
'''simple docstring'''





import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase=False ,					UpperCAmelCase=False ,					UpperCAmelCase="data2vec."		)->     Optional[Any]:







      """simple docstring"""
      __A    		=     'backbone.' if is_semantic else ''

      __A    		=     []
      for i in range(config.num_hidden_layers		):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append(
                (f'{prefix}blocks.{i}.norm1.weight', f'{hf_prefix}encoder.layer.{i}.layernorm_before.weight')		)
            rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'{hf_prefix}encoder.layer.{i}.layernorm_before.bias')		)
            rename_keys.append(
                (f'{prefix}blocks.{i}.attn.proj.weight', f'{hf_prefix}encoder.layer.{i}.attention.output.dense.weight')		)
            rename_keys.append(
                (f'{prefix}blocks.{i}.attn.proj.bias', f'{hf_prefix}encoder.layer.{i}.attention.output.dense.bias')		)
            rename_keys.append(
                (f'{prefix}blocks.{i}.norm2.weight', f'{hf_prefix}encoder.layer.{i}.layernorm_after.weight')		)
            rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'{hf_prefix}encoder.layer.{i}.layernorm_after.bias')		)
            rename_keys.append(
                (f'{prefix}blocks.{i}.mlp.fc1.weight', f'{hf_prefix}encoder.layer.{i}.intermediate.dense.weight')		)
            rename_keys.append(
                (f'{prefix}blocks.{i}.mlp.fc1.bias', f'{hf_prefix}encoder.layer.{i}.intermediate.dense.bias')		)
            rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'{hf_prefix}encoder.layer.{i}.output.dense.weight')		)
            rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'{hf_prefix}encoder.layer.{i}.output.dense.bias')		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              (f'{prefix}cls_token', f'{hf_prefix}embeddings.cls_token'),
              (f'{prefix}patch_embed.proj.weight', f'{hf_prefix}embeddings.patch_embeddings.projection.weight'),
              (f'{prefix}patch_embed.proj.bias', f'{hf_prefix}embeddings.patch_embeddings.projection.bias'),
          ]		)

      if has_lm_head:
            # mask token + shared relative position bias + layernorm
            rename_keys.extend(
                [
                    ('mask_token', f'{hf_prefix}embeddings.mask_token'),
                    (
                        'rel_pos_bias.relative_position_bias_table',
                        f'{hf_prefix}encoder.relative_position_bias.relative_position_bias_table',
                    ),
                    (
                        'rel_pos_bias.relative_position_index',
                        f'{hf_prefix}encoder.relative_position_bias.relative_position_index',
                    ),
                    ('norm.weight', 'layernorm.weight'),
                    ('norm.bias', 'layernorm.bias'),
                ]		)
      elif is_semantic:
            # semantic segmentation classification heads
            rename_keys.extend(
                [
                    ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                    ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                    ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                    ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
                ]		)
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('fc_norm.weight', f'{hf_prefix}pooler.layernorm.weight'),
                    ('fc_norm.bias', f'{hf_prefix}pooler.layernorm.bias'),
                    ('head.weight', 'classifier.weight'),
                    ('head.bias', 'classifier.bias'),
                ]		)

      return rename_keys





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase=False ,					UpperCAmelCase=False ,					UpperCAmelCase="data2vec_vision."		)->     Optional[int]:







      """simple docstring"""
      for i in range(config.num_hidden_layers		):
            __A    		=     'backbone.' if is_semantic else ''
            # queries, keys and values
            __A    		=     state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'		)
            __A    		=     state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'		)
            __A    		=     state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'		)

            __A    		=     in_proj_weight[
                : config.hidden_size, :
            ]
            __A    		=     q_bias
            __A    		=     in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            __A    		=     in_proj_weight[
                -config.hidden_size :, :
            ]
            __A    		=     v_bias

            # gamma_1 and gamma_2
            # we call them lambda because otherwise they are renamed when using .from_pretrained
            __A    		=     state_dict.pop(f'{prefix}blocks.{i}.gamma_1'		)
            __A    		=     state_dict.pop(f'{prefix}blocks.{i}.gamma_2'		)

            __A    		=     gamma_a
            __A    		=     gamma_a

            # relative_position bias table + index
            if not has_lm_head:
                  # each layer has its own relative position bias
                  __A    		=     state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'		)
                  __A    		=     state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'		)

                  __A    		=     table
                  __A    		=     index





def     snake_case							(						)->     List[str]:







      """simple docstring"""
      __A    		=     argparse.ArgumentParser(
          'Convert Data2VecVision to HF for image classification and pretraining' ,					add_help=_lowerCAmelCase		)
      parser.add_argument('--hf_checkpoint_name' ,					type=_lowerCAmelCase		)
      parser.add_argument('--input_size' ,					default=2_2_4 ,					type=_lowerCAmelCase ,					help='images input size'		)
      parser.add_argument('--beit_checkpoint' ,					default='' ,					help='beit checkpoint'		)

      return parser.parse_args()





