



"""simple docstring"""





import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase      =		logging.get_logger(__name__)
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__=False   ):
      UpperCAmelCase_    				=							OrderedDict()
      for key, value in state_dict.items():
            if encoder_only and not key.startswith("head"   ):
                  UpperCAmelCase_    				=							"segformer.encoder." + key
            if key.startswith("backbone"   ):
                  UpperCAmelCase_    				=							key.replace("backbone"			,    "segformer.encoder"   )
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  UpperCAmelCase_    				=							key[key.find("patch_embed"   ) + len("patch_embed"   )]
                  UpperCAmelCase_    				=							key.replace(f"""patch_embed{idx}"""			,    f"""patch_embeddings.{int(lowerCAmelCase__   )-1}"""   )
            if "norm" in key:
                  UpperCAmelCase_    				=							key.replace("norm"			,    "layer_norm"   )
            if "segformer.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  UpperCAmelCase_    				=							key[key.find("segformer.encoder.layer_norm"   ) + len("segformer.encoder.layer_norm"   )]
                  UpperCAmelCase_    				=							key.replace(f"""layer_norm{idx}"""			,    f"""layer_norm.{int(lowerCAmelCase__   )-1}"""   )
            if "layer_norm1" in key:
                  UpperCAmelCase_    				=							key.replace("layer_norm1"			,    "layer_norm_1"   )
            if "layer_norm2" in key:
                  UpperCAmelCase_    				=							key.replace("layer_norm2"			,    "layer_norm_2"   )
            if "block" in key:
                  # replace for example block1 by block.0
                  UpperCAmelCase_    				=							key[key.find("block"   ) + len("block"   )]
                  UpperCAmelCase_    				=							key.replace(f"""block{idx}"""			,    f"""block.{int(lowerCAmelCase__   )-1}"""   )
            if "attn.q" in key:
                  UpperCAmelCase_    				=							key.replace("attn.q"			,    "attention.self.query"   )
            if "attn.proj" in key:
                  UpperCAmelCase_    				=							key.replace("attn.proj"			,    "attention.output.dense"   )
            if "attn" in key:
                  UpperCAmelCase_    				=							key.replace("attn"			,    "attention.self"   )
            if "fc1" in key:
                  UpperCAmelCase_    				=							key.replace("fc1"			,    "dense1"   )
            if "fc2" in key:
                  UpperCAmelCase_    				=							key.replace("fc2"			,    "dense2"   )
            if "linear_pred" in key:
                  UpperCAmelCase_    				=							key.replace("linear_pred"			,    "classifier"   )
            if "linear_fuse" in key:
                  UpperCAmelCase_    				=							key.replace("linear_fuse.conv"			,    "linear_fuse"   )
                  UpperCAmelCase_    				=							key.replace("linear_fuse.bn"			,    "batch_norm"   )
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  UpperCAmelCase_    				=							key[key.find("linear_c"   ) + len("linear_c"   )]
                  UpperCAmelCase_    				=							key.replace(f"""linear_c{idx}"""			,    f"""linear_c.{int(lowerCAmelCase__   )-1}"""   )
            if key.startswith("head"   ):
                  UpperCAmelCase_    				=							key.replace("head"			,    "classifier"   )
            UpperCAmelCase_    				=							value

      return new_state_dict
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # for each of the encoder blocks:
      for i in range(config.num_encoder_blocks   ):
            for j in range(config.depths[i]   ):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  UpperCAmelCase_    				=							state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""   )
                  UpperCAmelCase_    				=							state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.bias"""   )
                  # next, add keys and values (in that order) to the state dict
                  UpperCAmelCase_    				=							kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCAmelCase_    				=							kv_bias[: config.hidden_sizes[i]]
                  UpperCAmelCase_    				=							kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCAmelCase_    				=							kv_bias[
                      config.hidden_sizes[i] :
                  ]
def      a__			(   ):
      UpperCAmelCase_    				=							"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_    				=							Image.open(requests.get(lowerCAmelCase__			,    stream=lowerCAmelCase__   ).raw   )

      return image
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							SegformerConfig()
      UpperCAmelCase_    				=							False

