





'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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





def 						UpperCamelCase				(							_lowerCamelCase					:					Any       ):
    A__				=			DPTConfig()

    if "large" in checkpoint_url:
        A__				=			10_24
        A__				=			40_96
        A__				=			24
        A__				=			16
        A__				=			[5, 11, 17, 23]
        A__				=			[2_56, 5_12, 10_24, 10_24]
        A__				=			(1, 3_84, 3_84)

    if "ade" in checkpoint_url:
        A__				=			True

        A__				=			1_50
        A__				=			"huggingface/label-files"
        A__				=			"ade20k-id2label.json"
        A__				=			json.load(open(cached_download(hf_hub_url(__snake_case						,       __snake_case						,       repo_type="dataset"       )       )						,       "r"       )       )
        A__				=			{int(__snake_case       ): v for k, v in idalabel.items()}
        A__				=			idalabel
        A__				=			{v: k for k, v in idalabel.items()}
        A__				=			[1, 1_50, 4_80, 4_80]

    return config, expected_shape





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]       ):
    A__				=			["pretrained.model.head.weight", "pretrained.model.head.bias"]
    for k in ignore_keys:
        state_dict.pop(__snake_case						,       __snake_case       )





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]       ):
    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        A__				=			name.replace("pretrained.model"						,       "dpt.encoder"       )
    if "pretrained.model" in name:
        A__				=			name.replace("pretrained.model"						,       "dpt.embeddings"       )
    if "patch_embed" in name:
        A__				=			name.replace("patch_embed"						,       "patch_embeddings"       )
    if "pos_embed" in name:
        A__				=			name.replace("pos_embed"						,       "position_embeddings"       )
    if "attn.proj" in name:
        A__				=			name.replace("attn.proj"						,       "attention.output.dense"       )
    if "proj" in name and "project" not in name:
        A__				=			name.replace("proj"						,       "projection"       )
    if "blocks" in name:
        A__				=			name.replace("blocks"						,       "layer"       )
    if "mlp.fc1" in name:
        A__				=			name.replace("mlp.fc1"						,       "intermediate.dense"       )
    if "mlp.fc2" in name:
        A__				=			name.replace("mlp.fc2"						,       "output.dense"       )
    if "norm1" in name:
        A__				=			name.replace("norm1"						,       "layernorm_before"       )
    if "norm2" in name:
        A__				=			name.replace("norm2"						,       "layernorm_after"       )
    if "scratch.output_conv" in name:
        A__				=			name.replace("scratch.output_conv"						,       "head"       )
    if "scratch" in name:
        A__				=			name.replace("scratch"						,       "neck"       )
    if "layer1_rn" in name:
        A__				=			name.replace("layer1_rn"						,       "convs.0"       )
    if "layer2_rn" in name:
        A__				=			name.replace("layer2_rn"						,       "convs.1"       )
    if "layer3_rn" in name:
        A__				=			name.replace("layer3_rn"						,       "convs.2"       )
    if "layer4_rn" in name:
        A__				=			name.replace("layer4_rn"						,       "convs.3"       )
    if "refinenet" in name:
        A__				=			int(name[len("neck.refinenet"       ) : len("neck.refinenet"       ) + 1]       )
        # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
        A__				=			name.replace(F"refinenet{layer_idx}"						,       F"fusion_stage.layers.{abs(layer_idx-4       )}"       )
    if "out_conv" in name:
        A__				=			name.replace("out_conv"						,       "projection"       )
    if "resConfUnit1" in name:
        A__				=			name.replace("resConfUnit1"						,       "residual_layer1"       )
    if "resConfUnit2" in name:
        A__				=			name.replace("resConfUnit2"						,       "residual_layer2"       )
    if "conv1" in name:
        A__				=			name.replace("conv1"						,       "convolution1"       )
    if "conv2" in name:
        A__				=			name.replace("conv2"						,       "convolution2"       )
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        A__				=			name.replace("pretrained.act_postprocess1.0.project.0"						,       "neck.reassemble_stage.readout_projects.0.0"       )
    if "pretrained.act_postprocess2.0.project.0" in name:
        A__				=			name.replace("pretrained.act_postprocess2.0.project.0"						,       "neck.reassemble_stage.readout_projects.1.0"       )
    if "pretrained.act_postprocess3.0.project.0" in name:
        A__				=			name.replace("pretrained.act_postprocess3.0.project.0"						,       "neck.reassemble_stage.readout_projects.2.0"       )
    if "pretrained.act_postprocess4.0.project.0" in name:
        A__				=			name.replace("pretrained.act_postprocess4.0.project.0"						,       "neck.reassemble_stage.readout_projects.3.0"       )
    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        A__				=			name.replace("pretrained.act_postprocess1.3"						,       "neck.reassemble_stage.layers.0.projection"       )
    if "pretrained.act_postprocess1.4" in name:
        A__				=			name.replace("pretrained.act_postprocess1.4"						,       "neck.reassemble_stage.layers.0.resize"       )
    if "pretrained.act_postprocess2.3" in name:
        A__				=			name.replace("pretrained.act_postprocess2.3"						,       "neck.reassemble_stage.layers.1.projection"       )
    if "pretrained.act_postprocess2.4" in name:
        A__				=			name.replace("pretrained.act_postprocess2.4"						,       "neck.reassemble_stage.layers.1.resize"       )
    if "pretrained.act_postprocess3.3" in name:
        A__				=			name.replace("pretrained.act_postprocess3.3"						,       "neck.reassemble_stage.layers.2.projection"       )
    if "pretrained.act_postprocess4.3" in name:
        A__				=			name.replace("pretrained.act_postprocess4.3"						,       "neck.reassemble_stage.layers.3.projection"       )
    if "pretrained.act_postprocess4.4" in name:
        A__				=			name.replace("pretrained.act_postprocess4.4"						,       "neck.reassemble_stage.layers.3.resize"       )
    if "pretrained" in name:
        A__				=			name.replace("pretrained"						,       "dpt"       )
    if "bn" in name:
        A__				=			name.replace("bn"						,       "batch_norm"       )
    if "head" in name:
        A__				=			name.replace("head"						,       "head.head"       )
    if "encoder.norm" in name:
        A__				=			name.replace("encoder.norm"						,       "layernorm"       )
    if "auxlayer" in name:
        A__				=			name.replace("auxlayer"						,       "auxiliary_head.head"       )

