



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def 		UpperCamelCase     (       snake_case__	:   int     )     ->					Any:
   UpperCamelCase   :  Optional[int]						     =     SwinvaConfig()
   UpperCamelCase   :  str						     =     swinva_name.split('_'     )

   UpperCamelCase   :  Any						     =     name_split[1]
   if "to" in name_split[3]:
      UpperCamelCase   :  List[str]						     =     int(name_split[3][-3:]     )
   else:
      UpperCamelCase   :  List[str]						     =     int(name_split[3]     )
   if "to" in name_split[2]:
      UpperCamelCase   :  List[Any]						     =     int(name_split[2][-2:]     )
   else:
      UpperCamelCase   :  str						     =     int(name_split[2][6:]     )

   if model_size == "tiny":
      UpperCamelCase   :  Tuple						     =     96
      UpperCamelCase   :  str						     =     (2, 2, 6, 2)
      UpperCamelCase   :  List[str]						     =     (3, 6, 12, 24)
   elif model_size == "small":
      UpperCamelCase   :  Optional[Any]						     =     96
      UpperCamelCase   :  int						     =     (2, 2, 18, 2)
      UpperCamelCase   :  str						     =     (3, 6, 12, 24)
   elif model_size == "base":
      UpperCamelCase   :  int						     =     128
      UpperCamelCase   :  Optional[int]						     =     (2, 2, 18, 2)
      UpperCamelCase   :  int						     =     (4, 8, 16, 32)
   else:
      UpperCamelCase   :  Dict						     =     192
      UpperCamelCase   :  Any						     =     (2, 2, 18, 2)
      UpperCamelCase   :  str						     =     (6, 12, 24, 48)

   if "to" in swinva_name:
      UpperCamelCase   :  List[Any]						     =     (12, 12, 12, 6)

   if ("22k" in swinva_name) and ("to" not in swinva_name):
      UpperCamelCase   :  Union[str, Any]						     =     21841
      UpperCamelCase   :  List[Any]						     =     'huggingface/label-files'
      UpperCamelCase   :  str						     =     'imagenet-22k-id2label.json'
      UpperCamelCase   :  Union[str, Any]						     =     json.load(open(hf_hub_download(snake_case__	,					snake_case__	,					repo_type='dataset'     )	,					'r'     )     )
      UpperCamelCase   :  int						     =     {int(snake_case__     ): v for k, v in idalabel.items()}
      UpperCamelCase   :  Dict						     =     idalabel
      UpperCamelCase   :  Union[str, Any]						     =     {v: k for k, v in idalabel.items()}

   else:
      UpperCamelCase   :  Dict						     =     1000
      UpperCamelCase   :  str						     =     'huggingface/label-files'
      UpperCamelCase   :  Optional[Any]						     =     'imagenet-1k-id2label.json'
      UpperCamelCase   :  Any						     =     json.load(open(hf_hub_download(snake_case__	,					snake_case__	,					repo_type='dataset'     )	,					'r'     )     )
      UpperCamelCase   :  str						     =     {int(snake_case__     ): v for k, v in idalabel.items()}
      UpperCamelCase   :  Optional[Any]						     =     idalabel
      UpperCamelCase   :  Optional[int]						     =     {v: k for k, v in idalabel.items()}

   UpperCamelCase   :  Any						     =     img_size
   UpperCamelCase   :  Any						     =     num_classes
   UpperCamelCase   :  Optional[int]						     =     embed_dim
   UpperCamelCase   :  Optional[Any]						     =     depths
   UpperCamelCase   :  Optional[int]						     =     num_heads
   UpperCamelCase   :  Optional[Any]						     =     window_size

