-1k-id2label.json"""
      lowerCAmelCase  						=       10_00
      lowerCAmelCase  						=       (1, num_labels)

      lowerCAmelCase  						=       """huggingface/label-files"""
      lowerCAmelCase  						=       num_labels
      lowerCAmelCase  						=       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						repo_type="""dataset"""			)							,						"""r"""			)			)
      lowerCAmelCase  						=       {int(SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}

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

      lowerCAmelCase  						=       partial(SCREAMING_SNAKE_CASE							,						num_labels=SCREAMING_SNAKE_CASE							,						idalabel=SCREAMING_SNAKE_CASE							,						labelaid=SCREAMING_SNAKE_CASE			)

      lowerCAmelCase  						=       {
          """levit-128S""": 1_28,
          """levit-128""": 1_28,
          """levit-192""": 1_92,
          """levit-256""": 2_56,
          """levit-384""": 3_84,
      }

      lowerCAmelCase  						=       {
          """levit-128S""": ImageNetPreTrainedConfig(
              hidden_sizes=[1_28, 2_56, 3_84]							,						num_attention_heads=[4, 6, 8]							,						depths=[2, 3, 4]							,						key_dim=[16, 16, 16]							,						drop_path_rate=0							,						),
          """levit-128""": ImageNetPreTrainedConfig(
              hidden_sizes=[1_28, 2_56, 3_84]							,						num_attention_heads=[4, 8, 12]							,						depths=[4, 4, 4]							,						key_dim=[16, 16, 16]							,						drop_path_rate=0							,						),
          """levit-192""": ImageNetPreTrainedConfig(
              hidden_sizes=[1_92, 2_88, 3_84]							,						num_attention_heads=[3, 5, 6]							,						depths=[4, 4, 4]							,						key_dim=[32, 32, 32]							,						drop_path_rate=0							,						),
          """levit-256""": ImageNetPreTrainedConfig(
              hidden_sizes=[2_56, 3_84, 5_12]							,						num_attention_heads=[4, 6, 8]							,						depths=[4, 4, 4]							,						key_dim=[32, 32, 32]							,						drop_path_rate=0							,						),
          """levit-384""": ImageNetPreTrainedConfig(
              hidden_sizes=[3_84, 5_12, 7_68]							,						num_attention_heads=[6, 9, 12]							,						depths=[4, 4, 4]							,						key_dim=[32, 32, 32]							,						drop_path_rate=0.1							,						),
      }

      if model_name:
            convert_weight_and_push(
                names_to_hidden_sizes[model_name]							,						SCREAMING_SNAKE_CASE							,						names_to_config[model_name]							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      return config, expected_shape


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default=None,
           type=str,
           help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default="levit-dump-folder/",
           type=Path,
           required=False,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
       parser.add_argument(
           "--no-push_to_hub",
           dest="push_to_hub",
           action="store_false",
           help="Do not push model and image processor to the hub",
       )

       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       SCREAMING_SNAKE_CASE__					=   args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)













"""simple docstring"""





SCREAMING_SNAKE_CASE__					=   {
    "A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
    "H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
    "O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
    "V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
    "2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
    "8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
    ":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
    "?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
    "(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
SCREAMING_SNAKE_CASE__					=   {value: key for key, value in MORSE_CODE_DICT.items()}


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return " ".join(MORSE_CODE_DICT[char] for char in message.upper()			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return "".join(REVERSE_DICT[char] for char in message.split()			)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       """Morse code here!"""
      print(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       encrypt(SCREAMING_SNAKE_CASE			)
      print(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       decrypt(SCREAMING_SNAKE_CASE			)
      print(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
       main()






