

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


__UpperCAmelCase								=			"""▁"""

__UpperCAmelCase								=			get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
class SCREAMING_SNAKE_CASE     (  a__       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[str]										=BertGenerationTokenizer
  lowerCamelCase   :      str										=False
  lowerCamelCase   :      str										=True







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Union[str, Any]:






     """simple docstring"""







     super().setUp()

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						'''<s>'''
     __lowerCAmelCase  :      Tuple    =						1

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						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(_lowerCamelCase   )	,							10_02   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Any:






     """simple docstring"""







     self.assertEqual(self.get_tokenizer().vocab_size	,							10_00   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						BertGenerationTokenizer(_lowerCamelCase	,							keep_accents=_lowerCamelCase   )

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

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

     __lowerCAmelCase  :      Any    =						tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
     self.assertListEqual(
         _lowerCamelCase	,							[
             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""",
             """.""",
         ]	,							)
     __lowerCAmelCase  :      List[Any]    =						tokenizer.convert_tokens_to_ids(_lowerCamelCase   )
     self.assertListEqual(
         _lowerCamelCase	,							[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]	,							)

     __lowerCAmelCase  :      List[Any]    =						tokenizer.convert_ids_to_tokens(_lowerCamelCase   )
     self.assertListEqual(
         _lowerCamelCase	,							[
             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 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[Any]:






     """simple docstring"""







     return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						'''Hello World!'''
     __lowerCAmelCase  :      Any    =						[1_85_36, 22_60, 1_01]

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







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						(
         '''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'''
     )
     __lowerCAmelCase  :      List[str]    =						[
         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(_lowerCamelCase	,							self.big_tokenizer.encode(_lowerCamelCase   )   )







  @require_torch
  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     import torch

     from transformers import BertGenerationConfig, BertGenerationEncoder

     # Build sequence
     __lowerCAmelCase  :      Union[str, Any]    =						list(self.big_tokenizer.get_vocab().keys()   )[:10]
     __lowerCAmelCase  :      Dict    =						''' '''.join(_lowerCamelCase   )
     __lowerCAmelCase  :      List[Any]    =						self.big_tokenizer.encode_plus(_lowerCamelCase	,							return_tensors="""pt"""	,							return_token_type_ids=_lowerCamelCase   )
     __lowerCAmelCase  :      Optional[int]    =						self.big_tokenizer.batch_encode_plus(
         [sequence + """ """ + sequence]	,							return_tensors="""pt"""	,							return_token_type_ids=_lowerCamelCase   )

     __lowerCAmelCase  :      List[Any]    =						BertGenerationConfig()
     __lowerCAmelCase  :      List[Any]    =						BertGenerationEncoder(_lowerCamelCase   )

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

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







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						{'''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=_lowerCamelCase	,							model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""	,							revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""	,							)






import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def      snake_case_				(__A					:						Optional[Any]					)      ->     Tuple:
   __lowerCAmelCase  :      Optional[int]    =						SwinConfig()
   __lowerCAmelCase  :      List[Any]    =						swin_name.split("""_"""					)

   __lowerCAmelCase  :      Dict    =						name_split[1]
   __lowerCAmelCase  :      Optional[Any]    =						int(name_split[4]					)
   __lowerCAmelCase  :      List[Any]    =						int(name_split[3][-1]					)

   if model_size == "tiny":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      List[Any]    =						(2, 2, 6, 2)
      __lowerCAmelCase  :      Optional[Any]    =						(3, 6, 1_2, 2_4)
   elif model_size == "small":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      Optional[int]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      Optional[int]    =						(3, 6, 1_2, 2_4)
   elif model_size == "base":
      __lowerCAmelCase  :      List[Any]    =						1_2_8
      __lowerCAmelCase  :      Tuple    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(4, 8, 1_6, 3_2)
   else:
      __lowerCAmelCase  :      List[Any]    =						1_9_2
      __lowerCAmelCase  :      List[str]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(6, 1_2, 2_4, 4_8)

   if "in22k" in swin_name:
      __lowerCAmelCase  :      Dict    =						2_1_8_4_1
   else:
      __lowerCAmelCase  :      Optional[Any]    =						1_0_0_0
      __lowerCAmelCase  :      Union[str, Any]    =						"""huggingface/label-files"""
      __lowerCAmelCase  :      Any    =						"""imagenet-1k-id2label.json"""
      __lowerCAmelCase  :      Any    =						json.load(open(hf_hub_download(__A ,       __A ,       repo_type="""dataset"""					) ,       """r"""					)					)
      __lowerCAmelCase  :      int    =						{int(__A					): v for k, v in idalabel.items()}
      __lowerCAmelCase  :      str    =						idalabel
      __lowerCAmelCase  :      int    =						{v: k for k, v in idalabel.items()}