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     Optional[int]:







      """simple docstring"""
      def load_state_dict(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase="" ,					UpperCAmelCase="relative_position_index"		):
            __A    		=     []
            __A    		=     []
            __A    		=     []
            # copy state_dict so _load_from_state_dict can modify it
            __A    		=     getattr(_lowerCAmelCase ,					'_metadata' ,					_lowerCAmelCase		)
            __A    		=     state_dict.copy()
            if metadata is not None:
                  __A    		=     metadata

            def load(UpperCAmelCase ,					UpperCAmelCase=""		):
                  __A    		=     {} if metadata is None else metadata.get(prefix[:-1] ,					{}		)
                  module._load_from_state_dict(
                      _lowerCAmelCase ,					_lowerCAmelCase ,					_lowerCAmelCase ,					_lowerCAmelCase ,					_lowerCAmelCase ,					_lowerCAmelCase ,					_lowerCAmelCase		)
                  for name, child in module._modules.items():
                        if child is not None:
                              load(_lowerCAmelCase ,					prefix + name + '.'		)

            load(_lowerCAmelCase ,					prefix=_lowerCAmelCase		)

            __A    		=     []
            __A    		=     []
            for key in missing_keys:
                  __A    		=     True
                  for ignore_key in ignore_missing.split('|'		):
                        if ignore_key in key:
                              __A    		=     False
                              break
                  if keep_flag:
                        warn_missing_keys.append(_lowerCAmelCase		)
                  else:
                        ignore_missing_keys.append(_lowerCAmelCase		)

            __A    		=     warn_missing_keys

            if len(_lowerCAmelCase		) > 0:
                  print(
                      'Weights of {} not initialized from pretrained model: {}'.format(
                          model.__class__.__name__ ,					_lowerCAmelCase		)		)
            if len(_lowerCAmelCase		) > 0:
                  print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__ ,					_lowerCAmelCase		)		)
            if len(_lowerCAmelCase		) > 0:
                  print(
                      'Ignored weights of {} not initialized from pretrained model: {}'.format(
                          model.__class__.__name__ ,					_lowerCAmelCase		)		)
            if len(_lowerCAmelCase		) > 0:
                  print('\n'.join(_lowerCAmelCase		)		)

      __A    		=     {
          'pretrained': False,
          'use_shared_rel_pos_bias': True,
          'use_abs_pos_emb': False,
          'init_values': 0.1,
      }

      if is_finetuned:
            model_kwargs.update(
                {
                    'num_classes': 1_0_0_0,
                    'use_mean_pooling': True,
                    'init_scale': 0.001,
                    'use_rel_pos_bias': True,
                }		)

      __A    		=     create_model(
          'beit_large_patch16_224' if is_large else 'beit_base_patch16_224' ,					**_lowerCAmelCase ,					)
      __A    		=     model.patch_embed.patch_size
      __A    		=     (args.input_size // patch_size[0], args.input_size // patch_size[1])
      __A    		=     torch.load(args.beit_checkpoint ,					map_location='cpu'		)

      print(f'Load ckpt from {args.beit_checkpoint}'		)
      __A    		=     None
      for model_key in ("model", "module"):
            if model_key in checkpoint:
                  __A    		=     checkpoint[model_key]
                  print(f'Load state_dict by model_key = {model_key}'		)
                  break

      __A    		=     list(checkpoint_model.keys()		)
      for key in all_keys:
            if "relative_position_index" in key:
                  checkpoint_model.pop(_lowerCAmelCase		)

            if "relative_position_bias_table" in key:
                  __A    		=     checkpoint_model[key]
                  __A		, __A    		=     rel_pos_bias.size()
                  __A		, __A    		=     model.state_dict()[key].size()
                  __A    		=     model.patch_embed.patch_shape
                  if dst_patch_shape[0] != dst_patch_shape[1]:
                        raise NotImplementedError()

      load_state_dict(_lowerCAmelCase ,					_lowerCAmelCase ,					prefix=''		)

      return model





def     snake_case							(						)->     List[Any]:







      """simple docstring"""
      __A    		=     get_args()

      __A    		=     'ft1k' in args.hf_checkpoint_name
      __A    		=     'large' in args.hf_checkpoint_name

      if is_finetuned:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
            # into this folder.
            import modeling_finetune  # noqa: F401
      else:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
            # into this folder
            # IMPORTANT: Note that for now we've only converted the down-stream
            # model and not the full pretrained model. This means for the integration
            # test you need to add a `return x` after the following line:
            # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
            # to make the integration test pass.
            import modeling_cyclical  # noqa: F401