    return name





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					str       ):
    for i in range(config.num_hidden_layers       ):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        A__				=			state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"       )
        A__				=			state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias"       )
        # next, add query, keys and values (in that order) to the state dict
        A__				=			in_proj_weight[: config.hidden_size, :]
        A__				=			in_proj_bias[: config.hidden_size]
        A__				=			in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        A__				=			in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        A__				=			in_proj_weight[
            -config.hidden_size :, :
        ]
        A__				=			in_proj_bias[-config.hidden_size :]





def 						UpperCamelCase				(							):
    A__				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
    A__				=			Image.open(requests.get(__snake_case						,       stream=__snake_case       ).raw       )
    return im





@torch.no_grad()
def 						UpperCamelCase				(							_lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					str						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					Tuple       ):
    A__				=			get_dpt_config(__snake_case       )
    # load original state_dict from URL
    A__				=			torch.hub.load_state_dict_from_url(__snake_case						,       map_location="cpu"       )
    # remove certain keys
    remove_ignore_keys_(__snake_case       )
    # rename keys
    for key in state_dict.copy().keys():
        A__				=			state_dict.pop(__snake_case       )
        A__				=			val
    # read in qkv matrices
    read_in_q_k_v(__snake_case						,       __snake_case       )

    # load HuggingFace model
    A__				=			DPTForSemanticSegmentation(__snake_case       ) if "ade" in checkpoint_url else DPTForDepthEstimation(__snake_case       )
    model.load_state_dict(__snake_case       )
    model.eval()

    # Check outputs on an image
    A__				=			4_80 if "ade" in checkpoint_url else 3_84
    A__				=			DPTImageProcessor(size=__snake_case       )

    A__				=			prepare_img()
    A__				=			image_processor(__snake_case						,       return_tensors="pt"       )

    # forward pass
    A__				=			model(**__snake_case       ).logits if "ade" in checkpoint_url else model(**__snake_case       ).predicted_depth

    # Assert logits
    A__				=			torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]       )
    if "ade" in checkpoint_url:
        A__				=			torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]       )
    assert outputs.shape == torch.Size(__snake_case       )
    assert (
        torch.allclose(outputs[0, 0, :3, :3]						,       __snake_case						,       atol=1e-4       )
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3]						,       __snake_case       )
    )

    Path(__snake_case       ).mkdir(exist_ok=__snake_case       )
    print(F"Saving model to {pytorch_dump_folder_path}"       )
    model.save_pretrained(__snake_case       )
    print(F"Saving image processor to {pytorch_dump_folder_path}"       )
    image_processor.save_pretrained(__snake_case       )

    if push_to_hub:
        print("Pushing model to hub..."       )
        model.push_to_hub(
            repo_path_or_name=Path(__snake_case						,       __snake_case       )						,       organization="nielsr"						,       commit_message="Add model"						,       use_temp_dir=__snake_case						,       )
        image_processor.push_to_hub(
            repo_path_or_name=Path(__snake_case						,       __snake_case       )						,       organization="nielsr"						,       commit_message="Add image processor"						,       use_temp_dir=__snake_case						,       )


if __name__ == "__main__":
  __lowerCAmelCase							:	Dict			=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
      type=str,
      help="URL of the original DPT checkpoint you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default=None,
      type=str,
      required=True,
      help="Path to the output PyTorch model directory.",
  )
  parser.add_argument(
      "--push_to_hub",
      action="store_true",
  )
  parser.add_argument(
      "--model_name",
      default="dpt-large",
      type=str,
      help="Name of the model, in case you're pushing to the hub.",
  )

