


"""simple docstring"""






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class     __lowerCamelCase  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Dict    =		BarthezTokenizer
     A_       :	str    =		BarthezTokenizerFast
     A_       :	int    =		True
     A_       :	Union[str, Any]    =		True


     def 							_UpperCAmelCase      (      self							)       ->							str:
          super().setUp()

          _a    	=				BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''							)
          tokenizer.save_pretrained(self.tmpdirname							)
          tokenizer.save_pretrained(self.tmpdirname    ,				legacy_format=__UpperCAmelCase							)
          _a    	=				tokenizer


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				'''<pad>'''
          _a    	=				1

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


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				list(self.get_tokenizer().get_vocab().keys()							)

          self.assertEqual(vocab_keys[0]    ,				'''<s>'''							)
          self.assertEqual(vocab_keys[1]    ,				'''<pad>'''							)
          self.assertEqual(vocab_keys[-1]    ,				'''<mask>'''							)
          self.assertEqual(len(__UpperCAmelCase							)    ,				101122							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          self.assertEqual(self.get_tokenizer().vocab_size    ,				101122							)


     @require_torch
     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          _a    	=				[0, 57, 3018, 70307, 91, 2]

          _a    	=				self.tokenizer(
              __UpperCAmelCase    ,				max_length=len(__UpperCAmelCase							)    ,				padding=__UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				return_tensors='''pt'''							)
          self.assertIsInstance(__UpperCAmelCase    ,				__UpperCAmelCase							)

          self.assertEqual((2, 6)    ,				batch.input_ids.shape							)
          self.assertEqual((2, 6)    ,				batch.attention_mask.shape							)
          _a    	=				batch.input_ids.tolist()[0]
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          if not self.test_rust_tokenizer:
               return

          _a    	=				self.get_tokenizer()
          _a    	=				self.get_rust_tokenizer()

          _a    	=				'''I was born in 92000, and this is falsé.'''

          _a    	=				tokenizer.tokenize(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.tokenize(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				self.get_rust_tokenizer()
          _a    	=				tokenizer.encode(__UpperCAmelCase							)
          _a    	=				rust_tokenizer.encode(__UpperCAmelCase							)
          self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          # fmt: off
          _a    	=				{'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # moussaKam/mbarthez is a french model. So we also use french texts.
          _a    	=				[
              '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
              '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
              '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
              '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
              '''telles que la traduction et la synthèse de texte.''',
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=__UpperCAmelCase    ,				model_name='''moussaKam/mbarthez'''    ,				revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''    ,				sequences=__UpperCAmelCase    ,				)



"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case		=       logging.get_logger(__name__)
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					Optional[int]=False,     _lowerCAmelCase     :					Any=False       ):






     """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'beit.encoder.layer.{i}.layernorm_before.weight')       )
          rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias')       )
          rename_keys.append(
              (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight')       )
          rename_keys.append(
              (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias')       )
          rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight')       )
          rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias')       )
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight')       )
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias')       )
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight')       )
          rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias')       )

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

     if has_lm_head:
          # mask token + layernorm
          rename_keys.extend(
              [
                  ('''mask_token''', '''beit.embeddings.mask_token'''),
                  ('''norm.weight''', '''layernorm.weight'''),
                  ('''norm.bias''', '''layernorm.bias'''),
              ]       )
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
                  ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                  ('''head.weight''', '''classifier.weight'''),
                  ('''head.bias''', '''classifier.bias'''),
              ]       )

     return rename_keys
def       A_   (   _lowerCAmelCase     :					int,     _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					List[Any]=False       ):






     """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
def       A_   (   _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					Any,     _lowerCAmelCase     :					List[Any]       ):






     """simple docstring"""

     _a    	=				dct.pop(_lowerCAmelCase       )
     _a    	=				val
def       A_   (   ):






     """simple docstring"""

     _a    	=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a    	=				Image.open(requests.get(_lowerCAmelCase,     stream=_lowerCAmelCase       ).raw       )
     return im
@torch.no_grad()
def       A_   (   _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Optional[int]=False       ):






     """simple docstring"""

     _a    	=				False if '''rvlcdip''' in checkpoint_url else True
     _a    	=				BeitConfig(use_absolute_position_embeddings=_lowerCAmelCase,     use_mask_token=_lowerCAmelCase       )

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          _a    	=				10_24
          _a    	=				40_96
          _a    	=				24
          _a    	=				16

     # labels
     if "rvlcdip" in checkpoint_url:
          _a    	=				16
          _a    	=				'''huggingface/label-files'''
          _a    	=				'''rvlcdip-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()}

     # load state_dict of original model, remove and rename some keys
     _a    	=				torch.hub.load_state_dict_from_url(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']

     _a    	=				create_rename_keys(_lowerCAmelCase,     has_lm_head=_lowerCAmelCase       )
     for src, dest in rename_keys:
          rename_key(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )
     read_in_q_k_v(_lowerCAmelCase,     _lowerCAmelCase,     has_lm_head=_lowerCAmelCase       )

     # load HuggingFace model
     _a    	=				BeitForMaskedImageModeling(_lowerCAmelCase       ) if has_lm_head else BeitForImageClassification(_lowerCAmelCase       )
     model.eval()
     model.load_state_dict(_lowerCAmelCase       )

     # Check outputs on an image
     _a    	=				BeitImageProcessor(
         size=config.image_size,     resample=PILImageResampling.BILINEAR,     do_center_crop=_lowerCAmelCase       )
     _a    	=				prepare_img()

     _a    	=				image_processor(images=_lowerCAmelCase,     return_tensors='''pt'''       )
     _a    	=				encoding['''pixel_values''']

     _a    	=				model(_lowerCAmelCase       )
     _a    	=				outputs.logits

     # verify logits
     _a    	=				[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
     assert logits.shape == torch.Size(_lowerCAmelCase       ), "Shape of logits not as expected"

     Path(_lowerCAmelCase       ).mkdir(exist_ok=_lowerCAmelCase       )
     print(f'Saving model 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 push_to_hub:
          if has_lm_head:
               _a    	=				'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
          else:
               _a    	=				'''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
          image_processor.push_to_hub(
              repo_path_or_name=Path(_lowerCAmelCase,     _lowerCAmelCase       ),     organization='''nielsr''',     commit_message='''Add image processor''',     use_temp_dir=_lowerCAmelCase,     )
          model.push_to_hub(
              repo_path_or_name=Path(_lowerCAmelCase,     _lowerCAmelCase       ),     organization='''nielsr''',     commit_message='''Add model''',     use_temp_dir=_lowerCAmelCase,     )


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()

   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
       type=str,
       help='''URL to the original PyTorch checkpoint (.pth file).''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
   )
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
   )
   __snake_case		=       parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)