






import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_		:		Union[str, Any]		     =			logging.get_logger()

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = True  )	->    Optional[int]:
  print(F'''Converting {name}...'''  )

  with torch.no_grad():
    if hidden_sizes == 1_2_8:
      if name[-1] == "S":
        UpperCamelCase_: int   				=    timm.create_model('levit_128s'     ,   pretrained=A__  )
      else:
        UpperCamelCase_: Dict   				=    timm.create_model('levit_128'     ,   pretrained=A__  )
    if hidden_sizes == 1_9_2:
      UpperCamelCase_: Optional[int]   				=    timm.create_model('levit_192'     ,   pretrained=A__  )
    if hidden_sizes == 2_5_6:
      UpperCamelCase_: str   				=    timm.create_model('levit_256'     ,   pretrained=A__  )
    if hidden_sizes == 3_8_4:
      UpperCamelCase_: Optional[int]   				=    timm.create_model('levit_384'     ,   pretrained=A__  )

    from_model.eval()
    UpperCamelCase_: List[Any]   				=    LevitForImageClassificationWithTeacher(A__  ).eval()
    UpperCamelCase_: int   				=    OrderedDict()

    UpperCamelCase_: List[str]   				=    from_model.state_dict()
    UpperCamelCase_: List[Any]   				=    list(from_model.state_dict().keys()  )
    UpperCamelCase_: List[str]   				=    list(our_model.state_dict().keys()  )
    print(len(A__  )     ,   len(A__  )  )
    for i in range(len(A__  )  ):
      UpperCamelCase_: Optional[int]   				=    weights[og_keys[i]]
    our_model.load_state_dict(A__  )

    UpperCamelCase_: str   				=    torch.randn((2, 3, 2_2_4, 2_2_4)  )
    UpperCamelCase_: Dict   				=    from_model(A__  )
    UpperCamelCase_: List[Any]   				=    our_model(A__  ).logits

  assert torch.allclose(A__     ,   A__  ), "The model logits don't match the original one."

  UpperCamelCase_: Optional[int]   				=    name
  print(A__  )

  if push_to_hub:
    our_model.save_pretrained(save_directory / checkpoint_name  )
    UpperCamelCase_: int   				=    LevitImageProcessor()
    image_processor.save_pretrained(save_directory / checkpoint_name  )

    print(F'''Pushed {checkpoint_name}'''  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = True  )	->    List[Any]:
  UpperCamelCase_: int   				=    """imagenet-1k-id2label.json"""
  UpperCamelCase_: str   				=    1_0_0_0
  UpperCamelCase_: List[Any]   				=    (1, num_labels)

  UpperCamelCase_: str   				=    """huggingface/label-files"""
  UpperCamelCase_: Optional[int]   				=    num_labels
  UpperCamelCase_: Dict   				=    json.load(open(hf_hub_download(A__     ,   A__     ,   repo_type='dataset'  )     ,   'r'  )  )
  UpperCamelCase_: int   				=    {int(A__  ): v for k, v in idalabel.items()}

  UpperCamelCase_: Any   				=    idalabel
  UpperCamelCase_: Tuple   				=    {v: k for k, v in idalabel.items()}

  UpperCamelCase_: int   				=    partial(A__     ,   num_labels=A__     ,   idalabel=A__     ,   labelaid=A__  )

  UpperCamelCase_: Dict   				=    {
      """levit-128S""": 1_2_8,
      """levit-128""": 1_2_8,
      """levit-192""": 1_9_2,
      """levit-256""": 2_5_6,
      """levit-384""": 3_8_4,
  }

  UpperCamelCase_: int   				=    {
      """levit-128S""": ImageNetPreTrainedConfig(
          hidden_sizes=[1_2_8, 2_5_6, 3_8_4]     ,   num_attention_heads=[4, 6, 8]     ,   depths=[2, 3, 4]     ,   key_dim=[1_6, 1_6, 1_6]     ,   drop_path_rate=0     ,   ),
      """levit-128""": ImageNetPreTrainedConfig(
          hidden_sizes=[1_2_8, 2_5_6, 3_8_4]     ,   num_attention_heads=[4, 8, 1_2]     ,   depths=[4, 4, 4]     ,   key_dim=[1_6, 1_6, 1_6]     ,   drop_path_rate=0     ,   ),
      """levit-192""": ImageNetPreTrainedConfig(
          hidden_sizes=[1_9_2, 2_8_8, 3_8_4]     ,   num_attention_heads=[3, 5, 6]     ,   depths=[4, 4, 4]     ,   key_dim=[3_2, 3_2, 3_2]     ,   drop_path_rate=0     ,   ),
      """levit-256""": ImageNetPreTrainedConfig(
          hidden_sizes=[2_5_6, 3_8_4, 5_1_2]     ,   num_attention_heads=[4, 6, 8]     ,   depths=[4, 4, 4]     ,   key_dim=[3_2, 3_2, 3_2]     ,   drop_path_rate=0     ,   ),
      """levit-384""": ImageNetPreTrainedConfig(
          hidden_sizes=[3_8_4, 5_1_2, 7_6_8]     ,   num_attention_heads=[6, 9, 1_2]     ,   depths=[4, 4, 4]     ,   key_dim=[3_2, 3_2, 3_2]     ,   drop_path_rate=0.1     ,   ),
  }

  if model_name:
    convert_weight_and_push(
        names_to_hidden_sizes[model_name]     ,   A__     ,   names_to_config[model_name]     ,   A__     ,   A__  )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]     ,   A__     ,   A__     ,   A__     ,   A__  )
  return config, expected_shape


if __name__ == "__main__":
   A_		:		Optional[Any]		     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default=None,
       type=str,
       help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default='levit-dump-folder/',
       type=Path,
       required=False,
       help='Path to the output PyTorch model directory.',
   )
   parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
   parser.add_argument(
       '--no-push_to_hub',
       dest='push_to_hub',
       action='store_false',
       help='Do not push model and image processor to the hub',
   )