   return config
def 		UpperCamelCase     (       snake_case__	:   List[str]     )     ->					str:
   if "patch_embed.proj" in name:
      UpperCamelCase   :  Tuple						     =     name.replace('patch_embed.proj'	,					'embeddings.patch_embeddings.projection'     )
   if "patch_embed.norm" in name:
      UpperCamelCase   :  List[str]						     =     name.replace('patch_embed.norm'	,					'embeddings.norm'     )
   if "layers" in name:
      UpperCamelCase   :  List[Any]						     =     'encoder.' + name
   if "attn.proj" in name:
      UpperCamelCase   :  Union[str, Any]						     =     name.replace('attn.proj'	,					'attention.output.dense'     )
   if "attn" in name:
      UpperCamelCase   :  str						     =     name.replace('attn'	,					'attention.self'     )
   if "norm1" in name:
      UpperCamelCase   :  List[Any]						     =     name.replace('norm1'	,					'layernorm_before'     )
   if "norm2" in name:
      UpperCamelCase   :  Dict						     =     name.replace('norm2'	,					'layernorm_after'     )
   if "mlp.fc1" in name:
      UpperCamelCase   :  List[str]						     =     name.replace('mlp.fc1'	,					'intermediate.dense'     )
   if "mlp.fc2" in name:
      UpperCamelCase   :  str						     =     name.replace('mlp.fc2'	,					'output.dense'     )
   if "q_bias" in name:
      UpperCamelCase   :  int						     =     name.replace('q_bias'	,					'query.bias'     )
   if "k_bias" in name:
      UpperCamelCase   :  Tuple						     =     name.replace('k_bias'	,					'key.bias'     )
   if "v_bias" in name:
      UpperCamelCase   :  Optional[Any]						     =     name.replace('v_bias'	,					'value.bias'     )
   if "cpb_mlp" in name:
      UpperCamelCase   :  Optional[Any]						     =     name.replace('cpb_mlp'	,					'continuous_position_bias_mlp'     )
   if name == "norm.weight":
      UpperCamelCase   :  Optional[int]						     =     'layernorm.weight'
   if name == "norm.bias":
      UpperCamelCase   :  List[str]						     =     'layernorm.bias'

   if "head" in name:
      UpperCamelCase   :  Optional[Any]						     =     name.replace('head'	,					'classifier'     )
   else:
      UpperCamelCase   :  Optional[int]						     =     'swinv2.' + name

   return name
def 		UpperCamelCase     (       snake_case__	:   List[Any]	,					snake_case__	:   List[Any]     )     ->					List[Any]:
   for key in orig_state_dict.copy().keys():
      UpperCamelCase   :  Any						     =     orig_state_dict.pop(snake_case__     )

      if "mask" in key:
         continue
      elif "qkv" in key:
         UpperCamelCase   :  Tuple						     =     key.split('.'     )
         UpperCamelCase   :  Tuple						     =     int(key_split[1]     )
         UpperCamelCase   :  Any						     =     int(key_split[3]     )
         UpperCamelCase   :  Any						     =     model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            UpperCamelCase   :  List[str]						     =     val[:dim, :]
            UpperCamelCase   :  str						     =     val[dim : dim * 2, :]
            UpperCamelCase   :  str						     =     val[-dim:, :]
         else:
            UpperCamelCase   :  int						     =     val[:dim]
            UpperCamelCase   :  Any						     =     val[
                dim : dim * 2
            ]
            UpperCamelCase   :  Optional[int]						     =     val[-dim:]
      else:
         UpperCamelCase   :  Union[str, Any]						     =     val

   return orig_state_dict
def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   str     )     ->					List[Any]:
   UpperCamelCase   :  Dict						     =     timm.create_model(snake_case__	,					pretrained=snake_case__     )
   timm_model.eval()

   UpperCamelCase   :  int						     =     get_swinva_config(snake_case__     )
   UpperCamelCase   :  List[str]						     =     SwinvaForImageClassification(snake_case__     )
   model.eval()

   UpperCamelCase   :  Any						     =     convert_state_dict(timm_model.state_dict()	,					snake_case__     )
   model.load_state_dict(snake_case__     )

   UpperCamelCase   :  List[Any]						     =     'http://images.cocodataset.org/val2017/000000039769.jpg'

