





'''simple docstring'''




import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str		)		->  Optional[int]:
   snake_case 						=   SwinConfig.from_pretrained(
       """microsoft/swin-tiny-patch4-window7-224"""      ,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]		)
   snake_case 						=   MaskFormerConfig(backbone_config=__lowerCAmelCase		)

   snake_case 						=   """huggingface/label-files"""
   if "ade20k-full" in model_name:
      # this should be ok
      snake_case 						=   8_47
      snake_case 						=   """maskformer-ade20k-full-id2label.json"""
   elif "ade" in model_name:
      # this should be ok
      snake_case 						=   1_50
      snake_case 						=   """ade20k-id2label.json"""
   elif "coco-stuff" in model_name:
      # this should be ok
      snake_case 						=   1_71
      snake_case 						=   """maskformer-coco-stuff-id2label.json"""
   elif "coco" in model_name:
      # TODO
      snake_case 						=   1_33
      snake_case 						=   """coco-panoptic-id2label.json"""
   elif "cityscapes" in model_name:
      # this should be ok
      snake_case 						=   19
      snake_case 						=   """cityscapes-id2label.json"""
   elif "vistas" in model_name:
      # this should be ok
      snake_case 						=   65
      snake_case 						=   """mapillary-vistas-id2label.json"""

   snake_case 						=   json.load(open(hf_hub_download(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)      ,    """r"""		)		)
   snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}

   return config





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any		)		->  Any:
   snake_case 						=   []
   # stem
   # fmt: off
   rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")		)
   rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")		)
   rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")		)
   rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")		)
   # stages
   for i in range(len(config.backbone_config.depths		)		):
      for j in range(config.backbone_config.depths[i]		):
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')		)
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')		)
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')		)
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')		)
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')		)
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')		)
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')		)
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')		)
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')		)
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')		)
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')		)
         rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')		)

      if i < 3:
         rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')		)
         rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')		)
         rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')		)
      rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')		)
      rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')		)

   # FPN
   rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")		)
   rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")		)
   rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")		)
   for source_index, target_index in zip(range(3      ,    0      ,    -1		)      ,    range(0      ,    3		)		):
      rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')		)
      rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')		)
      rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')		)
      rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')		)
      rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')		)
      rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')		)
   rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")		)
   rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")		)

   # Transformer decoder
   for idx in range(config.decoder_config.decoder_layers		):
      # self-attention out projection
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')		)
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')		)
      # cross-attention out projection
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')		)
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')		)
      # MLP 1
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')		)
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')		)
      # MLP 2
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')		)
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')		)
      # layernorm 1 (self-attention layernorm)
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')		)
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')		)
      # layernorm 2 (cross-attention layernorm)
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')		)
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')		)
      # layernorm 3 (final layernorm)
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')		)
      rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')		)

   rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")		)
   rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")		)

   # heads on top
   rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")		)

   rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")		)
   rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")		)

   rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")		)
   rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")		)

   for i in range(3		):
      rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')		)
      rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')		)
   # fmt: on

   return rename_keys





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							str		)		->  Tuple:
   snake_case 						=   dct.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Union[str, Any]		)		->  Tuple:
   snake_case 						=   [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
   for i in range(len(backbone_config.depths		)		):
      snake_case 						=   num_features[i]
      for j in range(backbone_config.depths[i]		):
         # fmt: off
         # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
         snake_case 						=   state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''		)
         snake_case 						=   state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''		)
         # next, add query, keys and values (in that order) to the state dict
         snake_case 						=   in_proj_weight[:dim, :]
         snake_case 						=   in_proj_bias[: dim]
         snake_case 						=   in_proj_weight[
             dim : dim * 2, :
         ]
         snake_case 						=   in_proj_bias[
             dim : dim * 2
         ]
         snake_case 						=   in_proj_weight[
             -dim :, :
         ]
         snake_case 						=   in_proj_bias[-dim :]
         # fmt: on