      # 1. Create model config
      __A    		=     DataaVecVisionConfig()
      if is_finetuned:
            __A    		=     True
            __A    		=     False
            __A    		=     True
            __A    		=     1_0_0_0

            __A    		=     'huggingface/label-files'
            __A    		=     'imagenet-1k-id2label.json'
            __A    		=     json.load(open(hf_hub_download(_lowerCAmelCase ,					_lowerCAmelCase ,					repo_type='dataset'		) ,					'r'		)		)
            __A    		=     {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
            __A    		=     idalabel
            __A    		=     {v: k for k, v in idalabel.items()}
      else:
            __A    		=     False
            __A    		=     True
            __A    		=     False

      if is_large:
            __A    		=     1_0_2_4
            __A    		=     4_0_9_6
            __A    		=     2_4
            __A    		=     1_6

      # 2. Load Beit model
      __A    		=     load_beit_model(_lowerCAmelCase ,					_lowerCAmelCase ,					_lowerCAmelCase		)
      orig_model.eval()

      # 3. Forward Beit model
      __A    		=     BeitImageProcessor(size=config.image_size ,					do_center_crop=_lowerCAmelCase		)
      __A    		=     Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'		)
      __A    		=     image_processor(images=_lowerCAmelCase ,					return_tensors='pt'		)
      __A    		=     encoding['pixel_values']

      __A    		=     (pixel_values,) if is_finetuned else (pixel_values, None)
      with torch.no_grad():
            __A    		=     orig_model(*_lowerCAmelCase		)

      # 4. Load HF Data2VecVision model
      if is_finetuned:
            __A    		=     DataaVecVisionForImageClassification(_lowerCAmelCase		)
            hf_model.eval()
            __A    		=     False
            __A    		=     'data2vec_vision.'
      else:
            __A    		=     DataaVecVisionModel(_lowerCAmelCase		)
            hf_model.eval()
            __A    		=     True
            __A    		=     ''

      __A    		=     create_rename_keys(_lowerCAmelCase ,					hf_prefix=_lowerCAmelCase ,					has_lm_head=_lowerCAmelCase		)
      __A    		=     orig_model.state_dict()
      for src, dest in rename_keys:
            __A    		=     state_dict.pop(_lowerCAmelCase		)
            __A    		=     val

      read_in_q_k_v(_lowerCAmelCase ,					_lowerCAmelCase ,					hf_prefix=_lowerCAmelCase ,					has_lm_head=_lowerCAmelCase		)
      __A		, __A    		=     hf_model.load_state_dict(_lowerCAmelCase ,					strict=_lowerCAmelCase		)
      print('HF missing' ,					_lowerCAmelCase		)
      print('HF unexpected_keys' ,					_lowerCAmelCase		)

      # 5. Forward HF Data2VecVision model
      with torch.no_grad():
            __A    		=     hf_model(_lowerCAmelCase		)

      __A    		=     hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

      # 6. Compare
      __A    		=     torch.max(torch.abs(hf_output - orig_model_output		)		).item()

      print(f'max_absolute_diff = {max_absolute_diff}'		)
      __A    		=     torch.allclose(_lowerCAmelCase ,					_lowerCAmelCase ,					atol=1e-3		)
      print('Do both models output the same tensors?' ,					'🔥' if success else '💩'		)
      if not success:
            raise Exception('Something went wRoNg'		)

      # 7. Save
      print(f'Saving to {args.hf_checkpoint_name}'		)
      hf_model.save_pretrained(args.hf_checkpoint_name		)
      image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
 main()
 # Run the following to convert checkpoints
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"








'''simple docstring'''





import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE ,       unittest.TestCase):
 UpperCAmelCase__      :      Tuple  			=   PreTrainedTokenizerFast
 UpperCAmelCase__      :      Tuple  			=   False
 UpperCAmelCase__      :      str  			=   True
 UpperCAmelCase__      :      str  			=   'tokenizer_file'






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







       '''simple docstring'''



       __A    		=     False  # because we don't have pretrained_vocab_files_map
       super().setUp()
       __A    		=     True

       __A    		=     ['robot-test/dummy-tokenizer-fast', 'robot-test/dummy-tokenizer-wordlevel']
       __A    		=     'SaulLu/dummy-tokenizer-bytelevel-bpe'

       # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
       __A    		=     [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

       __A    		=     PreTrainedTokenizerFast.from_pretrained(model_paths[0]     )
       tokenizer.save_pretrained(self.tmpdirname     )






 def 		lowercase_  (     self					:Any     )			->     List[str]:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:Optional[Any]     )			->     Optional[Any]:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:Tuple     )			->     str:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:List[Any]     )			->     Tuple:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:List[Any]     )			->     str:







       '''simple docstring'''



