






"""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()
UpperCAmelCase_     :				List[Any]         =				logging.get_logger(__name__)



def       _A    (__a							)				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	DPTConfig(embedding_type='''hybrid'''							)

    if "large" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						int							=	10_24
        SCREAMING_SNAKE_CASE_   :						Dict							=	40_96
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	24
        SCREAMING_SNAKE_CASE_   :						Any							=	16
        SCREAMING_SNAKE_CASE_   :						int							=	[5, 11, 17, 23]
        SCREAMING_SNAKE_CASE_   :						List[str]							=	[2_56, 5_12, 10_24, 10_24]
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	(1, 3_84, 3_84)

    if "nyu" or "midas" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	7_68
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[1, 1, 1, 0.5]
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[2_56, 5_12, 7_68, 7_68]
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	1_50
        SCREAMING_SNAKE_CASE_   :						str							=	16
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	(1, 3_84, 3_84)
        SCREAMING_SNAKE_CASE_   :						int							=	False
        SCREAMING_SNAKE_CASE_   :						Tuple							=	'''project'''

    if "ade" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						str							=	True
        SCREAMING_SNAKE_CASE_   :						Dict							=	7_68
        SCREAMING_SNAKE_CASE_   :						Tuple							=	[1, 1, 1, 0.5]
        SCREAMING_SNAKE_CASE_   :						List[str]							=	1_50
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	16
        SCREAMING_SNAKE_CASE_   :						Tuple							=	'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	'''ade20k-id2label.json'''
        SCREAMING_SNAKE_CASE_   :						List[str]							=	json.load(open(cached_download(hf_hub_url(__a					,       __a					,       repo_type='''dataset'''							)							)					,       '''r'''							)							)
        SCREAMING_SNAKE_CASE_   :						Dict							=	{int(__a							): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	idalabel
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{v: k for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_   :						Any							=	[1, 1_50, 4_80, 4_80]

    return config, expected_shape



def       _A    (__a							)				-> List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						int							=	['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
    for k in ignore_keys:
        state_dict.pop(__a					,       __a							)



def       _A    (__a							)				-> Optional[int]:
    """simple docstring"""






    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''pretrained.model'''					,       '''dpt.encoder'''							)
    if "pretrained.model" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''pretrained.model'''					,       '''dpt.embeddings'''							)
    if "patch_embed" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''patch_embed'''					,       ''''''							)
    if "pos_embed" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''pos_embed'''					,       '''position_embeddings'''							)
    if "attn.proj" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''attn.proj'''					,       '''attention.output.dense'''							)
    if "proj" in name and "project" not in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	name.replace('''proj'''					,       '''projection'''							)
    if "blocks" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''blocks'''					,       '''layer'''							)
    if "mlp.fc1" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''mlp.fc1'''					,       '''intermediate.dense'''							)
    if "mlp.fc2" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''mlp.fc2'''					,       '''output.dense'''							)
    if "norm1" in name and "backbone" not in name:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	name.replace('''norm1'''					,       '''layernorm_before'''							)
    if "norm2" in name and "backbone" not in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''norm2'''					,       '''layernorm_after'''							)
    if "scratch.output_conv" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''scratch.output_conv'''					,       '''head'''							)
    if "scratch" in name:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	name.replace('''scratch'''					,       '''neck'''							)
    if "layer1_rn" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''layer1_rn'''					,       '''convs.0'''							)
    if "layer2_rn" in name:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name.replace('''layer2_rn'''					,       '''convs.1'''							)
    if "layer3_rn" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''layer3_rn'''					,       '''convs.2'''							)
    if "layer4_rn" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''layer4_rn'''					,       '''convs.3'''							)
    if "refinenet" in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	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
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace(f'refinenet{layer_idx}'					,       f'fusion_stage.layers.{abs(layer_idx-4							)}'							)
    if "out_conv" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''out_conv'''					,       '''projection'''							)
    if "resConfUnit1" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''resConfUnit1'''					,       '''residual_layer1'''							)
    if "resConfUnit2" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''resConfUnit2'''					,       '''residual_layer2'''							)
    if "conv1" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''conv1'''					,       '''convolution1'''							)
    if "conv2" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''conv2'''					,       '''convolution2'''							)
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	name.replace('''pretrained.act_postprocess1.0.project.0'''					,       '''neck.reassemble_stage.readout_projects.0.0'''							)
    if "pretrained.act_postprocess2.0.project.0" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''pretrained.act_postprocess2.0.project.0'''					,       '''neck.reassemble_stage.readout_projects.1.0'''							)
    if "pretrained.act_postprocess3.0.project.0" in name:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	name.replace('''pretrained.act_postprocess3.0.project.0'''					,       '''neck.reassemble_stage.readout_projects.2.0'''							)
    if "pretrained.act_postprocess4.0.project.0" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''pretrained.act_postprocess4.0.project.0'''					,       '''neck.reassemble_stage.readout_projects.3.0'''							)