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[Any]      ,    __lowerCAmelCase       :							int		)		->  Any:
   # fmt: off
   snake_case 						=   config.decoder_config.hidden_size
   for idx in range(config.decoder_config.decoder_layers		):
      # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
      snake_case 						=   state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''		)
      snake_case 						=   state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''		)
      # next, add query, keys and values (in that order) to the state dict
      snake_case 						=   in_proj_weight[: hidden_size, :]
      snake_case 						=   in_proj_bias[:config.hidden_size]
      snake_case 						=   in_proj_weight[hidden_size : hidden_size * 2, :]
      snake_case 						=   in_proj_bias[hidden_size : hidden_size * 2]
      snake_case 						=   in_proj_weight[-hidden_size :, :]
      snake_case 						=   in_proj_bias[-hidden_size :]
      # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
      snake_case 						=   state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''		)
      snake_case 						=   state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''		)
      # next, add query, keys and values (in that order) to the state dict
      snake_case 						=   in_proj_weight[: hidden_size, :]
      snake_case 						=   in_proj_bias[:config.hidden_size]
      snake_case 						=   in_proj_weight[hidden_size : hidden_size * 2, :]
      snake_case 						=   in_proj_bias[hidden_size : hidden_size * 2]
      snake_case 						=   in_proj_weight[-hidden_size :, :]
      snake_case 						=   in_proj_bias[-hidden_size :]
   # fmt: on





def 	__lowerCamelCase			(      )		->  torch.Tensor:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)
   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							bool = False		)		->  List[Any]:
   snake_case 						=   get_maskformer_config(__lowerCAmelCase		)

   # load original state_dict
   with open(__lowerCAmelCase      ,    """rb"""		) as f:
      snake_case 						=   pickle.load(__lowerCAmelCase		)
   snake_case 						=   data["""model"""]

   # for name, param in state_dict.items():
   #     print(name, param.shape)

   # rename keys
   snake_case 						=   create_rename_keys(__lowerCAmelCase		)
   for src, dest in rename_keys:
      rename_key(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   read_in_swin_q_k_v(__lowerCAmelCase      ,    config.backbone_config		)
   read_in_decoder_q_k_v(__lowerCAmelCase      ,    __lowerCAmelCase		)

   # update to torch tensors
   for key, value in state_dict.items():
      snake_case 						=   torch.from_numpy(__lowerCAmelCase		)

   # load 🤗 model
   snake_case 						=   MaskFormerForInstanceSegmentation(__lowerCAmelCase		)
   model.eval()

   for name, param in model.named_parameters():
      print(__lowerCAmelCase      ,    param.shape		)

   snake_case						,    snake_case 						=   model.load_state_dict(__lowerCAmelCase      ,    strict=__lowerCAmelCase		)
   assert missing_keys == [
       "model.pixel_level_module.encoder.model.layernorm.weight",
       "model.pixel_level_module.encoder.model.layernorm.bias",
   ]
   assert len(__lowerCAmelCase		) == 0, F'''Unexpected keys: {unexpected_keys}'''

   # verify results
   snake_case 						=   prepare_img()
   if "vistas" in model_name:
      snake_case 						=   65
   elif "cityscapes" in model_name:
      snake_case 						=   6_55_35
   else:
      snake_case 						=   2_55
   snake_case 						=   True if """ade""" in model_name else False
   snake_case 						=   MaskFormerImageProcessor(ignore_index=__lowerCAmelCase      ,    reduce_labels=__lowerCAmelCase		)

   snake_case 						=   image_processor(__lowerCAmelCase      ,    return_tensors="""pt"""		)

   snake_case 						=   model(**__lowerCAmelCase		)

   print("""Logits:"""      ,    outputs.class_queries_logits[0, :3, :3]		)

   if model_name == "maskformer-swin-tiny-ade":
      snake_case 						=   torch.tensor(
          [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]		)
   assert torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,    __lowerCAmelCase      ,    atol=1e-4		)
   print("""Looks ok!"""		)

   if pytorch_dump_folder_path is not None:
      print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''		)
      Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
      model.save_pretrained(__lowerCAmelCase		)
      image_processor.save_pretrained(__lowerCAmelCase		)

   if push_to_hub:
      print("""Pushing model and image processor to the hub..."""		)
      model.push_to_hub(F'''nielsr/{model_name}'''		)
      image_processor.push_to_hub(F'''nielsr/{model_name}'''		)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="maskformer-swin-tiny-ade",
     type=str,
     help=("Name of the MaskFormer model you'd like to convert",),
 )
 parser.add_argument(
     "--checkpoint_path",
     default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
     type=str,
     help="Path to the original state dict (.pth file).",
 )
 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 or not to push the converted model to the 🤗 hub."
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )








'''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"""






     snake_case_						      =   DebertaTokenizer
     snake_case_						      =   True
     snake_case_						      =   DebertaTokenizerFast



     def      lowerCAmelCase			(   self							:			Any						)->	Optional[int]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        snake_case 						=   [
            """l""",
            """o""",
            """w""",
            """e""",
            """r""",
            """s""",
            """t""",
            """i""",
            """d""",
            """n""",
            """\u0120""",
            """\u0120l""",
            """\u0120n""",
            """\u0120lo""",
            """\u0120low""",
            """er""",
            """\u0120lowest""",
            """\u0120newer""",
            """\u0120wider""",
            """[UNK]""",
        ]
        snake_case 						=   dict(zip(__snake_case   ,       range(len(__snake_case						)						)						)						)
        snake_case 						=   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
        snake_case 						=   {"""unk_token""": """[UNK]"""}

        snake_case 						=   os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
        snake_case 						=   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(__snake_case						) + """\n"""						)
        with open(self.merges_file   ,       """w"""   ,       encoding="""utf-8"""						) as fp:
           fp.write("""\n""".join(__snake_case						)						)



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       **__snake_case							:			Union[str, Any]						)->	int:
        kwargs.update(self.special_tokens_map						)
        return self.tokenizer_class.from_pretrained(self.tmpdirname   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			int						)->	Any:
        snake_case 						=   """lower newer"""
        snake_case 						=   """lower newer"""
        return input_text, output_text



     def      lowerCAmelCase			(   self							:			str						)->	Any:
        snake_case 						=   self.get_tokenizer()
        snake_case 						=   """lower newer"""
        snake_case 						=   ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
        snake_case 						=   tokenizer.tokenize(__snake_case						)
        self.assertListEqual(__snake_case   ,       __snake_case						)

        snake_case 						=   tokens + [tokenizer.unk_token]
        snake_case 						=   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case						)   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			Any						)->	List[str]:
        snake_case 						=   self.get_tokenizer()
        snake_case 						=   tokenizer("""Hello"""   ,       """World"""						)
        snake_case 						=   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd["""token_type_ids"""]   ,       __snake_case						)



     @slow
     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Union[str, Any]:
        snake_case 						=   self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""						)

        snake_case 						=   tokenizer.encode("""sequence builders"""   ,       add_special_tokens=__snake_case						)
        snake_case 						=   tokenizer.encode("""multi-sequence build"""   ,       add_special_tokens=__snake_case						)

        snake_case 						=   tokenizer.encode(
            """sequence builders"""   ,       add_special_tokens=__snake_case   ,       add_prefix_space=__snake_case						)
        snake_case 						=   tokenizer.encode(
            """sequence builders"""   ,       """multi-sequence build"""   ,       add_special_tokens=__snake_case   ,       add_prefix_space=__snake_case						)

        snake_case 						=   tokenizer.build_inputs_with_special_tokens(__snake_case						)
        snake_case 						=   tokenizer.build_inputs_with_special_tokens(__snake_case   ,       __snake_case						)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode



     @slow
     def      lowerCAmelCase			(   self							:			Any						)->	Union[str, Any]:
        snake_case 						=   [self.tokenizer_class]
        if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class						)

        for tokenizer_class in tokenizer_classes:
           snake_case 						=   tokenizer_class.from_pretrained("""microsoft/deberta-base"""						)

           snake_case 						=   [
               """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.""",
           ]

           snake_case 						=   tokenizer(__snake_case   ,       padding=__snake_case						)
           snake_case 						=   [tokenizer.decode(__snake_case   ,       skip_special_tokens=__snake_case						) for seq in encoding["""input_ids"""]]

           # fmt: off
           snake_case 						=   {
               """input_ids""": [
                   [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 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

           snake_case 						=   [
               """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   ,       __snake_case						)

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

