


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def    a__    (		snake_case__					:							str     ):
  if "cls_token" in name:
    _UpperCAmelCase		:							Dict							=     name.replace("""cls_token"""						,   """vit.embeddings.cls_token"""     )
  if "mask_token" in name:
    _UpperCAmelCase		:							Any							=     name.replace("""mask_token"""						,   """decoder.mask_token"""     )
  if "decoder_pos_embed" in name:
    _UpperCAmelCase		:							Optional[int]							=     name.replace("""decoder_pos_embed"""						,   """decoder.decoder_pos_embed"""     )
  if "pos_embed" in name and "decoder" not in name:
    _UpperCAmelCase		:							List[str]							=     name.replace("""pos_embed"""						,   """vit.embeddings.position_embeddings"""     )
  if "patch_embed.proj" in name:
    _UpperCAmelCase		:							Optional[int]							=     name.replace("""patch_embed.proj"""						,   """vit.embeddings.patch_embeddings.projection"""     )
  if "patch_embed.norm" in name:
    _UpperCAmelCase		:							Union[str, Any]							=     name.replace("""patch_embed.norm"""						,   """vit.embeddings.norm"""     )
  if "decoder_blocks" in name:
    _UpperCAmelCase		:							List[str]							=     name.replace("""decoder_blocks"""						,   """decoder.decoder_layers"""     )
  if "blocks" in name:
    _UpperCAmelCase		:							Optional[int]							=     name.replace("""blocks"""						,   """vit.encoder.layer"""     )
  if "attn.proj" in name:
    _UpperCAmelCase		:							Any							=     name.replace("""attn.proj"""						,   """attention.output.dense"""     )
  if "attn" in name:
    _UpperCAmelCase		:							Dict							=     name.replace("""attn"""						,   """attention.self"""     )
  if "norm1" in name:
    _UpperCAmelCase		:							Any							=     name.replace("""norm1"""						,   """layernorm_before"""     )
  if "norm2" in name:
    _UpperCAmelCase		:							Tuple							=     name.replace("""norm2"""						,   """layernorm_after"""     )
  if "mlp.fc1" in name:
    _UpperCAmelCase		:							Dict							=     name.replace("""mlp.fc1"""						,   """intermediate.dense"""     )
  if "mlp.fc2" in name:
    _UpperCAmelCase		:							int							=     name.replace("""mlp.fc2"""						,   """output.dense"""     )
  if "decoder_embed" in name:
    _UpperCAmelCase		:							str							=     name.replace("""decoder_embed"""						,   """decoder.decoder_embed"""     )
  if "decoder_norm" in name:
    _UpperCAmelCase		:							List[str]							=     name.replace("""decoder_norm"""						,   """decoder.decoder_norm"""     )
  if "decoder_pred" in name:
    _UpperCAmelCase		:							Dict							=     name.replace("""decoder_pred"""						,   """decoder.decoder_pred"""     )
  if "norm.weight" in name and "decoder" not in name:
    _UpperCAmelCase		:							Tuple							=     name.replace("""norm.weight"""						,   """vit.layernorm.weight"""     )
  if "norm.bias" in name and "decoder" not in name:
    _UpperCAmelCase		:							List[str]							=     name.replace("""norm.bias"""						,   """vit.layernorm.bias"""     )

  return name




def    a__    (		snake_case__					:							Optional[int]						,   snake_case__					:							str     ):
  for key in orig_state_dict.copy().keys():
    _UpperCAmelCase		:							Any							=     orig_state_dict.pop(snake_case__     )