    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''pretrained.act_postprocess1.3'''					,       '''neck.reassemble_stage.layers.0.projection'''							)
    if "pretrained.act_postprocess1.4" in name:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name.replace('''pretrained.act_postprocess1.4'''					,       '''neck.reassemble_stage.layers.0.resize'''							)
    if "pretrained.act_postprocess2.3" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	name.replace('''pretrained.act_postprocess2.3'''					,       '''neck.reassemble_stage.layers.1.projection'''							)
    if "pretrained.act_postprocess2.4" in name:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name.replace('''pretrained.act_postprocess2.4'''					,       '''neck.reassemble_stage.layers.1.resize'''							)
    if "pretrained.act_postprocess3.3" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''pretrained.act_postprocess3.3'''					,       '''neck.reassemble_stage.layers.2.projection'''							)
    if "pretrained.act_postprocess4.3" in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	name.replace('''pretrained.act_postprocess4.3'''					,       '''neck.reassemble_stage.layers.3.projection'''							)
    if "pretrained.act_postprocess4.4" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''pretrained.act_postprocess4.4'''					,       '''neck.reassemble_stage.layers.3.resize'''							)
    if "pretrained" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''pretrained'''					,       '''dpt'''							)
    if "bn" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''bn'''					,       '''batch_norm'''							)
    if "head" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''head'''					,       '''head.head'''							)
    if "encoder.norm" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''encoder.norm'''					,       '''layernorm'''							)
    if "auxlayer" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''auxlayer'''					,       '''auxiliary_head.head'''							)
    if "backbone" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	name.replace('''backbone'''					,       '''backbone.bit.encoder'''							)

    if ".." in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''..'''					,       '''.'''							)

    if "stem.conv" in name:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	name.replace('''stem.conv'''					,       '''bit.embedder.convolution'''							)
    if "blocks" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''blocks'''					,       '''layers'''							)
    if "convolution" in name and "backbone" in name:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	name.replace('''convolution'''					,       '''conv'''							)
    if "layer" in name and "backbone" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''layer'''					,       '''layers'''							)
    if "backbone.bit.encoder.bit" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''backbone.bit.encoder.bit'''					,       '''backbone.bit'''							)
    if "embedder.conv" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''embedder.conv'''					,       '''embedder.convolution'''							)
    if "backbone.bit.encoder.stem.norm" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''backbone.bit.encoder.stem.norm'''					,       '''backbone.bit.embedder.norm'''							)
    return name



def       _A    (__a					,       __a							)				-> Dict:
    """simple docstring"""






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



def       _A    ()				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Dict							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	Image.open(requests.get(__a					,       stream=__a							).raw							)
    return im



@torch.no_grad()
def       _A    (__a					,       __a					,       __a					,       __a					,       __a							)				-> List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						int							=	get_dpt_config(__a							)
    # load original state_dict from URL
    # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    SCREAMING_SNAKE_CASE_   :						int							=	torch.load(__a					,       map_location='''cpu'''							)
    # remove certain keys
    remove_ignore_keys_(__a							)
    # rename keys
    for key in state_dict.copy().keys():
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	state_dict.pop(__a							)
        SCREAMING_SNAKE_CASE_   :						Dict							=	val
    # read in qkv matrices
    read_in_q_k_v(__a					,       __a							)

