



"""simple docstring"""




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						Any=1    )  ->       List[str]:





     '''simple docstring'''

     if n_shave_prefix_segments >= 0:
          return ".".join(path.split("."    )[n_shave_prefix_segments:]    )
     else:
          return ".".join(path.split("."    )[:n_shave_prefix_segments]    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						Any=0    )  ->       Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   []
     for old_item in old_list:
          _UpperCAmelCase      :     int        =   old_item.replace("in_layers.0"						,  "norm1"    )
          _UpperCAmelCase      :     List[Any]        =   new_item.replace("in_layers.2"						,  "conv1"    )

          _UpperCAmelCase      :     Optional[Any]        =   new_item.replace("out_layers.0"						,  "norm2"    )
          _UpperCAmelCase      :     Dict        =   new_item.replace("out_layers.3"						,  "conv2"    )

          _UpperCAmelCase      :     Dict        =   new_item.replace("emb_layers.1"						,  "time_emb_proj"    )
          _UpperCAmelCase      :     Optional[Any]        =   new_item.replace("skip_connection"						,  "conv_shortcut"    )

          _UpperCAmelCase      :     Dict        =   shave_segments(SCREAMING_SNAKE_CASE__						,  n_shave_prefix_segments=SCREAMING_SNAKE_CASE__    )

          mapping.append({"old": old_item, "new": new_item}    )

     return mapping

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						List[str]=0    )  ->       List[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   []
     for old_item in old_list:
          _UpperCAmelCase      :     Optional[Any]        =   old_item

          _UpperCAmelCase      :     Tuple        =   new_item.replace("norm.weight"						,  "group_norm.weight"    )
          _UpperCAmelCase      :     Optional[int]        =   new_item.replace("norm.bias"						,  "group_norm.bias"    )

          _UpperCAmelCase      :     List[Any]        =   new_item.replace("proj_out.weight"						,  "proj_attn.weight"    )
          _UpperCAmelCase      :     int        =   new_item.replace("proj_out.bias"						,  "proj_attn.bias"    )

          _UpperCAmelCase      :     Tuple        =   shave_segments(SCREAMING_SNAKE_CASE__						,  n_shave_prefix_segments=SCREAMING_SNAKE_CASE__    )

          mapping.append({"old": old_item, "new": new_item}    )

     return mapping

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Dict=None						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]=None						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]=None    )  ->       int:





     '''simple docstring'''

     assert isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ), "Paths should be a list of dicts containing 'old' and 'new' keys."

     # Splits the attention layers into three variables.
     if attention_paths_to_split is not None:
          for path, path_map in attention_paths_to_split.items():
               _UpperCAmelCase      :     Dict        =   old_checkpoint[path]
               _UpperCAmelCase      :     int        =   old_tensor.shape[0] // 3

               _UpperCAmelCase      :     int        =   (-1, channels) if len(old_tensor.shape    ) == 3 else (-1)

               _UpperCAmelCase      :     str        =   old_tensor.shape[0] // config["num_head_channels"] // 3

               _UpperCAmelCase      :     Optional[int]        =   old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]    )
               _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   old_tensor.split(channels // num_heads						,  dim=1    )

               _UpperCAmelCase      :     Any        =   query.reshape(SCREAMING_SNAKE_CASE__    )
               _UpperCAmelCase      :     Optional[Any]        =   key.reshape(SCREAMING_SNAKE_CASE__    )
               _UpperCAmelCase      :     int        =   value.reshape(SCREAMING_SNAKE_CASE__    )

     for path in paths:
          _UpperCAmelCase      :     Optional[int]        =   path["new"]

          # These have already been assigned
          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          # Global renaming happens here
          _UpperCAmelCase      :     List[Any]        =   new_path.replace("middle_block.0"						,  "mid_block.resnets.0"    )
          _UpperCAmelCase      :     Union[str, Any]        =   new_path.replace("middle_block.1"						,  "mid_block.attentions.0"    )
          _UpperCAmelCase      :     Tuple        =   new_path.replace("middle_block.2"						,  "mid_block.resnets.1"    )

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    _UpperCAmelCase      :     Dict        =   new_path.replace(replacement["old"]						,  replacement["new"]    )

