




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_ : List[str]    		=       get_tests_dir("fixtures/test_sentencepiece_no_bos.model")







@require_sentencepiece
@require_tokenizers
class __snake_case			(			A_ ,  unittest.TestCase		):
   UpperCAmelCase__  :    str          =  PegasusTokenizer
   UpperCAmelCase__  :    Tuple          =  PegasusTokenizerFast
   UpperCAmelCase__  :    Dict          =  True
   UpperCAmelCase__  :    List[Any]          =  True







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        super().setUp()

        # We have a SentencePiece fixture for testing
        UpperCAmelCase_          =							PegasusTokenizer(_lowerCamelCase)
        tokenizer.save_pretrained(self.tmpdirname)







   @cached_property
   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Dict					,						**_snake_case		:						Dict):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						List[Any]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							'''</s>'''
        UpperCAmelCase_          =							1

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







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							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(_lowerCamelCase)					,						1103)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
        UpperCAmelCase_          =							self.tokenizer_class.from_pretrained(self.tmpdirname)
        UpperCAmelCase_          =							(
            '''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>'''
        )
        UpperCAmelCase_          =							rust_tokenizer([raw_input_str]					,						return_tensors=_lowerCamelCase					,						add_special_tokens=_lowerCamelCase).input_ids[0]
        UpperCAmelCase_          =							py_tokenizer([raw_input_str]					,						return_tensors=_lowerCamelCase					,						add_special_tokens=_lowerCamelCase).input_ids[0]
        self.assertListEqual(_lowerCamelCase					,						_lowerCamelCase)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self._large_tokenizer
        # <mask_1> masks whole sentence while <mask_2> masks single word
        UpperCAmelCase_          =							'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
        UpperCAmelCase_          =							[2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
        UpperCAmelCase_          =							tokenizer([raw_input_str]					,						return_tensors=_lowerCamelCase).input_ids[0]
        self.assertListEqual(_lowerCamelCase					,						_lowerCamelCase)







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

        """simple docstring"""


        UpperCAmelCase_          =							self._large_tokenizer
        # The tracebacks for the following asserts are **better** without messages or self.assertEqual
        assert tokenizer.vocab_size == 96103
        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
        UpperCAmelCase_          =							'''To ensure a smooth flow of bank resolutions.'''
        UpperCAmelCase_          =							[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
        UpperCAmelCase_          =							tokenizer([raw_input_str]					,						return_tensors=_lowerCamelCase).input_ids[0]
        self.assertListEqual(_lowerCamelCase					,						_lowerCamelCase)
        assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







   @require_torch
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							['''This is going to be way too long.''' * 150, '''short example''']
        UpperCAmelCase_          =							['''not super long but more than 5 tokens''', '''tiny''']
        UpperCAmelCase_          =							self._large_tokenizer(_lowerCamelCase					,						padding=_lowerCamelCase					,						truncation=_lowerCamelCase					,						return_tensors='''pt''')
        UpperCAmelCase_          =							self._large_tokenizer(
            text_target=_lowerCamelCase					,						max_length=5					,						padding=_lowerCamelCase					,						truncation=_lowerCamelCase					,						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(_lowerCamelCase) == 2  # input_ids, attention_mask.







   @slow
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							{'''input_ids''': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 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, 18289, 17780, 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=_lowerCamelCase					,						model_name='''google/bigbird-pegasus-large-arxiv'''					,						revision='''ba85d0851d708441f91440d509690f1ab6353415'''					,						)







@require_sentencepiece
@require_tokenizers
class __snake_case			(			A_ ,  unittest.TestCase		):
   UpperCAmelCase__  :    List[str]          =  PegasusTokenizer
   UpperCAmelCase__  :    Dict          =  PegasusTokenizerFast
   UpperCAmelCase__  :    Union[str, Any]          =  True
   UpperCAmelCase__  :    Optional[Any]          =  True







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        super().setUp()

        # We have a SentencePiece fixture for testing
        UpperCAmelCase_          =							PegasusTokenizer(_lowerCamelCase					,						offset=0					,						mask_token_sent=_lowerCamelCase					,						mask_token='''[MASK]''')
        tokenizer.save_pretrained(self.tmpdirname)







   @cached_property
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Any					,						**_snake_case		:						int):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						Dict):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
        UpperCAmelCase_          =							self.tokenizer_class.from_pretrained(self.tmpdirname)
        UpperCAmelCase_          =							(
            '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
            ''' <pad> <pad> <pad>'''
        )
        UpperCAmelCase_          =							rust_tokenizer([raw_input_str]					,						return_tensors=_lowerCamelCase					,						add_special_tokens=_lowerCamelCase).input_ids[0]
        UpperCAmelCase_          =							py_tokenizer([raw_input_str]					,						return_tensors=_lowerCamelCase					,						add_special_tokens=_lowerCamelCase).input_ids[0]
        self.assertListEqual(_lowerCamelCase					,						_lowerCamelCase)







   @require_torch
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							['''This is going to be way too long.''' * 1000, '''short example''']
        UpperCAmelCase_          =							['''not super long but more than 5 tokens''', '''tiny''']
        UpperCAmelCase_          =							self._large_tokenizer(_lowerCamelCase					,						padding=_lowerCamelCase					,						truncation=_lowerCamelCase					,						return_tensors='''pt''')
        UpperCAmelCase_          =							self._large_tokenizer(
            text_target=_lowerCamelCase					,						max_length=5					,						padding=_lowerCamelCase					,						truncation=_lowerCamelCase					,						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(_lowerCamelCase) == 2  # input_ids, attention_mask.