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	DPTForSemanticSegmentation(__a							) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__a							)
    model.load_state_dict(__a							)
    model.eval()

    # Check outputs on an image
    SCREAMING_SNAKE_CASE_   :						Dict							=	4_80 if '''ade''' in checkpoint_url else 3_84
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	DPTImageProcessor(size=__a							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	prepare_img()
    SCREAMING_SNAKE_CASE_   :						Tuple							=	image_processor(__a					,       return_tensors='''pt'''							)

    # forward pass
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(**__a							).logits if '''ade''' in checkpoint_url else model(**__a							).predicted_depth

    if show_prediction:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	(
            torch.nn.functional.interpolate(
                outputs.unsqueeze(1							)					,       size=(image.size[1], image.size[0])					,       mode='''bicubic'''					,       align_corners=__a					,       )
            .squeeze()
            .cpu()
            .numpy()
        )

        Image.fromarray((prediction / prediction.max()) * 2_55							).show()

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

    if push_to_hub:
        model.push_to_hub('''ybelkada/dpt-hybrid-midas'''							)
        image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''							)


if __name__ == "__main__":
  UpperCAmelCase_     :				List[str]         =				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=False,
      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.""",
  )
  parser.add_argument(
      """--show_prediction""",
      action="""store_true""",
  )

  UpperCAmelCase_     :				List[Any]         =				parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )










"""simple docstring"""






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
  UpperCAmelCase_     :				Tuple         =				"""pt"""
elif is_tf_available():
  UpperCAmelCase_     :				Optional[Any]         =				"""tf"""
else:
  UpperCAmelCase_     :				str         =				"""jax"""




class 							lowerCAmelCase__       (    UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						ByTaTokenizer
      __UpperCamelCase							=						False
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          super().setUp()
          SCREAMING_SNAKE_CASE_   :						Dict							=	ByTaTokenizer()
          tokenizer.save_pretrained(self.tmpdirname)
      @cached_property
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          return ByTaTokenizer.from_pretrained('''google/byt5-small''')
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			**lowercase_  :						Tuple):




          '''simple docstring'''


          return self.tokenizer_class.from_pretrained(self.tmpdirname		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Optional[Any]		,			lowercase_  :						List[Any]=False		,			lowercase_  :						Tuple=20		,			lowercase_  :						List[str]=5):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	[]
          for i in range(len(lowercase_)):
              try:
                  SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer.decode([i]		,			clean_up_tokenization_spaces=lowercase_)
              except UnicodeDecodeError:
                  pass
              toks.append((i, tok))

          SCREAMING_SNAKE_CASE_   :						Dict							=	list(filter(lambda lowercase_: re.match(r'''^[ a-zA-Z]+$'''		,			t[1])		,			lowercase_))
          SCREAMING_SNAKE_CASE_   :						Any							=	list(filter(lambda lowercase_: [t[0]] == tokenizer.encode(t[1]		,			add_special_tokens=lowercase_)		,			lowercase_))
          if max_length is not None and len(lowercase_) > max_length:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	toks[:max_length]
          if min_length is not None and len(lowercase_) < min_length and len(lowercase_) > 0:
              while len(lowercase_) < min_length:
                  SCREAMING_SNAKE_CASE_   :						int							=	toks + toks
        # toks_str = [t[1] for t in toks]
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[t[0] for t in toks]

