






'''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__						:int          =			logging.get_logger(__name__)
def     __lowercase  (_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				DPTConfig(embedding_type="""hybrid"""     )

     if "large" in checkpoint_url:
          __lowerCamelCase						:       List[str]				   =				1_024
          __lowerCamelCase						:       int				   =				4_096
          __lowerCamelCase						:       Tuple				   =				24
          __lowerCamelCase						:       List[Any]				   =				16
          __lowerCamelCase						:       Any				   =				[5, 11, 17, 23]
          __lowerCamelCase						:       Dict				   =				[256, 512, 1_024, 1_024]
          __lowerCamelCase						:       Dict				   =				(1, 384, 384)

     if "nyu" or "midas" in checkpoint_url:
          __lowerCamelCase						:       Dict				   =				768
          __lowerCamelCase						:       List[Any]				   =				[1, 1, 1, 0.5]
          __lowerCamelCase						:       List[str]				   =				[256, 512, 768, 768]
          __lowerCamelCase						:       Union[str, Any]				   =				150
          __lowerCamelCase						:       str				   =				16
          __lowerCamelCase						:       Dict				   =				(1, 384, 384)
          __lowerCamelCase						:       Tuple				   =				False
          __lowerCamelCase						:       Optional[Any]				   =				"""project"""

     if "ade" in checkpoint_url:
          __lowerCamelCase						:       Optional[int]				   =				True
          __lowerCamelCase						:       List[Any]				   =				768
          __lowerCamelCase						:       int				   =				[1, 1, 1, 0.5]
          __lowerCamelCase						:       Dict				   =				150
          __lowerCamelCase						:       int				   =				16
          __lowerCamelCase						:       Union[str, Any]				   =				"""huggingface/label-files"""
          __lowerCamelCase						:       Optional[int]				   =				"""ade20k-id2label.json"""
          __lowerCamelCase						:       int				   =				json.load(open(cached_download(hf_hub_url(_lowercase,						_lowercase,						repo_type="""dataset"""     )     ),						"""r"""     )     )
          __lowerCamelCase						:       str				   =				{int(_lowercase     ): v for k, v in idalabel.items()}
          __lowerCamelCase						:       Optional[int]				   =				idalabel
          __lowerCamelCase						:       Optional[int]				   =				{v: k for k, v in idalabel.items()}
          __lowerCamelCase						:       List[Any]				   =				[1, 150, 480, 480]

     return config, expected_shape
def     __lowercase  (_lowercase     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
     for k in ignore_keys:
          state_dict.pop(_lowercase,						_lowercase     )
def     __lowercase  (_lowercase     )   ->     List[Any]:


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

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

     if ".." in name:
          __lowerCamelCase						:       str				   =				name.replace("""..""",						"""."""     )

     if "stem.conv" in name:
          __lowerCamelCase						:       Dict				   =				name.replace("""stem.conv""",						"""bit.embedder.convolution"""     )
     if "blocks" in name:
          __lowerCamelCase						:       Tuple				   =				name.replace("""blocks""",						"""layers"""     )
     if "convolution" in name and "backbone" in name:
          __lowerCamelCase						:       str				   =				name.replace("""convolution""",						"""conv"""     )
     if "layer" in name and "backbone" in name:
          __lowerCamelCase						:       Optional[int]				   =				name.replace("""layer""",						"""layers"""     )
     if "backbone.bit.encoder.bit" in name:
          __lowerCamelCase						:       Union[str, Any]				   =				name.replace("""backbone.bit.encoder.bit""",						"""backbone.bit"""     )
     if "embedder.conv" in name:
          __lowerCamelCase						:       Optional[int]				   =				name.replace("""embedder.conv""",						"""embedder.convolution"""     )
     if "backbone.bit.encoder.stem.norm" in name:
          __lowerCamelCase						:       Union[str, Any]				   =				name.replace("""backbone.bit.encoder.stem.norm""",						"""backbone.bit.embedder.norm"""     )
     return name
def     __lowercase  (_lowercase,						_lowercase     )   ->     int:


     """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)
          __lowerCamelCase						:       Tuple				   =				state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"     )
          __lowerCamelCase						:       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
          __lowerCamelCase						:       List[str]				   =				in_proj_weight[: config.hidden_size, :]
          __lowerCamelCase						:       Union[str, Any]				   =				in_proj_bias[: config.hidden_size]
          __lowerCamelCase						:       Optional[Any]				   =				in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __lowerCamelCase						:       List[Any]				   =				in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __lowerCamelCase						:       Tuple				   =				in_proj_weight[
              -config.hidden_size :, :
          ]
          __lowerCamelCase						:       Dict				   =				in_proj_bias[-config.hidden_size :]
def     __lowercase  ()   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     __lowerCamelCase						:       Tuple				   =				Image.open(requests.get(_lowercase,						stream=_lowercase     ).raw     )
     return im
@torch.no_grad()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     List[Any]:


     """simple docstring"""
     __lowerCamelCase   ,      __lowerCamelCase						:       List[str]				   =				get_dpt_config(_lowercase     )
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     __lowerCamelCase						:       Tuple				   =				torch.load(_lowercase,						map_location="""cpu"""     )
     # remove certain keys
     remove_ignore_keys_(_lowercase     )
     # rename keys
     for key in state_dict.copy().keys():
          __lowerCamelCase						:       List[Any]				   =				state_dict.pop(_lowercase     )
          __lowerCamelCase						:       Union[str, Any]				   =				val
     # read in qkv matrices
     read_in_q_k_v(_lowercase,						_lowercase     )

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

     # Check outputs on an image
     __lowerCamelCase						:       Union[str, Any]				   =				480 if """ade""" in checkpoint_url else 384
     __lowerCamelCase						:       int				   =				DPTImageProcessor(size=_lowercase     )

     __lowerCamelCase						:       Any				   =				prepare_img()
     __lowerCamelCase						:       Union[str, Any]				   =				image_processor(_lowercase,						return_tensors="""pt"""     )

     # forward pass
     __lowerCamelCase						:       str				   =				model(**_lowercase     ).logits if """ade""" in checkpoint_url else model(**_lowercase     ).predicted_depth

     if show_prediction:
          __lowerCamelCase						:       List[str]				   =				(
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1     ),						size=(image.size[1], image.size[0]),						mode="""bicubic""",						align_corners=_lowercase,						)
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 255     ).show()

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

     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__						:Union[str, Any]          =			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__						:str          =			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 collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


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

UpperCAmelCase__						:Dict          =			{"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}

UpperCAmelCase__						:List[Any]          =			{
    """vocab_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
    },
    """emoji_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
    },
}

UpperCAmelCase__						:Optional[Any]          =			{
    """abeja/gpt-neox-japanese-2.7b""": 2_048,
}
def     __lowercase  (_lowercase,						_lowercase     )   ->     str:


     """simple docstring"""
     with open(_lowercase,						"""r""",						encoding="""utf-8"""     ) as f:
          __lowerCamelCase						:       Dict				   =				json.loads(f.read()     )

     __lowerCamelCase						:       Dict				   =				collections.OrderedDict()
     __lowerCamelCase						:       int				   =				collections.OrderedDict()
     __lowerCamelCase						:       List[Any]				   =				collections.OrderedDict()
     with open(_lowercase,						"""r""",						encoding="""utf-8"""     ) as f:
          __lowerCamelCase						:       Union[str, Any]				   =				f.readlines()
     __lowerCamelCase						:       Any				   =				[[t.rstrip("""\n"""     )] if (t == """,""" or """,""" not in t) else t.rstrip("""\n"""     ).split(""","""     ) for t in token]
     for idx, b in enumerate(_lowercase     ):
          __lowerCamelCase						:       str				   =				b
          __lowerCamelCase						:       List[str]				   =				idx
          for wd in b:
               __lowerCamelCase						:       Optional[Any]				   =				idx

     return vocab, raw_vocab, ids_to_tokens, emoji







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Optional[Any]   		=			VOCAB_FILES_NAMES
       snake_case__    :     List[str]   		=			PRETRAINED_VOCAB_FILES_MAP
       snake_case__    :     Union[str, Any]   		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case__    :     Union[str, Any]   		=			['input_ids', 'attention_mask']




       def __init__(					self      : Tuple	,    A__      : int	,    A__      : List[str]	,    A__      : Any="<|endoftext|>"	,    A__      : Tuple="<|endoftext|>"	,    A__      : Dict="<|startoftext|>"	,    A__      : Dict="<|endoftext|>"	,    A__      : List[str]=False	,    **A__      : List[Any]	,    ):


            """simple docstring"""







            super().__init__(
                unk_token=A__	,    pad_token=A__	,    bos_token=A__	,    eos_token=A__	,    do_clean_text=A__	,    **A__	,    )
            if not os.path.isfile(A__					):
                 raise ValueError(
                     f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
                     """ model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""					)
            if not os.path.isfile(A__					):
                 raise ValueError(
                     f"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"
                     """ pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""					)
            __lowerCamelCase						:       Tuple				   =				do_clean_text
            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				load_vocab_and_emoji(A__	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				SubWordJapaneseTokenizer(
                vocab=self.vocab	,    ids_to_tokens=self.ids_to_tokens	,    emoji=self.emoji					)




       @property
       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            return len(self.raw_vocab					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            return dict(self.raw_vocab	,    **self.added_tokens_encoder					)




       def 					a_					(					self      : List[Any]	,    A__      : Optional[int]					):