      # set attributes based on model_name
      UpperCAmelCase_    				=							"huggingface/label-files"
      if "segformer" in model_name:
            UpperCAmelCase_    				=							model_name[len("segformer."   ) : len("segformer."   ) + 2]
            if "ade" in model_name:
                  UpperCAmelCase_    				=							150
                  UpperCAmelCase_    				=							"ade20k-id2label.json"
                  UpperCAmelCase_    				=							(1, 150, 128, 128)
            elif "city" in model_name:
                  UpperCAmelCase_    				=							19
                  UpperCAmelCase_    				=							"cityscapes-id2label.json"
                  UpperCAmelCase_    				=							(1, 19, 128, 128)
            else:
                  raise ValueError(f"""Model {model_name} not supported"""   )
      elif "mit" in model_name:
            UpperCAmelCase_    				=							True
            UpperCAmelCase_    				=							model_name[4:6]
            UpperCAmelCase_    				=							1000
            UpperCAmelCase_    				=							"imagenet-1k-id2label.json"
            UpperCAmelCase_    				=							(1, 1000)
      else:
            raise ValueError(f"""Model {model_name} not supported"""   )

      # set config attributes
      UpperCAmelCase_    				=							json.load(open(hf_hub_download(lowerCAmelCase__			,    lowerCAmelCase__			,    repo_type="dataset"   )			,    "r"   )   )
      UpperCAmelCase_    				=							{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
      UpperCAmelCase_    				=							idalabel
      UpperCAmelCase_    				=							{v: k for k, v in idalabel.items()}
      if size == "b0":
            pass
      elif size == "b1":
            UpperCAmelCase_    				=							[64, 128, 320, 512]
            UpperCAmelCase_    				=							256
      elif size == "b2":
            UpperCAmelCase_    				=							[64, 128, 320, 512]
            UpperCAmelCase_    				=							768
            UpperCAmelCase_    				=							[3, 4, 6, 3]
      elif size == "b3":
            UpperCAmelCase_    				=							[64, 128, 320, 512]
            UpperCAmelCase_    				=							768
            UpperCAmelCase_    				=							[3, 4, 18, 3]
      elif size == "b4":
            UpperCAmelCase_    				=							[64, 128, 320, 512]
            UpperCAmelCase_    				=							768
            UpperCAmelCase_    				=							[3, 8, 27, 3]
      elif size == "b5":
            UpperCAmelCase_    				=							[64, 128, 320, 512]
            UpperCAmelCase_    				=							768
            UpperCAmelCase_    				=							[3, 6, 40, 3]
      else:
            raise ValueError(f"""Size {size} not supported"""   )

      # load image processor (only resize + normalize)
      UpperCAmelCase_    				=							SegformerImageProcessor(
          image_scale=(512, 512)			,    keep_ratio=lowerCAmelCase__			,    align=lowerCAmelCase__			,    do_random_crop=lowerCAmelCase__   )

      # prepare image
      UpperCAmelCase_    				=							prepare_img()
      UpperCAmelCase_    				=							image_processor(images=lowerCAmelCase__			,    return_tensors="pt"   ).pixel_values

      logger.info(f"""Converting model {model_name}..."""   )

      # load original state dict
      if encoder_only:
            UpperCAmelCase_    				=							torch.load(lowerCAmelCase__			,    map_location=torch.device("cpu"   )   )
      else:
            UpperCAmelCase_    				=							torch.load(lowerCAmelCase__			,    map_location=torch.device("cpu"   )   )["state_dict"]

      # rename keys
      UpperCAmelCase_    				=							rename_keys(lowerCAmelCase__			,    encoder_only=lowerCAmelCase__   )
      if not encoder_only:
            del state_dict["decode_head.conv_seg.weight"]
            del state_dict["decode_head.conv_seg.bias"]