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							(
            '''This is an example string that is used to test the original TF implementation against the HF'''
            ''' implementation'''
        )

        UpperCAmelCase_          =							self._large_tokenizer(_lowerCamelCase).input_ids

        self.assertListEqual(
            _lowerCamelCase					,						[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]					,						)








"""simple docstring"""



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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__          = logging.get_logger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Union[str, Any]=False		)     ->					Optional[int]:
  _snake_case					     =       []
  for i in range(config.num_hidden_layers		):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''deit.encoder.layer.{i}.layernorm_before.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')		)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('''cls_token''', '''deit.embeddings.cls_token'''),
          ('''dist_token''', '''deit.embeddings.distillation_token'''),
          ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
          ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
          ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
      ]		)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('''norm.weight''', '''layernorm.weight'''),
            ('''norm.bias''', '''layernorm.bias'''),
            ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
            ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
        ]		)

    # if just the base model, we should remove "deit" from all keys that start with "deit"
    _snake_case					     =       [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''		) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ('''norm.weight''', '''deit.layernorm.weight'''),
            ('''norm.bias''', '''deit.layernorm.bias'''),
            ('''head.weight''', '''cls_classifier.weight'''),
            ('''head.bias''', '''cls_classifier.bias'''),
            ('''head_dist.weight''', '''distillation_classifier.weight'''),
            ('''head_dist.bias''', '''distillation_classifier.bias'''),
        ]		)

  return rename_keys
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Tuple=False		)     ->					Tuple:
  for i in range(config.num_hidden_layers		):
    if base_model:
      _snake_case					     =       ''''''
    else:
      _snake_case					     =       '''deit.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''		)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''		)
    # next, add query, keys and values (in that order) to the state dict
    _snake_case					     =       in_proj_weight[
        : config.hidden_size, :
    ]
    _snake_case					     =       in_proj_bias[: config.hidden_size]
    _snake_case					     =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case					     =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case					     =       in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case					     =       in_proj_bias[-config.hidden_size :]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Tuple		)     ->					Tuple:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( )     ->					Dict:
  _snake_case					     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case					     =       Image.open(requests.get(__lowerCamelCase ,					stream=__lowerCamelCase		).raw		)
  return im
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str		)     ->					str:
  _snake_case					     =       DeiTConfig()
  # all deit models have fine-tuned heads
  _snake_case					     =       False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  _snake_case					     =       10_00
  _snake_case					     =       '''huggingface/label-files'''
  _snake_case					     =       '''imagenet-1k-id2label.json'''
  _snake_case					     =       json.load(open(hf_hub_download(__lowerCamelCase ,					__lowerCamelCase ,					repo_type='''dataset'''		) ,					'''r'''		)		)
  _snake_case					     =       {int(__lowerCamelCase		): v for k, v in idalabel.items()}
  _snake_case					     =       idalabel
  _snake_case					     =       {v: k for k, v in idalabel.items()}
  _snake_case					     =       int(deit_name[-6:-4]		)
  _snake_case					     =       int(deit_name[-3:]		)
  # size of the architecture
  if deit_name[9:].startswith('''tiny'''		):
    _snake_case					     =       1_92
    _snake_case					     =       7_68
    _snake_case					     =       12
    _snake_case					     =       3
  elif deit_name[9:].startswith('''small'''		):
    _snake_case					     =       3_84
    _snake_case					     =       15_36
    _snake_case					     =       12
    _snake_case					     =       6
  if deit_name[9:].startswith('''base'''		):
    pass
  elif deit_name[4:].startswith('''large'''		):
    _snake_case					     =       10_24
    _snake_case					     =       40_96
    _snake_case					     =       24
    _snake_case					     =       16

  # load original model from timm
  _snake_case					     =       timm.create_model(__lowerCamelCase ,					pretrained=__lowerCamelCase		)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  _snake_case					     =       timm_model.state_dict()
  _snake_case					     =       create_rename_keys(__lowerCamelCase ,					__lowerCamelCase		)
  for src, dest in rename_keys:
    rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
  read_in_q_k_v(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)

  # load HuggingFace model
  _snake_case					     =       DeiTForImageClassificationWithTeacher(__lowerCamelCase		).eval()
  model.load_state_dict(__lowerCamelCase		)

  # Check outputs on an image, prepared by DeiTImageProcessor
  _snake_case					     =       int(
      (2_56 / 2_24) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  _snake_case					     =       DeiTImageProcessor(size=__lowerCamelCase ,					crop_size=config.image_size		)
  _snake_case					     =       image_processor(images=prepare_img() ,					return_tensors='''pt'''		)
  _snake_case					     =       encoding['''pixel_values''']
  _snake_case					     =       model(__lowerCamelCase		)

  _snake_case					     =       timm_model(__lowerCamelCase		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(__lowerCamelCase ,					outputs.logits ,					atol=1E-3		)

  Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
  print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''		)
  model.save_pretrained(__lowerCamelCase		)
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
  image_processor.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--deit_name',
     default='vit_deit_base_distilled_patch16_224',
     type=str,
     help='Name of the DeiT 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_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