            """simple docstring"""







            return self.subword_tokenizer.tokenize(A__	,    clean=self.do_clean_text					)




       def 					a_					(					self      : Optional[Any]	,    A__      : Any					):


            """simple docstring"""







            return self.vocab.get(A__	,    self.vocab.get(self.unk_token					)					)




       def 					a_					(					self      : Any	,    A__      : Any					):


            """simple docstring"""







            return self.subword_tokenizer.convert_id_to_token(A__					)




       def 					a_					(					self      : Optional[int]	,    A__      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				"""""".join(A__					).strip()
            return out_string




       def 					a_					(					self      : str	,    A__      : "Conversation"					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				[]
            for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(A__	,    add_special_tokens=A__					) + [self.eos_token_id]					)

            if len(A__					) > self.model_max_length:
                 __lowerCamelCase						:       str				   =				input_ids[-self.model_max_length :]
            return input_ids




       def 					a_					(					self      : str	,    A__      : str	,    A__      : Optional[str] = None					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				0
            if os.path.isdir(A__					):
                 __lowerCamelCase						:       Union[str, Any]				   =				os.path.join(
                     A__	,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
                 __lowerCamelCase						:       int				   =				os.path.join(
                     A__	,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""]					)
            else:
                 __lowerCamelCase						:       str				   =				(
                     (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
                 )
                 __lowerCamelCase						:       int				   =				(
                     (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""]
                 )
            with open(A__	,    """w"""	,    encoding="""utf-8"""					) as writer:
                 for token_index, token in self.ids_to_tokens.items():
                      if index != token_index:
                           logger.warning(
                               f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                               """ Please check that the vocabulary is not corrupted!"""					)
                           __lowerCamelCase						:       Union[str, Any]				   =				token_index
                      writer.write(""",""".join(A__					) + """\n"""					)
                      index += 1
            with open(A__	,    """w"""	,    encoding="""utf-8"""					) as writer:
                 json.dump(self.emoji	,    A__					)
            return vocab_file, emoji_file







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : Union[str, Any]	,    A__      : Any	,    A__      : str	,    A__      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				vocab  # same as swe
            __lowerCamelCase						:       Union[str, Any]				   =				ids_to_tokens  # same as bpe
            __lowerCamelCase						:       List[str]				   =				emoji
            __lowerCamelCase						:       int				   =				np.max([len(A__					) for w in self.vocab.keys()]					)
            __lowerCamelCase						:       List[Any]				   =				re.compile(r"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"""					)
            __lowerCamelCase						:       Optional[Any]				   =				re.compile(r"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"""					)
            __lowerCamelCase						:       str				   =				re.compile(r"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"""					)
            __lowerCamelCase						:       Optional[int]				   =				re.compile(
                r"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""					)
            __lowerCamelCase						:       Union[str, Any]				   =				re.compile(
                r"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""					)
            __lowerCamelCase						:       Tuple				   =				re.compile(
                r"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"""					)
            __lowerCamelCase						:       List[str]				   =				"""─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"""
            __lowerCamelCase						:       List[str]				   =				"""▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"""
            __lowerCamelCase						:       Optional[int]				   =				str.maketrans({k: """<BLOCK>""" for k in keisen + blocks}					)




       def __len__(					self      : Tuple					):


            """simple docstring"""







            return len(self.ids_to_tokens					)




       def 					a_					(					self      : Dict	,    A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.content_repattera.sub("""<URL>"""	,    A__					)
            __lowerCamelCase						:       List[Any]				   =				self.content_repattera.sub("""<EMAIL>"""	,    A__					)
            __lowerCamelCase						:       str				   =				self.content_repattera.sub("""<TEL>"""	,    A__					)
            __lowerCamelCase						:       Optional[int]				   =				self.content_repattera.sub("""<DATE>"""	,    A__					)
            __lowerCamelCase						:       Any				   =				self.content_repattera.sub("""<DATE>"""	,    A__					)
            __lowerCamelCase						:       List[str]				   =				self.content_repattera.sub("""<PRICE>"""	,    A__					)
            __lowerCamelCase						:       Any				   =				content.translate(self.content_transa					)
            while "<BLOCK><BLOCK>" in content:
                 __lowerCamelCase						:       Dict				   =				content.replace("""<BLOCK><BLOCK>"""	,    """<BLOCK>"""					)
            return content