      # key and value matrices need special treatment
      read_in_k_v(lowerCAmelCase__			,    lowerCAmelCase__   )

      # create HuggingFace model and load state dict
      if encoder_only:
            UpperCAmelCase_    				=							False
            UpperCAmelCase_    				=							SegformerForImageClassification(lowerCAmelCase__   )
      else:
            UpperCAmelCase_    				=							SegformerForSemanticSegmentation(lowerCAmelCase__   )
      model.load_state_dict(lowerCAmelCase__   )
      model.eval()

      # forward pass
      UpperCAmelCase_    				=							model(lowerCAmelCase__   )
      UpperCAmelCase_    				=							outputs.logits

      # set expected_slice based on model name
      # ADE20k checkpoints
      if model_name == "segformer.b0.512x512.ade.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                    [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                    [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
                ]   )
      elif model_name == "segformer.b1.512x512.ade.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
                    [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
                    [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
                ]   )
      elif model_name == "segformer.b2.512x512.ade.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
                    [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
                    [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
                ]   )
      elif model_name == "segformer.b3.512x512.ade.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
                    [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
                    [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
                ]   )
      elif model_name == "segformer.b4.512x512.ade.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
                    [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
                    [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
                ]   )
      elif model_name == "segformer.b5.640x640.ade.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
                    [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
                    [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
                ]   )
      # Cityscapes checkpoints
      elif model_name == "segformer.b0.1024x1024.city.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
                    [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
                    [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
                ]   )
      elif model_name == "segformer.b0.512x1024.city.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
                    [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
                    [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
                ]   )
      elif model_name == "segformer.b0.640x1280.city.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [
                        [-1.1372e01, -1.2787e01, -1.3477e01],
                        [-1.2536e01, -1.4194e01, -1.4409e01],
                        [-1.3217e01, -1.4888e01, -1.5327e01],
                    ],
                    [
                        [-1.4791e01, -1.7122e01, -1.8277e01],
                        [-1.7163e01, -1.9192e01, -1.9533e01],
                        [-1.7897e01, -1.9991e01, -2.0315e01],
                    ],
                    [
                        [7.6723e-01, 4.1921e-01, -7.7878e-02],
                        [4.7772e-01, 9.5557e-03, -2.8082e-01],
                        [3.6032e-01, -2.4826e-01, -5.1168e-01],
                    ],
                ]   )
      elif model_name == "segformer.b0.768x768.city.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
                    [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
                    [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
                ]   )
      elif model_name == "segformer.b1.1024x1024.city.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                    [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                    [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
                ]   )
      elif model_name == "segformer.b2.1024x1024.city.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
                    [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
                    [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
                ]   )
      elif model_name == "segformer.b3.1024x1024.city.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
                    [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
                    [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
                ]   )
      elif model_name == "segformer.b4.1024x1024.city.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
                    [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
                    [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
                ]   )
      elif model_name == "segformer.b5.1024x1024.city.160k":
            UpperCAmelCase_    				=							torch.tensor(
                [
                    [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
                    [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
                    [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
                ]   )
      else:
            UpperCAmelCase_    				=							logits.argmax(-1   ).item()
            print("Predicted class:"			,    model.config.idalabel[predicted_class_idx]   )

      # verify logits
      if not encoder_only:
            assert logits.shape == expected_shape
            assert torch.allclose(logits[0, :3, :3, :3]			,    lowerCAmelCase__			,    atol=1e-2   )

      # finally, save model and image processor
      logger.info(f"""Saving PyTorch 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 __name__ == "__main__":
  lowerCamelCase      =		argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      default="""segformer.b0.512x512.ade.160k""",
      type=str,
      help="""Name of the model you'd like to convert.""",
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, help="""Path 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."""
  )
  lowerCamelCase      =		parser.parse_args()
  convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""





import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase      =		get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class    lowercase__     (					SCREAMING_SNAKE_CASE       ,      unittest.TestCase   ):







       '''simple docstring'''


       UpperCamelCase		=  XLMProphetNetTokenizer
       UpperCamelCase		=  False
       UpperCamelCase		=  True



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


             '''simple docstring'''




             super().setUp()

