


"""simple docstring"""



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case       =       get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")







@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase							( snake_case_       ,				unittest.TestCase      ):
   __UpperCAmelCase					:			Optional[int]				    =    PegasusTokenizer
   __UpperCAmelCase					:			Optional[int]				    =    PegasusTokenizerFast
   __UpperCAmelCase					:			Tuple				    =    True
   __UpperCAmelCase					:			Tuple				    =    True







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       super().setUp()

       # We have a SentencePiece fixture for testing
       snake_case       : Tuple    =       PegasusTokenizer(UpperCamelCase__		)
       tokenizer.save_pretrained(self.tmpdirname		)







   @cached_property
   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       return PegasusTokenizer.from_pretrained("google/pegasus-large"		)







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						PegasusTokenizer:

       '''simple docstring'''




       return PegasusTokenizer.from_pretrained(self.tmpdirname		,      **UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       return ("This is a test", "This is a test")







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

       '''simple docstring'''




       snake_case       : Dict    =       "</s>"
       snake_case       : Optional[Any]    =       1

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







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

       '''simple docstring'''




       snake_case       : List[str]    =       list(self.get_tokenizer().get_vocab().keys()		)

       self.assertEqual(vocab_keys[0]		,      "<pad>"		)
       self.assertEqual(vocab_keys[1]		,      "</s>"		)
       self.assertEqual(vocab_keys[-1]		,      "v"		)
       self.assertEqual(len(UpperCamelCase__		)		,      1103		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       self.assertEqual(self.get_tokenizer().vocab_size		,      1103		)







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
       snake_case       : Union[str, Any]    =       self.tokenizer_class.from_pretrained(self.tmpdirname		)
       snake_case       : int    =       (
           "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
           " </s> <pad> <pad> <pad>"
       )
       snake_case       : List[Any]    =       rust_tokenizer([raw_input_str]		,      return_tensors=UpperCamelCase__		,      add_special_tokens=UpperCamelCase__		).input_ids[0]
       snake_case       : Union[str, Any]    =       py_tokenizer([raw_input_str]		,      return_tensors=UpperCamelCase__		,      add_special_tokens=UpperCamelCase__		).input_ids[0]
       self.assertListEqual(UpperCamelCase__		,      UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       snake_case       : List[str]    =       "<mask_1> To ensure a <mask_2> flow of bank resolutions."
       snake_case       : Any    =       [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
       snake_case       : Optional[Any]    =       tokenizer([raw_input_str]		,      return_tensors=UpperCamelCase__		).input_ids[0]
       self.assertListEqual(UpperCamelCase__		,      UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 9_6103
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 103
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 1024
       snake_case       : List[Any]    =       "To ensure a smooth flow of bank resolutions."
       snake_case       : Optional[Any]    =       [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
       snake_case       : Optional[Any]    =       tokenizer([raw_input_str]		,      return_tensors=UpperCamelCase__		).input_ids[0]
       self.assertListEqual(UpperCamelCase__		,      UpperCamelCase__		)
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







   @require_torch
   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       snake_case       : Dict    =       ["This is going to be way too long." * 150, "short example"]
       snake_case       : int    =       ["not super long but more than 5 tokens", "tiny"]
       snake_case       : Union[str, Any]    =       self._large_tokenizer(UpperCamelCase__		,      padding=UpperCamelCase__		,      truncation=UpperCamelCase__		,      return_tensors="pt"		)
       snake_case       : List[str]    =       self._large_tokenizer(
           text_target=UpperCamelCase__		,      max_length=5		,      padding=UpperCamelCase__		,      truncation=UpperCamelCase__		,      return_tensors="pt"		)

       assert batch.input_ids.shape == (2, 1024)
       assert batch.attention_mask.shape == (2, 1024)
       assert targets["input_ids"].shape == (2, 5)
       assert len(UpperCamelCase__		) == 2  # input_ids, attention_mask.







   @slow
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       {"input_ids": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 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]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=UpperCamelCase__		,      model_name="google/bigbird-pegasus-large-arxiv"		,      revision="ba85d0851d708441f91440d509690f1ab6353415"		,      )







@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase							( snake_case_       ,				unittest.TestCase      ):
   __UpperCAmelCase					:			int				    =    PegasusTokenizer
   __UpperCAmelCase					:			List[Any]				    =    PegasusTokenizerFast
   __UpperCAmelCase					:			Dict				    =    True
   __UpperCAmelCase					:			Union[str, Any]				    =    True







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       super().setUp()

       # We have a SentencePiece fixture for testing
       snake_case       : List[Any]    =       PegasusTokenizer(UpperCamelCase__		,      offset=0		,      mask_token_sent=UpperCamelCase__		,      mask_token="[MASK]"		)
       tokenizer.save_pretrained(self.tmpdirname		)







   @cached_property
   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"		)







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						PegasusTokenizer:

       '''simple docstring'''




       return PegasusTokenizer.from_pretrained(self.tmpdirname		,      **UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Dict:

       '''simple docstring'''




       return ("This is a test", "This is a test")