   __lowerCAmelCase  :      Optional[Any]    =						img_size
   __lowerCAmelCase  :      Optional[Any]    =						num_classes
   __lowerCAmelCase  :      Tuple    =						embed_dim
   __lowerCAmelCase  :      Union[str, Any]    =						depths
   __lowerCAmelCase  :      Optional[Any]    =						num_heads
   __lowerCAmelCase  :      Tuple    =						window_size

   return config





def      snake_case_				(__A					:						int					)      ->     Optional[Any]:
   if "patch_embed.proj" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""patch_embed.proj""" ,       """embeddings.patch_embeddings.projection"""					)
   if "patch_embed.norm" in name:
      __lowerCAmelCase  :      List[Any]    =						name.replace("""patch_embed.norm""" ,       """embeddings.norm"""					)
   if "layers" in name:
      __lowerCAmelCase  :      int    =						"""encoder.""" + name
   if "attn.proj" in name:
      __lowerCAmelCase  :      Tuple    =						name.replace("""attn.proj""" ,       """attention.output.dense"""					)
   if "attn" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""attn""" ,       """attention.self"""					)
   if "norm1" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm1""" ,       """layernorm_before"""					)
   if "norm2" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm2""" ,       """layernorm_after"""					)
   if "mlp.fc1" in name:
      __lowerCAmelCase  :      List[str]    =						name.replace("""mlp.fc1""" ,       """intermediate.dense"""					)
   if "mlp.fc2" in name:
      __lowerCAmelCase  :      str    =						name.replace("""mlp.fc2""" ,       """output.dense"""					)

   if name == "norm.weight":
      __lowerCAmelCase  :      Dict    =						"""layernorm.weight"""
   if name == "norm.bias":
      __lowerCAmelCase  :      Optional[int]    =						"""layernorm.bias"""

   if "head" in name:
      __lowerCAmelCase  :      int    =						name.replace("""head""" ,       """classifier"""					)
   else:
      __lowerCAmelCase  :      List[str]    =						"""swin.""" + name

   return name





def      snake_case_				(__A					:						List[Any] ,       __A					:						str					)      ->     int:
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase  :      Tuple    =						orig_state_dict.pop(__A					)

      if "mask" in key:
         continue
      elif "qkv" in key:
         __lowerCAmelCase  :      Any    =						key.split("""."""					)
         __lowerCAmelCase  :      Union[str, Any]    =						int(key_split[1]					)
         __lowerCAmelCase  :      Optional[Any]    =						int(key_split[3]					)
         __lowerCAmelCase  :      Optional[int]    =						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

   return orig_state_dict





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      List[Any]    =						timm.create_model(__A ,       pretrained=__A					)
   timm_model.eval()

   __lowerCAmelCase  :      str    =						get_swin_config(__A					)
   __lowerCAmelCase  :      Any    =						SwinForImageClassification(__A					)
   model.eval()

   __lowerCAmelCase  :      str    =						convert_state_dict(timm_model.state_dict() ,       __A					)
   model.load_state_dict(__A					)

   __lowerCAmelCase  :      str    =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""

   __lowerCAmelCase  :      Any    =						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" ,       """-"""					)					)					)
   __lowerCAmelCase  :      List[Any]    =						Image.open(requests.get(__A ,       stream=__A					).raw					)
   __lowerCAmelCase  :      List[str]    =						image_processor(images=__A ,       return_tensors="""pt"""					)

   __lowerCAmelCase  :      Tuple    =						timm_model(inputs["""pixel_values"""]					)
   __lowerCAmelCase  :      Dict    =						model(**__A					).logits

   assert torch.allclose(__A ,       __A ,       atol=1e-3					)

   print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''					)
   model.save_pretrained(__A					)

   print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
   image_processor.save_pretrained(__A					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--swin_name""",
       default="""swin_tiny_patch4_window7_224""",
       type=str,
       help="""Name of the Swin 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_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