        # proj_attn.weight has to be converted from conv 1D to linear
          if "proj_attn.weight" in new_path:
               _UpperCAmelCase      :     Union[str, Any]        =   old_checkpoint[path["old"]][:, :, 0]
          else:
               _UpperCAmelCase      :     Tuple        =   old_checkpoint[path["old"]]

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[str]						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   {}

     _UpperCAmelCase      :     Dict        =   checkpoint["time_embed.0.weight"]
     _UpperCAmelCase      :     Union[str, Any]        =   checkpoint["time_embed.0.bias"]
     _UpperCAmelCase      :     List[Any]        =   checkpoint["time_embed.2.weight"]
     _UpperCAmelCase      :     Optional[int]        =   checkpoint["time_embed.2.bias"]

     _UpperCAmelCase      :     Any        =   checkpoint["input_blocks.0.0.weight"]
     _UpperCAmelCase      :     str        =   checkpoint["input_blocks.0.0.bias"]

     _UpperCAmelCase      :     Any        =   checkpoint["out.0.weight"]
     _UpperCAmelCase      :     Tuple        =   checkpoint["out.0.bias"]
     _UpperCAmelCase      :     Dict        =   checkpoint["out.2.weight"]
     _UpperCAmelCase      :     Any        =   checkpoint["out.2.bias"]

     # Retrieves the keys for the input blocks only
     _UpperCAmelCase      :     List[str]        =   len({".".join(layer.split("."    )[:2]    ) for layer in checkpoint if "input_blocks" in layer}    )
     _UpperCAmelCase      :     Dict        =   {
         layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
         for layer_id in range(SCREAMING_SNAKE_CASE__    )
     }

     # Retrieves the keys for the middle blocks only
     _UpperCAmelCase      :     List[Any]        =   len({".".join(layer.split("."    )[:2]    ) for layer in checkpoint if "middle_block" in layer}    )
     _UpperCAmelCase      :     List[Any]        =   {
         layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
         for layer_id in range(SCREAMING_SNAKE_CASE__    )
     }

     # Retrieves the keys for the output blocks only
     _UpperCAmelCase      :     Any        =   len({".".join(layer.split("."    )[:2]    ) for layer in checkpoint if "output_blocks" in layer}    )
     _UpperCAmelCase      :     List[str]        =   {
         layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
         for layer_id in range(SCREAMING_SNAKE_CASE__    )
     }

     for i in range(1						,  SCREAMING_SNAKE_CASE__    ):
          _UpperCAmelCase      :     int        =   (i - 1) // (config["num_res_blocks"] + 1)
          _UpperCAmelCase      :     Union[str, Any]        =   (i - 1) % (config["num_res_blocks"] + 1)

          _UpperCAmelCase      :     int        =   [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
          _UpperCAmelCase      :     Union[str, Any]        =   [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

          if f'input_blocks.{i}.0.op.weight' in checkpoint:
               _UpperCAmelCase      :     List[Any]        =   checkpoint[
                   f'input_blocks.{i}.0.op.weight'
               ]
               _UpperCAmelCase      :     Union[str, Any]        =   checkpoint[
                   f'input_blocks.{i}.0.op.bias'
               ]
               continue

          _UpperCAmelCase      :     List[str]        =   renew_resnet_paths(SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Dict        =   {"old": f'input_blocks.{i}.0', "new": f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
          _UpperCAmelCase      :     str        =   {"old": "resnets.2.op", "new": "downsamplers.0.op"}
          assign_to_checkpoint(
              SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  additional_replacements=[meta_path, resnet_op]						,  config=SCREAMING_SNAKE_CASE__    )

          if len(SCREAMING_SNAKE_CASE__    ):
               _UpperCAmelCase      :     int        =   renew_attention_paths(SCREAMING_SNAKE_CASE__    )
               _UpperCAmelCase      :     Optional[int]        =   {
                   "old": f'input_blocks.{i}.1',
                   "new": f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
               }
               _UpperCAmelCase      :     List[Any]        =   {
                   f'input_blocks.{i}.1.qkv.bias': {
                       "key": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                       "query": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                       "value": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                   },
                   f'input_blocks.{i}.1.qkv.weight': {
                       "key": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                       "query": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                       "value": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                   },
               }
               assign_to_checkpoint(
                   SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  additional_replacements=[meta_path]						,  attention_paths_to_split=SCREAMING_SNAKE_CASE__						,  config=SCREAMING_SNAKE_CASE__						,  )