   UpperCamelCase   :  Dict						     =     AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'	,					'-'     )     )     )
   UpperCamelCase   :  Optional[Any]						     =     Image.open(requests.get(snake_case__	,					stream=snake_case__     ).raw     )
   UpperCamelCase   :  Tuple						     =     image_processor(images=snake_case__	,					return_tensors='pt'     )

   UpperCamelCase   :  Optional[Any]						     =     timm_model(inputs['pixel_values']     )
   UpperCamelCase   :  Union[str, Any]						     =     model(**snake_case__     ).logits

   assert torch.allclose(snake_case__	,					snake_case__	,					atol=1E-3     )

   print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""     )
   model.save_pretrained(snake_case__     )

   print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
   image_processor.save_pretrained(snake_case__     )

   model.push_to_hub(
       repo_path_or_name=Path(snake_case__	,					snake_case__     )	,					organization='nandwalritik'	,					commit_message='Add model'	,					)


if __name__ == "__main__":
    __UpperCAmelCase	       =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--swinv2_name''',
        default='''swinv2_tiny_patch4_window8_256''',
        type=str,
        help='''Name of the Swinv2 timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    __UpperCAmelCase	       =	parser.parse_args()
    convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)








import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch



class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Union[str, Any]       					=  JukeboxTokenizer
       UpperCAmelCase__    : Optional[int]       					=  {
           "artist": "Zac Brown Band",
           "genres": "Country",
           "lyrics": "I met a traveller from an antique land,\n        Who said \"Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ",
       }
       @require_torch
       def  snake_case_	(      self	)					->		Optional[Any]:
          import torch

          UpperCamelCase   :  Tuple						     =     JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics'	)
          UpperCamelCase   :  List[str]						     =     tokenizer(**self.metas	)['input_ids']
          # fmt: off
          UpperCamelCase   :  Dict						     =     [
              torch.tensor([[
                  0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
                  76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                  44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                  47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                  76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                  30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
                  27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
                  45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
                  41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                  19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                  76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                  76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                  64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                  30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                  27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                  34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                  27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                  41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                  76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                  44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                  76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                  32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                  40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                  20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                  45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                  31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                  45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                  76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                  34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                  31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                  40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                  38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                  76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                  41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                  27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                  46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                  76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                  41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                  46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                  41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                  40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                  27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                  76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                  76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                  41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                  76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                  27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                  34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                  44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                  40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                  46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                  38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                  40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                  27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                  20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                  76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                  76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                  76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                  76, 76]]	),
              torch.tensor([[0, 0, 0, 1069, 11]]	),
              torch.tensor([[0, 0, 0, 1069, 11]]	),
          ]
          # fmt: on
          self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]	)	)
          self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]	)	)
          self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]	)	)






       @require_torch
       def  snake_case_	(      self	)					->		Optional[Any]:
          import torch

          UpperCamelCase   :  str						     =     JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics'	)
          UpperCamelCase   :  Dict						     =     tokenizer(**self.metas	)['input_ids']
          # fmt: off
          UpperCamelCase   :  Optional[int]						     =     [
              torch.tensor([[
                  0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
                  31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                  31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                  40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                  79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                  77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                  27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                  37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                  32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                  77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                  77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                  77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                  46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                  77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                  77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                  77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                  77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                  64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                  40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                  40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                  38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                  31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                  41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                  77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                  46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                  41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                  31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                  31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                  23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                  44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                  31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                  38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                  40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                  27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                  31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                  34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                  31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                  1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                  31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                  45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                  31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                  15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                  11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                  45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                  41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                  44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                  46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                  27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                  35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                  77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                  31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                  41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                  77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                  40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                  77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                  77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                  27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                  77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                  77, 77, 77, 77, 77, 77]]	),
              torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]	),
              torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]	),
          ]
          # fmt: on
          self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]	)	)
          self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]	)	)
          self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]	)	)