    if "qkv" in key:
      _UpperCAmelCase		:							str							=     key.split("""."""     )
      _UpperCAmelCase		:							str							=     int(key_split[1]     )
      if "decoder_blocks" in key:
        _UpperCAmelCase		:							Any							=     config.decoder_hidden_size
        _UpperCAmelCase		:							Optional[Any]							=     """decoder.decoder_layers."""
        if "weight" in key:
          _UpperCAmelCase		:							Tuple							=     val[:dim, :]
          _UpperCAmelCase		:							str							=     val[dim : dim * 2, :]
          _UpperCAmelCase		:							Tuple							=     val[-dim:, :]
        elif "bias" in key:
          _UpperCAmelCase		:							List[Any]							=     val[:dim]
          _UpperCAmelCase		:							str							=     val[dim : dim * 2]
          _UpperCAmelCase		:							Optional[int]							=     val[-dim:]
      else:
        _UpperCAmelCase		:							Optional[int]							=     config.hidden_size
        _UpperCAmelCase		:							Optional[int]							=     """vit.encoder.layer."""
        if "weight" in key:
          _UpperCAmelCase		:							Tuple							=     val[:dim, :]
          _UpperCAmelCase		:							List[str]							=     val[dim : dim * 2, :]
          _UpperCAmelCase		:							Any							=     val[-dim:, :]
        elif "bias" in key:
          _UpperCAmelCase		:							List[Any]							=     val[:dim]
          _UpperCAmelCase		:							List[Any]							=     val[dim : dim * 2]
          _UpperCAmelCase		:							Any							=     val[-dim:]

    else:
      _UpperCAmelCase		:							str							=     val

  return orig_state_dict




def    a__    (		snake_case__					:							Union[str, Any]						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Any							=     ViTMAEConfig()
  if "large" in checkpoint_url:
    _UpperCAmelCase		:							Optional[int]							=     1024
    _UpperCAmelCase		:							str							=     4096
    _UpperCAmelCase		:							Any							=     24
    _UpperCAmelCase		:							Dict							=     16
  elif "huge" in checkpoint_url:
    _UpperCAmelCase		:							Optional[int]							=     14
    _UpperCAmelCase		:							Any							=     1280
    _UpperCAmelCase		:							Optional[Any]							=     5120
    _UpperCAmelCase		:							Optional[int]							=     32
    _UpperCAmelCase		:							str							=     16

  _UpperCAmelCase		:							Optional[Any]							=     ViTMAEForPreTraining(snake_case__     )

  _UpperCAmelCase		:							str							=     torch.hub.load_state_dict_from_url(snake_case__						,   map_location="""cpu"""     )["""model"""]

  _UpperCAmelCase		:							List[str]							=     ViTMAEImageProcessor(size=config.image_size     )

  _UpperCAmelCase		:							List[Any]							=     convert_state_dict(snake_case__						,   snake_case__     )

  model.load_state_dict(snake_case__     )
  model.eval()

  _UpperCAmelCase		:							Tuple							=     """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

  _UpperCAmelCase		:							List[Any]							=     Image.open(requests.get(snake_case__						,   stream=snake_case__     ).raw     )
  _UpperCAmelCase		:							List[Any]							=     ViTMAEImageProcessor(size=config.image_size     )
  _UpperCAmelCase		:							List[Any]							=     image_processor(images=snake_case__						,   return_tensors="""pt"""     )

  # forward pass
  torch.manual_seed(2     )
  _UpperCAmelCase		:							Optional[Any]							=     model(**snake_case__     )
  _UpperCAmelCase		:							Tuple							=     outputs.logits

  if "large" in checkpoint_url:
    _UpperCAmelCase		:							List[str]							=     torch.tensor(
        [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]     )
  elif "huge" in checkpoint_url:
    _UpperCAmelCase		:							Optional[Any]							=     torch.tensor(
        [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]     )
  else:
    _UpperCAmelCase		:							Dict							=     torch.tensor(
        [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]     )

  # verify logits
  assert torch.allclose(logits[0, :3, :3]						,   snake_case__						,   atol=1e-4     )

  print(f'''Saving model 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__     )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__					:     Optional[int]       =     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--checkpoint_url',
     default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
     type=str,
     help='URL of the checkpoint you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )

 SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     parser.parse_args()
 convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     '▁'

SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
class 		_SCREAMING_SNAKE_CASE     (						A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    BertGenerationTokenizer
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    True


     def        __snake_case(  self    ):
       super().setUp()

