





"""simple docstring"""





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Optional[Any]						    =   DebertaTokenizer
       __magic_name__	:str						    =   True
       __magic_name__	:Dict						    =   DebertaTokenizerFast






       def 	snake_case		(       self ):



         '''simple docstring'''

         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowerCAmelCase__     :Dict											=				[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '[UNK]',
         ]
         lowerCAmelCase__     :Union[str, Any]											=				dict(zip(__UpperCAmelCase       ,					range(len(__UpperCAmelCase ) ) ) )
         lowerCAmelCase__     :Dict											=				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         lowerCAmelCase__     :int											=				{'unk_token': '[UNK]'}

         lowerCAmelCase__     :List[Any]											=				os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES['vocab_file'] )
         lowerCAmelCase__     :Union[str, Any]											=				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(__UpperCAmelCase ) + '\n' )
         with open(self.merges_file       ,					'w'       ,					encoding='utf-8' ) as fp:
           fp.write('\n'.join(__UpperCAmelCase ) )






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         kwargs.update(self.special_tokens_map )
         return self.tokenizer_class.from_pretrained(self.tmpdirname       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				'lower newer'
         lowerCAmelCase__     :List[str]											=				'lower newer'
         return input_text, output_text






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				self.get_tokenizer()
         lowerCAmelCase__     :List[Any]											=				'lower newer'
         lowerCAmelCase__     :Optional[int]											=				['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
         lowerCAmelCase__     :Tuple											=				tokenizer.tokenize(__UpperCAmelCase )
         self.assertListEqual(__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :Any											=				tokens + [tokenizer.unk_token]
         lowerCAmelCase__     :str											=				[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase )       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.get_tokenizer()
         lowerCAmelCase__     :Optional[int]											=				tokenizer('Hello'       ,					'World' )
         lowerCAmelCase__     :List[Any]											=				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd['token_type_ids']       ,					__UpperCAmelCase )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				self.tokenizer_class.from_pretrained('microsoft/deberta-base' )

         lowerCAmelCase__     :Optional[Any]											=				tokenizer.encode('sequence builders'       ,					add_special_tokens=__UpperCAmelCase )
         lowerCAmelCase__     :str											=				tokenizer.encode('multi-sequence build'       ,					add_special_tokens=__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				tokenizer.encode(
             'sequence builders'       ,					add_special_tokens=__UpperCAmelCase       ,					add_prefix_space=__UpperCAmelCase )
         lowerCAmelCase__     :Any											=				tokenizer.encode(
             'sequence builders'       ,					'multi-sequence build'       ,					add_special_tokens=__UpperCAmelCase       ,					add_prefix_space=__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase )
         lowerCAmelCase__     :str											=				tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase       ,					__UpperCAmelCase )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				[self.tokenizer_class]
         if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class )

         for tokenizer_class in tokenizer_classes:
           lowerCAmelCase__     :Tuple											=				tokenizer_class.from_pretrained('microsoft/deberta-base' )

           lowerCAmelCase__     :Optional[int]											=				[
               'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
               'ALBERT incorporates two parameter reduction techniques',
               'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
               ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
               ' vocabulary embedding.',
           ]

           lowerCAmelCase__     :Optional[int]											=				tokenizer(__UpperCAmelCase       ,					padding=__UpperCAmelCase )
           lowerCAmelCase__     :Tuple											=				[tokenizer.decode(__UpperCAmelCase       ,					skip_special_tokens=__UpperCAmelCase ) for seq in encoding['input_ids']]

           # fmt: off
           lowerCAmelCase__     :Tuple											=				{
               'input_ids': [
                   [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 2, 0, 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, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 4, 2]
               ],
               'token_type_ids': [
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 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],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           lowerCAmelCase__     :List[Any]											=				[
               'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
               'ALBERT incorporates two parameter reduction techniques',
               'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
               ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
               ' vocabulary embedding.',
           ]

           self.assertDictEqual(encoding.data       ,					__UpperCAmelCase )

           for expected, decoded in zip(__UpperCAmelCase       ,					__UpperCAmelCase ):
             self.assertEqual(__UpperCAmelCase       ,					__UpperCAmelCase )




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__				:	Dict					       =					logging.get_logger(__name__)


def    __lowerCamelCase   (      A__						: Optional[Any]					)     ->    List[str]:
   lowerCamelCase_						:   int		= """huggingface/label-files"""
   lowerCamelCase_						:   Dict		= """imagenet-1k-id2label.json"""
   lowerCamelCase_						:   Optional[Any]		= json.load(open(hf_hub_download(A__						,						A__						,						repo_type="""dataset"""					)						,						"""r"""					)					)
   lowerCamelCase_						:   str		= {int(A__					): v for k, v in idalabel.items()}
   lowerCamelCase_						:   List[Any]		= {v: k for k, v in idalabel.items()}

   lowerCamelCase_						:   str		= """std_conv""" if """bit""" in model_name else False