   A_		:		Any		     =			parser.parse_args()
   A_		:		Path		     =			args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[Any]									=CLIPTokenizer
       a    :		List[Any]									=CLIPTokenizerFast
       a    :		str									=True
       a    :		List[Any]									={}
       a    :		str									=False





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

         # fmt: off
         UpperCamelCase_: str   				=    ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
         # fmt: on
         UpperCamelCase_: Any   				=    dict(zip(_lowerCamelCase  ,      range(len(_lowerCamelCase							)							)							)							)
         UpperCamelCase_: Optional[int]   				=    ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
         UpperCamelCase_: Any   				=    {'unk_token': '<unk>'}

         UpperCamelCase_: List[Any]   				=    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['vocab_file']							)
         UpperCamelCase_: Optional[int]   				=    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['merges_file']							)
         with open(self.vocab_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write(json.dumps(_lowerCamelCase							) + '\n'							)
         with open(self.merges_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write('\n'.join(_lowerCamelCase							)							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return CLIPTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, Any]   				=    'lower newer'
         UpperCamelCase_: Tuple   				=    'lower newer'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    CLIPTokenizer(self.vocab_file  ,      self.merges_file  ,      **self.special_tokens_map							)
         UpperCamelCase_: Optional[int]   				=    'lower newer'
         UpperCamelCase_: Any   				=    ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
         UpperCamelCase_: Union[str, Any]   				=    tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: str   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Optional[Any]   				=    [1_0, 2, 1_6, 9, 3, 2, 1_6, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       @require_ftfy
       def       _a   ( self							):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: Optional[int]   				=    self.tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)
             UpperCamelCase_: List[str]   				=    self.rust_tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)

             UpperCamelCase_: Optional[int]   				=    'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
             UpperCamelCase_: Tuple   				=    tokenizer_s.tokenize(_lowerCamelCase							)
             UpperCamelCase_: Any   				=    tokenizer_r.tokenize(_lowerCamelCase							)

             self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
             # with Tilde) encoded in 2 different ways
             UpperCamelCase_: List[str]   				=    'xa\u0303y' + ' ' + 'x\xe3y'
             UpperCamelCase_: List[Any]   				=    tokenizer_s.tokenize(_lowerCamelCase							)
             UpperCamelCase_: int   				=    tokenizer_r.tokenize(_lowerCamelCase							)

             self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on unicode of space type
             UpperCamelCase_: Dict   				=    [
                 '\u0009',  # (horizontal tab, '\t')
                 '\u000B',  # (vertical tab)
                 '\u000C',  # (form feed)
                 '\u0020',  # (space, ' ')
                 '\u200E',  # (left-to-right mark):w
                 '\u200F',  # (right-to-left mark)
             ]
             for unicode_seq in spaces_unicodes:
               UpperCamelCase_: int   				=    tokenizer_s.tokenize(_lowerCamelCase							)
               UpperCamelCase_: Dict   				=    tokenizer_r.tokenize(_lowerCamelCase							)

               self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on unicode of line break type
             UpperCamelCase_: List[str]   				=    [
                 '\u000A',  # (line feed, '\n')
                 '\r\n',  # (carriage return and line feed, '\r\n')
                 '\u000D',  # (carriage return, '\r')
                 '\r',  # (carriage return, '\r')
                 '\u000D',  # (carriage return, '\r')
                 '\u2028',  # (line separator)
                 '\u2029',  # (paragraph separator)
                 # "\u0085", # (next line)
             ]

             # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
             # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
             # space (and thus into an empty list).

             for unicode_seq in line_break_unicodes:
               UpperCamelCase_: Optional[Any]   				=    tokenizer_s.tokenize(_lowerCamelCase							)
               UpperCamelCase_: str   				=    tokenizer_r.tokenize(_lowerCamelCase							)

               self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: str   				=    'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
             UpperCamelCase_: str   				=    f'''{text_of_1_token} {text_of_1_token}'''

             UpperCamelCase_: Optional[Any]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      )
             UpperCamelCase_: int   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							) + 1, len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: List[str]   				=    f''' {text}'''

             UpperCamelCase_: str   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      )
             UpperCamelCase_: Dict   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (1, 1 + len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (1 + len(_lowerCamelCase							) + 1, 1 + len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )





       def       _a   ( self							):
         # Test related to the breaking change introduced in transformers v4.17.0
         # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
         with self.assertRaises(_lowerCamelCase							) as context:
           self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer'							)

         self.assertTrue(
             context.exception.args[0].startswith(
                 'The `backend_tokenizer` provided does not match the expected format.'							)							)





       @require_ftfy
       def       _a   ( self							):
         super().test_tokenization_python_rust_equals()





       def       _a   ( self							):
         # CLIP always lower cases letters
         pass