       __A    		=     self.tmpdirname
       # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                   try:
                         __A    		=     tempfile.mkdtemp()
                         __A    		=     self.rust_tokenizer_class.from_pretrained(_A		,			**_A     )

                         tokenizer.save_pretrained(self.tmpdirname     )
                         super().test_training_new_tokenizer()
                   finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname     )
                         __A    		=     tmpdirname_orig






 def 		lowercase_  (     self					:int     )			->     int:







       '''simple docstring'''



       __A    		=     self.tmpdirname
       # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                   try:
                         __A    		=     tempfile.mkdtemp()
                         __A    		=     self.rust_tokenizer_class.from_pretrained(_A		,			**_A     )

                         tokenizer.save_pretrained(self.tmpdirname     )
                         super().test_training_new_tokenizer_with_special_tokens_change()
                   finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname     )
                         __A    		=     tmpdirname_orig






 def 		lowercase_  (     self					:Optional[Any]     )			->     Any:







       '''simple docstring'''



       __A    		=     self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name     )

       __A    		=     ('a' for _ in range(1_000     ))
       __A    		=     tokenizer.train_new_from_iterator(text_iterator=_A		,			length=1_000		,			vocab_size=50     )

       __A    		=     new_tokenizer.encode('a🤗'     )
       self.assertEqual(_A		,			[64, 172, 253, 97, 245]     )


@require_tokenizers
class 	UpperCamelCase__					(      unittest.TestCase):






 def 		lowercase_  (     self					:Tuple     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     AutoTokenizer.from_pretrained('bert-base-cased'     )
       __A    		=     json.loads(tokenizer._tokenizer.to_str()     )
       __A    		=     len(_A     )

       with tempfile.TemporaryDirectory() as tmp_dir:
             # Hack to save this in the tokenizer_config.json
             __A    		=     ['tokenizer.4.0.0.json']
             tokenizer.save_pretrained(_A     )
             json.dump(_A		,			open(os.path.join(_A		,			'tokenizer.4.0.0.json'     )		,			'w'     )     )

             # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
             __A    		=     AutoTokenizer.from_pretrained(_A     )
             self.assertEqual(len(_A     )		,			len(_A     ) + 1     )
             __A    		=     json.loads(new_tokenizer._tokenizer.to_str()     )
             self.assertIn('huggingface'		,			json_tokenizer['model']['vocab']     )

             # Will need to be adjusted if we reach v42 and this test is still here.
             # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
             shutil.move(os.path.join(_A		,			'tokenizer.4.0.0.json'     )		,			os.path.join(_A		,			'tokenizer.42.0.0.json'     )     )
             __A    		=     ['tokenizer.42.0.0.json']
             tokenizer.save_pretrained(_A     )
             __A    		=     AutoTokenizer.from_pretrained(_A     )
             self.assertEqual(len(_A     )		,			len(_A     )     )
             __A    		=     json.loads(new_tokenizer._tokenizer.to_str()     )
             self.assertNotIn('huggingface'		,			json_tokenizer['model']['vocab']     )






 def 		lowercase_  (     self					:List[Any]     )			->     Optional[int]:







       '''simple docstring'''



       __A    		=     'hf-internal-testing/test-two-tokenizers'

       # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
       __A    		=     AutoTokenizer.from_pretrained(_A     )
       self.assertEqual(len(_A     )		,			28_997     )
       __A    		=     json.loads(tokenizer._tokenizer.to_str()     )
       self.assertIn('huggingface'		,			json_tokenizer['model']['vocab']     )

       # Testing an older version by monkey-patching the version in the module it's used.
       import transformers as old_transformers

       __A    		=     '3.0.0'
       __A    		=     old_transformers.models.auto.AutoTokenizer.from_pretrained(_A     )
       self.assertEqual(len(_A     )		,			28_996     )
       __A    		=     json.loads(old_tokenizer._tokenizer.to_str()     )
       self.assertNotIn('huggingface'		,			json_tokenizer['model']['vocab']     )


@require_tokenizers
class 	UpperCamelCase__					(      unittest.TestCase):






 def 		lowercase_  (     self					:List[str]     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     PreTrainedTokenizerFast.from_pretrained('robot-test/dummy-tokenizer-wordlevel'     )
       __A    		=     'The Matrix is a 1999 science fiction action film.'

       with concurrent.futures.ThreadPoolExecutor() as executor:
             __A    		=     [executor.submit(self.fetch		,			_A		,			_A     ) for i in range(10     )]
             __A    		=     [future.result() for future in futures]
             self.assertEqual(_A		,			[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10     )]     )






 def 		lowercase_  (     self					:Optional[int]		,			_A					:str		,			_A					:Any     )			->     Optional[int]:







       '''simple docstring'''



       return tokenizer.encode(_A		,			truncation='longest_first'		,			padding='longest'     )