       def 					a_					(					self      : List[Any]	,    A__      : Dict	,    A__      : Optional[Any]=False					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				text.replace(""" """	,    """<SP>"""					)
            __lowerCamelCase						:       Optional[int]				   =				text.replace("""　"""	,    """<SP>"""					)
            __lowerCamelCase						:       Tuple				   =				text.replace("""\r\n"""	,    """<BR>"""					)
            __lowerCamelCase						:       Any				   =				text.replace("""\n"""	,    """<BR>"""					)
            __lowerCamelCase						:       Tuple				   =				text.replace("""\r"""	,    """<BR>"""					)
            __lowerCamelCase						:       List[str]				   =				text.replace("""\t"""	,    """<TAB>"""					)
            __lowerCamelCase						:       List[Any]				   =				text.replace("""—"""	,    """ー"""					)
            __lowerCamelCase						:       Any				   =				text.replace("""−"""	,    """ー"""					)
            for k, v in self.emoji["emoji"].items():
                 if k in text:
                      __lowerCamelCase						:       Dict				   =				text.replace(A__	,    A__					)
            if clean:
                 __lowerCamelCase						:       Optional[int]				   =				self.clean_text(A__					)

            def check_simbol(A__      : Union[str, Any]					):
                 __lowerCamelCase						:       str				   =				x.encode()
                 if len(A__					) == 1 and len(A__					) == 2:
                      __lowerCamelCase						:       Optional[int]				   =				(int(e[0]					) << 8) + int(e[1]					)
                      if (
                          (c >= 0XC2_A1 and c <= 0XC2_BF)
                          or (c >= 0XC7_80 and c <= 0XC7_83)
                          or (c >= 0XCA_B9 and c <= 0XCB_BF)
                          or (c >= 0XCC_80 and c <= 0XCD_A2)
                      ):
                           return True
                 return False

            def checkuae(A__      : Tuple					):
                 __lowerCamelCase						:       List[Any]				   =				x.encode()
                 if len(A__					) == 1 and len(A__					) == 3:
                      __lowerCamelCase						:       List[Any]				   =				(int(e[0]					) << 16) + (int(e[1]					) << 8) + int(e[2]					)
                      if c >= 0XE2_80_80 and c <= 0XE2_B0_7F:
                           return True
                 return False

            __lowerCamelCase						:       List[Any]				   =				0
            __lowerCamelCase						:       Optional[Any]				   =				[]
            while pos < len(A__					):
                 __lowerCamelCase						:       Tuple				   =				min(len(A__					)	,    pos + self.maxlen + 1					) if text[pos] == """<""" else pos + 3
                 __lowerCamelCase						:       Optional[int]				   =				[]  # (token_id, token, pos)
                 for e in range(A__	,    A__	,    -1					):
                      __lowerCamelCase						:       Dict				   =				text[pos:e]
                      if wd in self.vocab:
                           if wd[0] == "<" and len(A__					) > 2:
                                __lowerCamelCase						:       Any				   =				[(self.vocab[wd], wd, e)]
                                break
                           else:
                                candidates.append((self.vocab[wd], wd, e)					)
                 if len(A__					) > 0:
                      # the smallest token_id is adopted
                      __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Dict				   =				sorted(A__	,    key=lambda A__					: x[0]					)[0]
                      result.append(A__					)
                      __lowerCamelCase						:       Optional[Any]				   =				e
                 else:
                      __lowerCamelCase						:       Any				   =				pos + 1
                      __lowerCamelCase						:       Any				   =				text[pos:end]
                      if check_simbol(A__					):
                           result.append("""<KIGOU>"""					)
                      elif checkuae(A__					):
                           result.append("""<U2000U2BFF>"""					)
                      else:
                           for i in wd.encode("""utf-8"""					):
                                result.append("""<|byte%d|>""" % i					)
                      __lowerCamelCase						:       Optional[int]				   =				end
            return result




       def 					a_					(					self      : Optional[Any]	,    A__      : Optional[int]	,    A__      : Any="\n"					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				[]
            __lowerCamelCase						:       List[Any]				   =				[]
            __lowerCamelCase						:       Optional[int]				   =				self.ids_to_tokens[index][0]
            if word[:6] == "<|byte" and word[-2:] == "|>":
                 byte_tokens.append(int(word[6:-2]					)					)
            else:
                 if len(A__					) > 0:
                      words.append(bytearray(A__					).decode("""utf-8"""	,    errors="""replace"""					)					)
                      __lowerCamelCase						:       Tuple				   =				[]
                 if word[:7] == "<|emoji" and word[-2:] == "|>":
                      words.append(self.emoji["""emoji_inv"""][word]					)
                 elif word == "<SP>":
                      words.append(""" """					)
                 elif word == "<BR>":
                      words.append(A__					)
                 elif word == "<TAB>":
                      words.append("""\t"""					)
                 elif word == "<BLOCK>":
                      words.append("""▀"""					)
                 elif word == "<KIGOU>":
                      words.append("""ǀ"""					)
                 elif word == "<U2000U2BFF>":
                      words.append("""‖"""					)
                 else:
                      words.append(A__					)
            if len(A__					) > 0:
                 words.append(bytearray(A__					).decode("""utf-8"""	,    errors="""replace"""					)					)
            __lowerCamelCase						:       str				   =				"""""".join(A__					)
            return text



