
'''simple docstring'''





import argparse
import json
from pathlib import Path

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

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


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





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







      """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'),
          ]		)

      if has_lm_head:
            # mask token + shared relative position bias + layernorm
            rename_keys.extend(
                [
                    ('mask_token', 'beit.embeddings.mask_token'),
                    (
                        'rel_pos_bias.relative_position_bias_table',
                        'beit.encoder.relative_position_bias.relative_position_bias_table',
                    ),
                    (
                        'rel_pos_bias.relative_position_index',
                        'beit.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', 'beit.pooler.layernorm.weight'),
                    ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
                    ('head.weight', 'classifier.weight'),
                    ('head.bias', 'classifier.bias'),
                ]		)

      return rename_keys





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase=False ,					UpperCAmelCase=False		)->     str:







      """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							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     List[str]:







      """simple docstring"""
      __A    		=     dct.pop(UpperCAmelCase		)
      __A    		=     val





def     snake_case							(						)->     List[Any]:







      """simple docstring"""
      __A    		=     'http://images.cocodataset.org/val2017/000000039769.jpg'
      __A    		=     Image.open(requests.get(UpperCAmelCase ,					stream=UpperCAmelCase		).raw		)
      return im





@torch.no_grad()
def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     str:







      """simple docstring"""
      __A    		=     BeitConfig()
      __A    		=     False
      __A    		=     False
      __A    		=     'huggingface/label-files'
      # set config parameters based on URL
      if checkpoint_url[-9:-4] == "pt22k":
            # masked image modeling
            __A    		=     True
            __A    		=     True
            __A    		=     True
      elif checkpoint_url[-9:-4] == "ft22k":
            # intermediate fine-tuning on ImageNet-22k
            __A    		=     True
            __A    		=     2_1_8_4_1
            __A    		=     'imagenet-22k-id2label.json'
            __A    		=     json.load(open(hf_hub_download(UpperCAmelCase ,					UpperCAmelCase ,					repo_type='dataset'		) ,					'r'		)		)
            __A    		=     {int(UpperCAmelCase		): v for k, v in idalabel.items()}
            # this dataset contains 21843 labels but the model only has 21841
            # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
            del idalabel[9_2_0_5]
            del idalabel[1_5_0_2_7]
            __A    		=     idalabel
            __A    		=     {v: k for k, v in idalabel.items()}
      elif checkpoint_url[-8:-4] == "to1k":
            # fine-tuning on ImageNet-1k
            __A    		=     True
            __A    		=     1_0_0_0
            __A    		=     'imagenet-1k-id2label.json'
            __A    		=     json.load(open(hf_hub_download(UpperCAmelCase ,					UpperCAmelCase ,					repo_type='dataset'		) ,					'r'		)		)
            __A    		=     {int(UpperCAmelCase		): v for k, v in idalabel.items()}
            __A    		=     idalabel
            __A    		=     {v: k for k, v in idalabel.items()}
            if "384" in checkpoint_url:
                  __A    		=     3_8_4
            if "512" in checkpoint_url:
                  __A    		=     5_1_2
      elif "ade20k" in checkpoint_url:
            # fine-tuning
            __A    		=     True
            __A    		=     1_5_0
            __A    		=     'ade20k-id2label.json'
            __A    		=     json.load(open(hf_hub_download(UpperCAmelCase ,					UpperCAmelCase ,					repo_type='dataset'		) ,					'r'		)		)
            __A    		=     {int(UpperCAmelCase		): v for k, v in idalabel.items()}
            __A    		=     idalabel
            __A    		=     {v: k for k, v in idalabel.items()}
            __A    		=     6_4_0
            __A    		=     True
      else:
            raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''		)

      # size of the architecture
      if "base" in checkpoint_url:
            pass
      elif "large" in checkpoint_url:
            __A    		=     1_0_2_4
            __A    		=     4_0_9_6
            __A    		=     2_4
            __A    		=     1_6
            if "ade20k" in checkpoint_url:
                  __A    		=     6_4_0
                  __A    		=     [7, 1_1, 1_5, 2_3]
      else:
            raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'		)

      # load state_dict of original model, remove and rename some keys
      __A    		=     torch.hub.load_state_dict_from_url(UpperCAmelCase ,					map_location='cpu' ,					check_hash=UpperCAmelCase		)
      __A    		=     state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