   # note that when using BiT as backbone for ViT-hybrid checkpoints,
   # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
   # config.conv_layer = "std_conv_same"
   lowerCamelCase_						:   Optional[Any]		= BitConfig(
       conv_layer=A__						,						num_labels=1000						,						idalabel=A__						,						labelaid=A__						,						)

   return config


def    __lowerCamelCase   (      A__						: str					)     ->    Any:
   if "stem.conv" in name:
      lowerCamelCase_						:   Any		= name.replace("""stem.conv"""						,						"""bit.embedder.convolution"""					)
   if "blocks" in name:
      lowerCamelCase_						:   Dict		= name.replace("""blocks"""						,						"""layers"""					)
   if "head.fc" in name:
      lowerCamelCase_						:   Optional[Any]		= name.replace("""head.fc"""						,						"""classifier.1"""					)
   if name.startswith("""norm"""					):
      lowerCamelCase_						:   int		= """bit.""" + name
   if "bit" not in name and "classifier" not in name:
      lowerCamelCase_						:   str		= """bit.encoder.""" + name

   return name


def    __lowerCamelCase   (      )     ->    List[Any]:
   lowerCamelCase_						:   List[Any]		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowerCamelCase_						:   Optional[Any]		= Image.open(requests.get(A__						,						stream=A__					).raw					)
   return im


@torch.no_grad()
def    __lowerCamelCase   (      A__						: List[Any]						,						A__						: List[str]						,						A__						: Tuple=False					)     ->    List[str]:
   lowerCamelCase_						:   Optional[Any]		= get_config(A__					)

   # load original model from timm
   lowerCamelCase_						:   Optional[Any]		= create_model(A__						,						pretrained=A__					)
   timm_model.eval()

   # load state_dict of original model
   lowerCamelCase_						:   Optional[int]		= timm_model.state_dict()
   for key in state_dict.copy().keys():
      lowerCamelCase_						:   int		= state_dict.pop(A__					)
      lowerCamelCase_						:   Union[str, Any]		= val.squeeze() if """head""" in key else val

   # load HuggingFace model
   lowerCamelCase_						:   Tuple		= BitForImageClassification(A__					)
   model.eval()
   model.load_state_dict(A__					)

   # create image processor
   lowerCamelCase_						:   Optional[Any]		= create_transform(**resolve_data_config({}						,						model=A__					)					)
   lowerCamelCase_						:   List[Any]		= transform.transforms

   lowerCamelCase_						:   List[Any]		= {
       """bilinear""": PILImageResampling.BILINEAR,
       """bicubic""": PILImageResampling.BICUBIC,
       """nearest""": PILImageResampling.NEAREST,
   }

   lowerCamelCase_						:   List[str]		= BitImageProcessor(
       do_resize=A__						,						size={"""shortest_edge""": timm_transforms[0].size}						,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,						do_center_crop=A__						,						crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}						,						do_normalize=A__						,						image_mean=timm_transforms[-1].mean.tolist()						,						image_std=timm_transforms[-1].std.tolist()						,						)

   lowerCamelCase_						:   int		= prepare_img()
   lowerCamelCase_						:   int		= transform(A__					).unsqueeze(0					)
   lowerCamelCase_						:   List[str]		= processor(A__						,						return_tensors="""pt"""					).pixel_values

   # verify pixel values
   assert torch.allclose(A__						,						A__					)

   # verify logits
   with torch.no_grad():
      lowerCamelCase_						:   str		= model(A__					)
      lowerCamelCase_						:   int		= outputs.logits

   print("""Logits:"""						,						logits[0, :3]					)
   print("""Predicted class:"""						,						model.config.idalabel[logits.argmax(-1					).item()]					)
   lowerCamelCase_						:   List[Any]		= timm_model(A__					)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(A__						,						outputs.logits						,						atol=1e-3					)
   print("""Looks ok!"""					)

   if pytorch_dump_folder_path is not None:
      Path(A__					).mkdir(exist_ok=A__					)
      print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''					)
      model.save_pretrained(A__					)
      processor.save_pretrained(A__					)

   if push_to_hub:
      print(f'''Pushing model {model_name} and processor to the hub'''					)
      model.push_to_hub(f'''ybelkada/{model_name}'''					)
      processor.push_to_hub(f'''ybelkada/{model_name}'''					)


if __name__ == "__main__":
      snake_case__				:	Dict					       =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='resnetv2_50x1_bitm',
          type=str,
          help='Name of the BiT 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.'
      )
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Whether to push the model to the hub.',
      )

      snake_case__				:	int					       =					parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