          # Ensure consistency
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer.decode(lowercase_		,			clean_up_tokenization_spaces=lowercase_)
          if " " not in output_txt and len(lowercase_) > 1:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	(
                  tokenizer.decode([toks_ids[0]]		,			clean_up_tokenization_spaces=lowercase_)
                  + ''' '''
                  + tokenizer.decode(toks_ids[1:]		,			clean_up_tokenization_spaces=lowercase_)
              )
          if with_prefix_space:
              SCREAMING_SNAKE_CASE_   :						List[str]							=	''' ''' + output_txt
          SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
          return output_txt, output_ids
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>'''])
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(['''hi''', '''I went to the gym''', ''''''])
          self.assertListEqual(batch_with_eos_added['''input_ids''']		,			batch_without_eos_added['''input_ids'''])
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Any							=	'''Unicode €.'''
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
          self.assertEqual(encoded['''input_ids''']		,			lowercase_)

          # decoding
          SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.decode(lowercase_)
          self.assertEqual(lowercase_		,			'''Unicode €.</s>''')

          SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer('''e è é ê ë''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
          self.assertEqual(encoded['''input_ids''']		,			lowercase_)
          # decoding
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.decode(lowercase_)
          self.assertEqual(lowercase_		,			'''e è é ê ë</s>''')

          # encode/decode, but with `encode` instead of `__call__`
          self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''))		,			'''e è é ê ë</s>''')
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          # fmt: off
          SCREAMING_SNAKE_CASE_   :						Dict							=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
          # fmt: on
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer(lowercase_		,			padding=lowercase_		,			return_tensors=lowercase_)
          self.assertIsInstance(lowercase_		,			lowercase_)

          if FRAMEWORK != "jax":
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	list(batch.input_ids.numpy()[0])
          else:
              SCREAMING_SNAKE_CASE_   :						int							=	list(batch.input_ids.tolist()[0])

          self.assertListEqual(lowercase_		,			lowercase_)

          self.assertEqual((2, 37)		,			batch.input_ids.shape)
          self.assertEqual((2, 37)		,			batch.attention_mask.shape)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer(lowercase_		,			padding=lowercase_		,			return_tensors=lowercase_)
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn('''input_ids'''		,			lowercase_)
          self.assertIn('''attention_mask'''		,			lowercase_)
          self.assertNotIn('''decoder_input_ids'''		,			lowercase_)
          self.assertNotIn('''decoder_attention_mask'''		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						List[str]							=	[
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer(
              text_target=lowercase_		,			max_length=32		,			padding='''max_length'''		,			truncation=lowercase_		,			return_tensors=lowercase_)
          self.assertEqual(32		,			targets['''input_ids'''].shape[1])
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.ta_base_tokenizer
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	['''A long paragraph for summarization. </s>''']
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	['''Summary of the text. </s>''']
          # fmt: off
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
          SCREAMING_SNAKE_CASE_   :						Tuple							=	[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
          # fmt: on

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tokenizer(lowercase_		,			text_target=lowercase_)

          self.assertEqual(lowercase_		,			batch['''input_ids'''][0])
          self.assertEqual(lowercase_		,			batch['''labels'''][0])
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  self.assertNotEqual(tokenizer.model_max_length		,			42)

        # Now let's start the test
          SCREAMING_SNAKE_CASE_   :						str							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  # Isolate this from the other tests because we save additional tokens/etc
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tempfile.mkdtemp()

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	''' He is very happy, UNwant\u00E9d,running'''
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  tokenizer.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						Any							=	tokenizer.__class__.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	after_tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  self.assertListEqual(lowercase_		,			lowercase_)

                  shutil.rmtree(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	self.get_tokenizers(model_max_length=42)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  # Isolate this from the other tests because we save additional tokens/etc
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tempfile.mkdtemp()

                  SCREAMING_SNAKE_CASE_   :						Any							=	''' He is very happy, UNwant\u00E9d,running'''
                  tokenizer.add_tokens(['''bim''', '''bambam'''])
                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer.additional_special_tokens
                  additional_special_tokens.append('''new_additional_special_token''')
                  tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens})
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  tokenizer.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						Dict							=	tokenizer.__class__.from_pretrained(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	after_tokenizer.encode(lowercase_		,			add_special_tokens=lowercase_)
                  self.assertListEqual(lowercase_		,			lowercase_)
                  self.assertIn('''new_additional_special_token'''		,			after_tokenizer.additional_special_tokens)
                  self.assertEqual(after_tokenizer.model_max_length		,			42)