  __lowerCAmelCase							:	Dict			=parser.parse_args()
  convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





"""simple docstring"""




import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_A			:       int   		=       get_tests_dir("""fixtures/spiece.model""")

@require_sentencepiece
@require_tokenizers
class 		a__						(      a_,			unittest.TestCase						):
   __lowerCAmelCase            =				DebertaVaTokenizer
   __lowerCAmelCase            =				DebertaVaTokenizerFast
   __lowerCAmelCase            =				True
   __lowerCAmelCase            =				True
   def 			__magic_name__			(			self				):
          super().setUp()

          # We have a SentencePiece fixture for testing
          lowercase    :				Any       		=							DebertaVaTokenizer(_a	,    unk_token="<unk>"				)
          tokenizer.save_pretrained(self.tmpdirname				)
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				int       		=							"this is a test"
          lowercase    :				Tuple       		=							"this is a test"
          return input_text, output_text
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							"<pad>"
          lowercase    :				List[str]       		=							0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a				)	,    _a				)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a				)	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							list(self.get_tokenizer().get_vocab().keys()				)
          self.assertEqual(vocab_keys[0]	,    "<pad>"				)
          self.assertEqual(vocab_keys[1]	,    "<unk>"				)
          self.assertEqual(vocab_keys[-1]	,    "[PAD]"				)
          self.assertEqual(len(_a				)	,    30_001				)
   def 			__magic_name__			(			self				):
          self.assertEqual(self.get_tokenizer().vocab_size	,    30_000				)
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				List[str]       		=							" \tHeLLo!how  \n Are yoU?  "
          lowercase    :				str       		=							["▁hello", "!", "how", "▁are", "▁you", "?"]
          # fmt: on

          lowercase    :				Union[str, Any]       		=							DebertaVaTokenizer(_a	,    do_lower_case=_a				)
          lowercase    :				Any       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)

          lowercase    :				str       		=							DebertaVaTokenizerFast(_a	,    do_lower_case=_a				)
          lowercase    :				Optional[Any]       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)
   @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				Optional[Any]       		=							"I was born in 92000, and this is falsé."
          lowercase    :				Tuple       		=							["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
          # fmt: on

          lowercase    :				List[Any]       		=							DebertaVaTokenizer(_a	,    split_by_punct=_a				)
          lowercase    :				Tuple       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)

          lowercase    :				Union[str, Any]       		=							DebertaVaTokenizerFast(_a	,    split_by_punct=_a				)
          lowercase    :				Any       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				int       		=							"I was born in 92000, and this is falsé."
          lowercase    :				Tuple       		=							["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
          # fmt: on

          lowercase    :				List[str]       		=							DebertaVaTokenizer(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Optional[Any]       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)
          self.assertListEqual(_a	,    _a				)

          lowercase    :				Union[str, Any]       		=							DebertaVaTokenizerFast(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Optional[int]       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)
          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				List[Any]       		=							"I was born in 92000, and this is falsé."
          lowercase    :				Optional[int]       		=							["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
          # fmt: on

          lowercase    :				List[Any]       		=							DebertaVaTokenizer(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Optional[int]       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)

          lowercase    :				Optional[int]       		=							DebertaVaTokenizerFast(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Dict       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				int       		=							"I was born in 92000, and this is falsé."
          lowercase    :				Dict       		=							["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
          # fmt: on

          lowercase    :				Union[str, Any]       		=							DebertaVaTokenizer(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Any       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)

          lowercase    :				Union[str, Any]       		=							DebertaVaTokenizerFast(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				Dict       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				Dict       		=							" \tHeLLo!how  \n Are yoU?  "
          lowercase    :				str       		=							["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
          # fmt: on

          lowercase    :				Optional[int]       		=							DebertaVaTokenizer(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				int       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)

          lowercase    :				List[str]       		=							DebertaVaTokenizerFast(_a	,    do_lower_case=_a	,    split_by_punct=_a				)
          lowercase    :				List[Any]       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)

          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.get_tokenizer()
          lowercase    :				Dict       		=							self.get_rust_tokenizer()

          lowercase    :				str       		=							"I was born in 92000, and this is falsé."