       _UpperCAmelCase		:							Optional[int]							=     BertGenerationTokenizer(A_							,					keep_accents=A_    )
       tokenizer.save_pretrained(self.tmpdirname    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     """<s>"""
       _UpperCAmelCase		:							Any							=     1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_    )							,					A_    )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_    )							,					A_    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     list(self.get_tokenizer().get_vocab().keys()    )

       self.assertEqual(vocab_keys[0]							,					"""<unk>"""    )
       self.assertEqual(vocab_keys[1]							,					"""<s>"""    )
       self.assertEqual(vocab_keys[-1]							,					"""<pad>"""    )
       self.assertEqual(len(A_    )							,					10_02    )


     def        __snake_case(  self    ):
       self.assertEqual(self.get_tokenizer().vocab_size							,					10_00    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     BertGenerationTokenizer(A_							,					keep_accents=A_    )

       _UpperCAmelCase		:							List[Any]							=     tokenizer.tokenize("""This is a test"""    )
       self.assertListEqual(A_							,					["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(A_    )							,					[2_85, 46, 10, 1_70, 3_82]							,					)

       _UpperCAmelCase		:							int							=     tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
       self.assertListEqual(
           A_							,					[
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """9""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """é""",
               """.""",
           ]							,					)
       _UpperCAmelCase		:							List[str]							=     tokenizer.convert_tokens_to_ids(A_    )
       self.assertListEqual(
           A_							,					[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]							,					)

       _UpperCAmelCase		:							Tuple							=     tokenizer.convert_ids_to_tokens(A_    )
       self.assertListEqual(
           A_							,					[
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """<unk>""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """<unk>""",
               """.""",
           ]							,					)


     @cached_property
     def        __snake_case(  self    ):
       return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""    )


     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     """Hello World!"""
       _UpperCAmelCase		:							str							=     [1_85_36, 22_60, 1_01]

       self.assertListEqual(A_							,					self.big_tokenizer.encode(A_    )    )


     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     (
           """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
           """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
       )
       _UpperCAmelCase		:							List[Any]							=     [
           8_71,
           4_19,
           3_58,
           9_46,
           9_91,
           25_21,
           4_52,
           3_58,
           13_57,
           3_87,
           77_51,
           35_36,
           1_12,
           9_85,
           4_56,
           1_26,
           8_65,
           9_38,
           54_00,
           57_34,
           4_58,
           13_68,
           4_67,
           7_86,
           24_62,
           52_46,
           11_59,
           6_33,
           8_65,
           45_19,
           4_57,
           5_82,
           8_52,
           25_57,
           4_27,
           9_16,
           5_08,
           4_05,
           3_43_24,
           4_97,
           3_91,
           4_08,
           1_13_42,
           12_44,
           3_85,
           1_00,
           9_38,
           9_85,
           4_56,
           5_74,
           3_62,
           1_25_97,
           32_00,
           31_29,
           11_72,
       ]

       self.assertListEqual(A_							,					self.big_tokenizer.encode(A_    )    )


     @require_torch
     @slow
     def        __snake_case(  self    ):
       import torch

       from transformers import BertGenerationConfig, BertGenerationEncoder

       # Build sequence
       _UpperCAmelCase		:							Optional[Any]							=     list(self.big_tokenizer.get_vocab().keys()    )[:10]
       _UpperCAmelCase		:							Tuple							=     """ """.join(A_    )
       _UpperCAmelCase		:							List[Any]							=     self.big_tokenizer.encode_plus(A_							,					return_tensors="""pt"""							,					return_token_type_ids=A_    )
       _UpperCAmelCase		:							Any							=     self.big_tokenizer.batch_encode_plus(
           [sequence + """ """ + sequence]							,					return_tensors="""pt"""							,					return_token_type_ids=A_    )

       _UpperCAmelCase		:							int							=     BertGenerationConfig()
       _UpperCAmelCase		:							Dict							=     BertGenerationEncoder(A_    )

       assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

       with torch.no_grad():
         model(**A_    )
         model(**A_    )




     @slow
     def        __snake_case(  self    ):
       # fmt: off
       _UpperCAmelCase		:							Optional[int]							=     {"""input_ids""": [[3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14], [4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_							,					model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""							,					revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""							,					)