     _UpperCAmelCase      :     Tuple        =   middle_blocks[0]
     _UpperCAmelCase      :     str        =   middle_blocks[1]
     _UpperCAmelCase      :     Optional[Any]        =   middle_blocks[2]

     _UpperCAmelCase      :     int        =   renew_resnet_paths(SCREAMING_SNAKE_CASE__    )
     assign_to_checkpoint(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  config=SCREAMING_SNAKE_CASE__    )

     _UpperCAmelCase      :     Optional[int]        =   renew_resnet_paths(SCREAMING_SNAKE_CASE__    )
     assign_to_checkpoint(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  config=SCREAMING_SNAKE_CASE__    )

     _UpperCAmelCase      :     Any        =   renew_attention_paths(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Union[str, Any]        =   {
         "middle_block.1.qkv.bias": {
             "key": "mid_block.attentions.0.key.bias",
             "query": "mid_block.attentions.0.query.bias",
             "value": "mid_block.attentions.0.value.bias",
         },
         "middle_block.1.qkv.weight": {
             "key": "mid_block.attentions.0.key.weight",
             "query": "mid_block.attentions.0.query.weight",
             "value": "mid_block.attentions.0.value.weight",
         },
     }
     assign_to_checkpoint(
         SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  attention_paths_to_split=SCREAMING_SNAKE_CASE__						,  config=SCREAMING_SNAKE_CASE__    )

     for i in range(SCREAMING_SNAKE_CASE__    ):
          _UpperCAmelCase      :     int        =   i // (config["num_res_blocks"] + 1)
          _UpperCAmelCase      :     Any        =   i % (config["num_res_blocks"] + 1)
          _UpperCAmelCase      :     List[str]        =   [shave_segments(SCREAMING_SNAKE_CASE__						,  2    ) for name in output_blocks[i]]
          _UpperCAmelCase      :     Union[str, Any]        =   {}

          for layer in output_block_layers:
               _UpperCAmelCase    ,	_UpperCAmelCase      :     Union[str, Any]        =   layer.split("."    )[0], shave_segments(SCREAMING_SNAKE_CASE__						,  1    )
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(SCREAMING_SNAKE_CASE__    )
               else:
                    _UpperCAmelCase      :     Union[str, Any]        =   [layer_name]

          if len(SCREAMING_SNAKE_CASE__    ) > 1:
               _UpperCAmelCase      :     List[Any]        =   [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
               _UpperCAmelCase      :     Tuple        =   [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

               _UpperCAmelCase      :     Optional[Any]        =   renew_resnet_paths(SCREAMING_SNAKE_CASE__    )
               _UpperCAmelCase      :     List[Any]        =   renew_resnet_paths(SCREAMING_SNAKE_CASE__    )

               _UpperCAmelCase      :     int        =   {"old": f'output_blocks.{i}.0', "new": f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
               assign_to_checkpoint(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  additional_replacements=[meta_path]						,  config=SCREAMING_SNAKE_CASE__    )

               if ["conv.weight", "conv.bias"] in output_block_list.values():
                    _UpperCAmelCase      :     List[str]        =   list(output_block_list.values()    ).index(["conv.weight", "conv.bias"]    )
                    _UpperCAmelCase      :     Union[str, Any]        =   checkpoint[
                        f'output_blocks.{i}.{index}.conv.weight'
                    ]
                    _UpperCAmelCase      :     Tuple        =   checkpoint[
                        f'output_blocks.{i}.{index}.conv.bias'
                    ]