          lowercase    :				Optional[Any]       		=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_a	,    add_special_tokens=_a				)				)
          lowercase    :				Dict       		=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_a	,    add_special_tokens=_a				)				)
          self.assertListEqual(_a	,    _a				)

          lowercase    :				str       		=							tokenizer.encode(_a	,    add_special_tokens=_a				)
          lowercase    :				Dict       		=							rust_tokenizer.encode(_a	,    add_special_tokens=_a				)
          self.assertListEqual(_a	,    _a				)

          lowercase    :				Optional[int]       		=							self.get_rust_tokenizer()
          lowercase    :				Tuple       		=							tokenizer.encode(_a				)
          lowercase    :				List[str]       		=							rust_tokenizer.encode(_a				)
          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							"This is a test"
          lowercase    :				Tuple       		=							[13, 1, 4_398, 25, 21, 1_289]
          lowercase    :				Optional[int]       		=							["▁", "T", "his", "▁is", "▁a", "▁test"]
          lowercase    :				Optional[Any]       		=							["▁", "<unk>", "his", "▁is", "▁a", "▁test"]

          lowercase    :				Any       		=							DebertaVaTokenizer(_a	,    keep_accents=_a				)
          lowercase    :				Dict       		=							DebertaVaTokenizerFast(_a	,    keep_accents=_a				)

          lowercase    :				str       		=							tokenizer.encode(_a	,    add_special_tokens=_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				str       		=							tokenizer.tokenize(_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				str       		=							tokenizer.convert_ids_to_tokens(_a				)
          self.assertListEqual(_a	,    _a				)

          lowercase    :				int       		=							rust_tokenizer.encode(_a	,    add_special_tokens=_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				Any       		=							rust_tokenizer.tokenize(_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				Union[str, Any]       		=							rust_tokenizer.convert_ids_to_tokens(_a				)
          self.assertListEqual(_a	,    _a				)

          # fmt: off
          lowercase    :				int       		=							"I was born in 92000, and this is falsé."
          lowercase    :				Any       		=							[13, 1, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]
          lowercase    :				List[str]       		=							["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
          lowercase    :				str       		=							["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
          # fmt: on

          lowercase    :				Tuple       		=							tokenizer.encode(_a	,    add_special_tokens=_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				List[Any]       		=							tokenizer.tokenize(_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				str       		=							tokenizer.convert_ids_to_tokens(_a				)
          self.assertListEqual(_a	,    _a				)

          lowercase    :				Optional[int]       		=							rust_tokenizer.encode(_a	,    add_special_tokens=_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				List[Any]       		=							rust_tokenizer.tokenize(_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				List[Any]       		=							rust_tokenizer.convert_ids_to_tokens(_a				)
          self.assertListEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							DebertaVaTokenizer(_a				)

          lowercase    :				List[Any]       		=							tokenizer.encode("sequence builders"				)
          lowercase    :				Dict       		=							tokenizer.encode("multi-sequence build"				)

          lowercase    :				List[Any]       		=							tokenizer.build_inputs_with_special_tokens(_a				)
          lowercase    :				Dict       		=							tokenizer.build_inputs_with_special_tokens(_a	,    _a				)

          self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]	,    _a				)
          self.assertEqual(
              [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]	,    _a	,    )

   @slow
   def 			__magic_name__			(			self				):
          # fmt: off
          lowercase    :				Dict       		=							{"input_ids": [[1, 39_867, 36, 19_390, 486, 27, 35_052, 81_436, 18, 60_685, 1_225, 7, 35_052, 81_436, 18, 9_367, 16_899, 18, 15_937, 53, 594, 773, 18, 16_287, 30_465, 36, 15_937, 6, 41_139, 38, 36_979, 60_763, 191, 6, 34_132, 99, 6, 50_538, 390, 43_230, 6, 34_132, 2_779, 20_850, 14, 699, 1_072, 1_194, 36, 382, 10_901, 53, 7, 699, 1_072, 2_084, 36, 20_422, 630, 53, 19, 105, 3_049, 1_896, 1_053, 16_899, 1_506, 11, 37_978, 4_243, 7, 1_237, 31_869, 200, 16_566, 654, 6, 35_052, 81_436, 7, 55_630, 13_593, 4, 2], [1, 26, 15_011, 13, 667, 8, 1_053, 18, 23_611, 1_237, 72_356, 12_820, 34, 104_134, 1_209, 35, 13_313, 6_627, 21, 202, 347, 7, 164, 2_399, 11, 46, 4_485, 4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1_232, 2_864, 15_785, 14_951, 105, 5, 8_581, 1_250, 4, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=_a	,    model_name="microsoft/deberta-v2-xlarge"	,    revision="ad6e42c1532ddf3a15c39246b63f5559d558b670"	,    )