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tokenizer.__class__.from_pretrained(lowercase_		,			model_max_length=43)
                  self.assertEqual(tokenizer.model_max_length		,			43)

                  shutil.rmtree(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	[]
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

          for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(lowercase_)

                  with open(os.path.join(lowercase_		,			'''special_tokens_map.json''')		,			encoding='''utf-8''') as json_file:
                      SCREAMING_SNAKE_CASE_   :						List[Any]							=	json.load(lowercase_)

                  with open(os.path.join(lowercase_		,			'''tokenizer_config.json''')		,			encoding='''utf-8''') as json_file:
                      SCREAMING_SNAKE_CASE_   :						Tuple							=	json.load(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						str							=	[F'<extra_id_{i}>' for i in range(125)]

                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]
                  SCREAMING_SNAKE_CASE_   :						int							=	added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]

                  with open(os.path.join(lowercase_		,			'''special_tokens_map.json''')		,			'''w'''		,			encoding='''utf-8''') as outfile:
                      json.dump(lowercase_		,			lowercase_)
                  with open(os.path.join(lowercase_		,			'''tokenizer_config.json''')		,			'''w'''		,			encoding='''utf-8''') as outfile:
                      json.dump(lowercase_		,			lowercase_)

                  # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                  # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                  # "special_tokens_map.json" files
                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer_class.from_pretrained(
                      lowercase_		,			)
                  self.assertIn(
                      '''an_additional_special_token'''		,			tokenizer_without_change_in_init.additional_special_tokens)
                  # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                  self.assertEqual(
                      ['''an_additional_special_token''']		,			tokenizer_without_change_in_init.convert_ids_to_tokens(
                          tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']))		,			)

                  # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''		,			lstrip=lowercase_)]
                  SCREAMING_SNAKE_CASE_   :						int							=	tokenizer_class.from_pretrained(
                      lowercase_		,			additional_special_tokens=lowercase_		,			)

                  self.assertIn('''a_new_additional_special_token'''		,			tokenizer.additional_special_tokens)
                  self.assertEqual(
                      ['''a_new_additional_special_token''']		,			tokenizer.convert_ids_to_tokens(
                          tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']))		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	[]
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

          for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(lowercase_)

                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer_class.from_pretrained(lowercase_)

                  self.assertTrue(tokenizer.decode([255]) == '''''')
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.get_tokenizers(fast=lowercase_		,			do_lower_case=lowercase_)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_   :						Any							=	['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer.convert_tokens_to_string(lowercase_)

                  self.assertIsInstance(lowercase_		,			lowercase_)



      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.get_tokenizers()
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'):
                  SCREAMING_SNAKE_CASE_   :						int							=	[
                      '''bos_token''',
                      '''eos_token''',
                      '''unk_token''',
                      '''sep_token''',
                      '''pad_token''',
                      '''cls_token''',
                      '''mask_token''',
                  ]

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	0
                  SCREAMING_SNAKE_CASE_   :						str							=	tokenizer.convert_ids_to_tokens(
                      lowercase_		,			skip_special_tokens=lowercase_)

                  for attr in attributes_list:
                      setattr(lowercase_		,			attr + '''_id'''		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			lowercase_)		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			attr + '''_id''')		,			lowercase_)

                      setattr(lowercase_		,			attr + '''_id'''		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			lowercase_)		,			lowercase_)
                      self.assertEqual(getattr(lowercase_		,			attr + '''_id''')		,			lowercase_)

                  setattr(lowercase_		,			'''additional_special_tokens_ids'''		,			[])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens''')		,			[])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens_ids''')		,			[])

                  setattr(lowercase_		,			'''additional_special_tokens_ids'''		,			[token_id_to_test_setters])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens''')		,			[token_to_test_setters])
                  self.assertListEqual(getattr(lowercase_		,			'''additional_special_tokens_ids''')		,			[token_id_to_test_setters])