      __A    		=     create_rename_keys(UpperCAmelCase ,					has_lm_head=UpperCAmelCase ,					is_semantic=UpperCAmelCase		)
      for src, dest in rename_keys:
            rename_key(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)
      read_in_q_k_v(UpperCAmelCase ,					UpperCAmelCase ,					has_lm_head=UpperCAmelCase ,					is_semantic=UpperCAmelCase		)
      if is_semantic:
            # add prefix to decoder keys
            for key, val in state_dict.copy().items():
                  __A    		=     state_dict.pop(UpperCAmelCase		)
                  if key.startswith('backbone.fpn'		):
                        __A    		=     key.replace('backbone.fpn' ,					'fpn'		)
                  __A    		=     val

    # load HuggingFace model
      if checkpoint_url[-9:-4] == "pt22k":
            __A    		=     BeitForMaskedImageModeling(UpperCAmelCase		)
      elif "ade20k" in checkpoint_url:
            __A    		=     BeitForSemanticSegmentation(UpperCAmelCase		)
      else:
            __A    		=     BeitForImageClassification(UpperCAmelCase		)
      model.eval()
      model.load_state_dict(UpperCAmelCase		)

      # Check outputs on an image
      if is_semantic:
            __A    		=     BeitImageProcessor(size=config.image_size ,					do_center_crop=UpperCAmelCase		)
            __A    		=     load_dataset('hf-internal-testing/fixtures_ade20k' ,					split='test'		)
            __A    		=     Image.open(ds[0]['file']		)
      else:
            __A    		=     BeitImageProcessor(
                size=config.image_size ,					resample=PILImageResampling.BILINEAR ,					do_center_crop=UpperCAmelCase		)
            __A    		=     prepare_img()

      __A    		=     image_processor(images=UpperCAmelCase ,					return_tensors='pt'		)
      __A    		=     encoding['pixel_values']

      __A    		=     model(UpperCAmelCase		)
      __A    		=     outputs.logits

      # verify logits
      __A    		=     torch.Size([1, 1_0_0_0]		)
      if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'		):
            __A    		=     torch.Size([1, 1_9_6, 8_1_9_2]		)
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'		):
            __A    		=     torch.Size([1, 1_9_6, 8_1_9_2]		)
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'		):
            __A    		=     torch.Size([1, 2_1_8_4_1]		)
            __A    		=     torch.tensor([2.2288, 2.4671, 0.7395]		)
            __A    		=     2_3_9_7
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'		):
            __A    		=     torch.Size([1, 2_1_8_4_1]		)
            __A    		=     torch.tensor([1.6881, -0.2787, 0.5901]		)
            __A    		=     2_3_9_6
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'		):
            __A    		=     torch.tensor([0.1241, 0.0798, -0.6569]		)
            __A    		=     2_8_5
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'		):
            __A    		=     torch.tensor([-1.2385, -1.0987, -1.0108]		)
            __A    		=     2_8_1
      elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'		):
            __A    		=     torch.tensor([-1.5303, -0.9484, -0.3147]		)
            __A    		=     7_6_1
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'		):
            __A    		=     torch.tensor([0.4610, -0.0928, 0.2086]		)
            __A    		=     7_6_1
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'		):
            __A    		=     torch.tensor([-0.4804, 0.6257, -0.1837]		)
            __A    		=     7_6_1
      elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'		):
            __A    		=     torch.tensor([[-0.5122, 0.5117, -0.2113]]		)
            __A    		=     7_6_1
      elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'		):
            __A    		=     torch.tensor([-0.3062, 0.7261, 0.4852]		)
            __A    		=     7_6_1
      elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'		):
            __A    		=     (1, 1_5_0, 1_6_0, 1_6_0)
            __A    		=     torch.tensor(
                [
                    [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                    [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                    [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
                ]		)
      elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'		):
            __A    		=     (1, 1_5_0, 1_6_0, 1_6_0)
            __A    		=     torch.tensor(
                [
                    [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
                    [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
                    [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
                ]		)
      else:
            raise ValueError('Can\'t verify logits as model is not supported'		)

      assert logits.shape == expected_shape, "Shape of logits not as expected"
      if not has_lm_head:
            if is_semantic:
                  assert torch.allclose(
                      logits[0, :3, :3, :3] ,					UpperCAmelCase ,					atol=1e-3		), "First elements of logits not as expected"
            else:
                  print('Predicted class idx:' ,					logits.argmax(-1		).item()		)
                  assert torch.allclose(
                      logits[0, :3] ,					UpperCAmelCase ,					atol=1e-3		), "First elements of logits not as expected"
                  assert logits.argmax(-1		).item() == expected_class_idx, "Predicted class index not as expected"

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


if __name__ == "__main__":
 a__						:				List[Any]           =    argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_url",
     default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.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."
 )
 a__						:				int           =    parser.parse_args()
 convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








'''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'     )