                    # Clear attentions as they have been attributed above.
                    if len(SCREAMING_SNAKE_CASE__    ) == 2:
                         _UpperCAmelCase      :     Tuple        =   []

               if len(SCREAMING_SNAKE_CASE__    ):
                    _UpperCAmelCase      :     Dict        =   renew_attention_paths(SCREAMING_SNAKE_CASE__    )
                    _UpperCAmelCase      :     Any        =   {
                        "old": f'output_blocks.{i}.1',
                        "new": f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                    }
                    _UpperCAmelCase      :     Dict        =   {
                        f'output_blocks.{i}.1.qkv.bias': {
                            "key": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                            "query": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                            "value": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                        },
                        f'output_blocks.{i}.1.qkv.weight': {
                            "key": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                            "query": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                            "value": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                        },
                    }
                    assign_to_checkpoint(
                        SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  additional_replacements=[meta_path]						,  attention_paths_to_split=to_split if any("qkv" in key for key in attentions    ) else None						,  config=SCREAMING_SNAKE_CASE__						,  )
          else:
               _UpperCAmelCase      :     str        =   renew_resnet_paths(SCREAMING_SNAKE_CASE__						,  n_shave_prefix_segments=1    )
               for path in resnet_0_paths:
                    _UpperCAmelCase      :     Dict        =   ".".join(["output_blocks", str(SCREAMING_SNAKE_CASE__    ), path["old"]]    )
                    _UpperCAmelCase      :     Dict        =   ".".join(["up_blocks", str(SCREAMING_SNAKE_CASE__    ), "resnets", str(SCREAMING_SNAKE_CASE__    ), path["new"]]    )

                    _UpperCAmelCase      :     List[str]        =   checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase    : List[str]							=							argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )

 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the architecture.",
 )

 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

 _lowerCAmelCase    : Dict							=							parser.parse_args()

 _lowerCAmelCase    : List[str]							=							torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  _lowerCAmelCase    : List[str]							=							json.loads(f.read())

 _lowerCAmelCase    : Optional[Any]							=							convert_ldm_checkpoint(checkpoint, config)

 if "ldm" in config:
  del config["ldm"]

 _lowerCAmelCase    : int							=							UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _lowerCAmelCase    : Union[str, Any]							=							DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
  _lowerCAmelCase    : str							=							VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

  _lowerCAmelCase    : Any							=							LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)





"""simple docstring"""




import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class      UpperCAmelCase_  (    _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Dict								=			GPTSanJapaneseTokenizer
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			False
      __SCREAMING_SNAKE_CASE						:       List[str]								=			{'do_clean_text': False, 'add_prefix_space': False}



      def 						snake_case_ (					self					:     Any						):
           super().setUp()

           # fmt: off
           _UpperCAmelCase      :     Any        =   ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
           # fmt: on
           _UpperCAmelCase      :     Optional[int]        =   {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
           _UpperCAmelCase      :     List[Any]        =   {"unk_token": "<unk>"}

           _UpperCAmelCase      :     List[Any]        =   os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["vocab_file"]						)
           _UpperCAmelCase      :     Any        =   os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["emoji_file"]						)
           with open(self.vocab_file					,     "w"					,     encoding="utf-8"						) as vocab_writer:
                vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)
           with open(self.emoji_file					,     "w"						) as emoji_writer:
                emoji_writer.write(json.dumps(A						)						)



      def 						snake_case_ (					self					:     int					,     **A					:     List[str]						):
           kwargs.update(self.special_tokens_map						)
           return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     int					,     A					:     Any						):
           _UpperCAmelCase      :     Optional[Any]        =   "こんにちは、世界。 \nこんばんは、㔺界。😀"
           _UpperCAmelCase      :     List[Any]        =   "こんにちは、世界。 \nこんばんは、世界。😀"
           return input_text, output_text



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     str						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     str        =   self.get_input_output_texts(A						)
           _UpperCAmelCase      :     List[Any]        =   tokenizer.encode(A					,     add_special_tokens=A						)
           _UpperCAmelCase      :     Union[str, Any]        =   tokenizer.decode(A					,     clean_up_tokenization_spaces=A						)
           return text, ids



      def 						snake_case_ (					self					:     Any						):
           pass  # TODO add if relevant



      def 						snake_case_ (					self					:     Union[str, Any]						):
           pass  # TODO add if relevant



      def 						snake_case_ (					self					:     int						):
           pass  # TODO add if relevant



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     List[Any]        =   self.get_tokenizer()

           # Testing tokenization
           _UpperCAmelCase      :     Optional[int]        =   "こんにちは、世界。　こんばんは、㔺界。"
           _UpperCAmelCase      :     Dict        =   ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
           _UpperCAmelCase      :     List[Any]        =   tokenizer.tokenize(A						)
           self.assertListEqual(A					,     A						)