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : str    =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
       snake_case       : List[Any]    =       self.tokenizer_class.from_pretrained(self.tmpdirname		)
       snake_case       : Tuple    =       (
           "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
           " <pad> <pad> <pad>"
       )
       snake_case       : List[Any]    =       rust_tokenizer([raw_input_str]		,      return_tensors=UpperCamelCase__		,      add_special_tokens=UpperCamelCase__		).input_ids[0]
       snake_case       : Optional[Any]    =       py_tokenizer([raw_input_str]		,      return_tensors=UpperCamelCase__		,      add_special_tokens=UpperCamelCase__		).input_ids[0]
       self.assertListEqual(UpperCamelCase__		,      UpperCamelCase__		)







   @require_torch
   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : int    =       ["This is going to be way too long." * 1000, "short example"]
       snake_case       : int    =       ["not super long but more than 5 tokens", "tiny"]
       snake_case       : int    =       self._large_tokenizer(UpperCamelCase__		,      padding=UpperCamelCase__		,      truncation=UpperCamelCase__		,      return_tensors="pt"		)
       snake_case       : Any    =       self._large_tokenizer(
           text_target=UpperCamelCase__		,      max_length=5		,      padding=UpperCamelCase__		,      truncation=UpperCamelCase__		,      return_tensors="pt"		)

       assert batch.input_ids.shape == (2, 4096)
       assert batch.attention_mask.shape == (2, 4096)
       assert targets["input_ids"].shape == (2, 5)
       assert len(UpperCamelCase__		) == 2  # input_ids, attention_mask.







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Tuple    =       (
           "This is an example string that is used to test the original TF implementation against the HF"
           " implementation"
       )

       snake_case       : Optional[int]    =       self._large_tokenizer(UpperCamelCase__		).input_ids

       self.assertListEqual(
           UpperCamelCase__		,      [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]		,      )









"""simple docstring"""



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     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Tuple:


    """simple docstring"""





    snake_case       : Optional[Any]    =       SwinConfig()
    snake_case       : Union[str, Any]    =       swin_name.split("_"					)

    snake_case       : str    =       name_split[1]
    snake_case       : Tuple    =       int(name_split[4]					)
    snake_case       : str    =       int(name_split[3][-1]					)

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

    if "in22k" in swin_name:
        snake_case       : List[Any]    =       2_1841
    else:
        snake_case       : str    =       1000
        snake_case       : Any    =       "huggingface/label-files"
        snake_case       : str    =       "imagenet-1k-id2label.json"
        snake_case       : Optional[int]    =       json.load(open(hf_hub_download(lowercase		,				lowercase		,				repo_type="dataset"					)		,				"r"					)					)
        snake_case       : List[Any]    =       {int(lowercase					): v for k, v in idalabel.items()}
        snake_case       : Optional[int]    =       idalabel
        snake_case       : Optional[int]    =       {v: k for k, v in idalabel.items()}

    snake_case       : Tuple    =       img_size
    snake_case       : Any    =       num_classes
    snake_case       : str    =       embed_dim
    snake_case       : Any    =       depths
    snake_case       : Dict    =       num_heads
    snake_case       : List[Any]    =       window_size

    return config







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]					)      ->		Any:


    """simple docstring"""





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

    if name == "norm.weight":
        snake_case       : Optional[int]    =       "layernorm.weight"
    if name == "norm.bias":
        snake_case       : Tuple    =       "layernorm.bias"

    if "head" in name:
        snake_case       : Any    =       name.replace("head"		,				"classifier"					)
    else:
        snake_case       : Tuple    =       "swin." + name

    return name







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    List[Any]					)      ->		Any:


    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        snake_case       : int    =       orig_state_dict.pop(lowercase					)

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

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

    return orig_state_dict







def     __lowerCAmelCase		(		lowercase    :    Tuple		,				lowercase    :    Any					)      ->		Dict:


    """simple docstring"""





    snake_case       : str    =       timm.create_model(lowercase		,				pretrained=lowercase					)
    timm_model.eval()

    snake_case       : int    =       get_swin_config(lowercase					)
    snake_case       : Optional[int]    =       SwinForImageClassification(lowercase					)
    model.eval()

    snake_case       : int    =       convert_state_dict(timm_model.state_dict()		,				lowercase					)
    model.load_state_dict(lowercase					)

    snake_case       : Dict    =       "http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case       : Tuple    =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"		,				"-"					)					)					)
    snake_case       : Optional[int]    =       Image.open(requests.get(lowercase		,				stream=lowercase					).raw					)
    snake_case       : Dict    =       image_processor(images=lowercase		,				return_tensors="pt"					)

    snake_case       : str    =       timm_model(inputs["pixel_values"]					)
    snake_case       : Tuple    =       model(**lowercase					).logits

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

    print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'					)
    model.save_pretrained(lowercase					)

    print(F'Saving image processor to {pytorch_dump_folder_path}'					)
    image_processor.save_pretrained(lowercase					)


if __name__ == "__main__":
  __snake_case       =       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."""
  )

  __snake_case       =       parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