             # We have a SentencePiece fixture for testing
             UpperCAmelCase_    				=							XLMProphetNetTokenizer(_UpperCAmelCase	,       keep_accents=_UpperCAmelCase					)
             tokenizer.save_pretrained(self.tmpdirname					)



       def   lowercase__       (							self    :						Tuple					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							"[PAD]"
             UpperCAmelCase_    				=							0

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



       def   lowercase__       (							self    :						Dict					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							list(self.get_tokenizer().get_vocab().keys()					)

             self.assertEqual(vocab_keys[0]	,       "[PAD]"					)
             self.assertEqual(vocab_keys[1]	,       "[CLS]"					)
             self.assertEqual(vocab_keys[-1]	,       "j"					)
             self.assertEqual(len(_UpperCAmelCase					)	,       1012					)



       def   lowercase__       (							self    :						Union[str, Any]					)				->		Optional[Any]:


             '''simple docstring'''




             self.assertEqual(self.get_tokenizer().vocab_size	,       1012					)



       def   lowercase__       (							self    :						Tuple					)				->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							XLMProphetNetTokenizer(_UpperCAmelCase	,       keep_accents=_UpperCAmelCase					)

             UpperCAmelCase_    				=							tokenizer.tokenize("This is a test"					)
             self.assertListEqual(_UpperCAmelCase	,       ["▁This", "▁is", "▁a", "▁t", "est"]					)

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)	,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]	,       )

             UpperCAmelCase_    				=							tokenizer.tokenize("I was born in 92000, and this is falsé."					)
             self.assertListEqual(
                 _UpperCAmelCase	,       [
                     SPIECE_UNDERLINE + "I",
                     SPIECE_UNDERLINE + "was",
                     SPIECE_UNDERLINE + "b",
                     "or",
                     "n",
                     SPIECE_UNDERLINE + "in",
                     SPIECE_UNDERLINE + "",
                     "9",
                     "2",
                     "0",
                     "0",
                     "0",
                     ",",
                     SPIECE_UNDERLINE + "and",
                     SPIECE_UNDERLINE + "this",
                     SPIECE_UNDERLINE + "is",
                     SPIECE_UNDERLINE + "f",
                     "al",
                     "s",
                     "é",
                     ".",
                 ]	,       )
             UpperCAmelCase_    				=							tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)
             self.assertListEqual(
                 _UpperCAmelCase	,       [
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
                 ]	,       )

             UpperCAmelCase_    				=							tokenizer.convert_ids_to_tokens(_UpperCAmelCase					)
             self.assertListEqual(
                 _UpperCAmelCase	,       [
                     SPIECE_UNDERLINE + "I",
                     SPIECE_UNDERLINE + "was",
                     SPIECE_UNDERLINE + "b",
                     "or",
                     "n",
                     SPIECE_UNDERLINE + "in",
                     SPIECE_UNDERLINE + "",
                     "[UNK]",
                     "2",
                     "0",
                     "0",
                     "0",
                     ",",
                     SPIECE_UNDERLINE + "and",
                     SPIECE_UNDERLINE + "this",
                     SPIECE_UNDERLINE + "is",
                     SPIECE_UNDERLINE + "f",
                     "al",
                     "s",
                     "[UNK]",
                     ".",
                 ]	,       )



       @cached_property
       def   lowercase__       (							self    :						Union[str, Any]					)				->		Optional[Any]:


             '''simple docstring'''




             return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"					)



       @slow
       def   lowercase__       (							self    :						List[Any]					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							"Hello World!"
             UpperCAmelCase_    				=							[35389, 6672, 49, 2]
             self.assertListEqual(_UpperCAmelCase	,       self.big_tokenizer.encode(_UpperCAmelCase					)					)







       @slow
       def   lowercase__       (							self    :						Optional[int]					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							{"input_ids": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 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], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_UpperCAmelCase	,       model_name="microsoft/xprophetnet-large-wiki100-cased"	,       revision="1acad1643ddd54a44df6a1b797ada8373685d90e"	,       )