           # Testing conversion to ids without special tokens
           _UpperCAmelCase      :     Optional[int]        =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
           _UpperCAmelCase      :     List[str]        =   tokenizer.convert_tokens_to_ids(A						)
           self.assertListEqual(A					,     A						)

           # Testing conversion to ids with special tokens
           _UpperCAmelCase      :     str        =   tokens + [tokenizer.unk_token]
           _UpperCAmelCase      :     Any        =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
           _UpperCAmelCase      :     str        =   tokenizer.convert_tokens_to_ids(A						)
           self.assertListEqual(A					,     A						)



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.get_tokenizer()

           # Testing tokenization
           _UpperCAmelCase      :     Dict        =   "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
           _UpperCAmelCase      :     Tuple        =   "こんにちは、、、、世界。こんばんは、、、、世界。"
           _UpperCAmelCase      :     int        =   tokenizer.encode(A						)
           _UpperCAmelCase      :     Optional[Any]        =   tokenizer.decode(A						)
           self.assertEqual(A					,     A						)



      @slow
      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     List[Any]        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           # Testing tokenization
           _UpperCAmelCase      :     List[Any]        =   "こんにちは、世界。"
           _UpperCAmelCase      :     List[str]        =   "こんばんは、㔺界。😀"
           _UpperCAmelCase      :     Any        =   "こんにちは、世界。こんばんは、世界。😀"
           _UpperCAmelCase      :     Union[str, Any]        =   tokenizer.encode(prefix_text + input_text						)
           _UpperCAmelCase      :     Tuple        =   tokenizer.encode(""					,     prefix_text=prefix_text + input_text						)
           _UpperCAmelCase      :     Optional[int]        =   tokenizer.encode(A					,     prefix_text=A						)
           _UpperCAmelCase      :     Tuple        =   tokenizer.decode(A						)
           _UpperCAmelCase      :     Optional[Any]        =   tokenizer.decode(A						)
           _UpperCAmelCase      :     Tuple        =   tokenizer.decode(A						)
           self.assertEqual(A					,     A						)
           self.assertEqual(A					,     A						)
           self.assertEqual(A					,     A						)



      @slow
      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     Dict        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           # Testing tokenization
           _UpperCAmelCase      :     Any        =   "こんにちは、世界。"
           _UpperCAmelCase      :     List[Any]        =   "こんばんは、㔺界。😀"

           _UpperCAmelCase      :     Optional[Any]        =   len(tokenizer.encode(A						)						) - 2
           _UpperCAmelCase      :     List[Any]        =   len(tokenizer.encode(A						)						) - 2

           _UpperCAmelCase      :     List[str]        =   [1] + [0] * (len_prefix + len_text + 1)
           _UpperCAmelCase      :     str        =   [1] * (len_prefix + len_text + 1) + [0]
           _UpperCAmelCase      :     int        =   [1] + [1] * (len_prefix) + [0] * (len_text + 1)

           _UpperCAmelCase      :     Union[str, Any]        =   tokenizer(prefix_text + input_text						).token_type_ids
           _UpperCAmelCase      :     Any        =   tokenizer(""					,     prefix_text=prefix_text + input_text						).token_type_ids
           _UpperCAmelCase      :     List[Any]        =   tokenizer(A					,     prefix_text=A						).token_type_ids
           self.assertListEqual(A					,     A						)
           self.assertListEqual(A					,     A						)
           self.assertListEqual(A					,     A						)



      @slow
      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     str        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           _UpperCAmelCase      :     Dict        =   tokenizer.encode("あンいワ"						)
           _UpperCAmelCase      :     str        =   tokenizer.encode(""					,     prefix_text="あンいワ"						)
           _UpperCAmelCase      :     Dict        =   tokenizer.encode("いワ"					,     prefix_text="あン"						)

           self.assertEqual(tokenizer.decode(A						)					,     tokenizer.decode(A						)						)
           self.assertEqual(tokenizer.decode(A						)					,     tokenizer.decode(A						)						)
           self.assertNotEqual(A					,     A						)
           self.assertNotEqual(A					,     A						)
           self.assertEqual(x_token_a[1]					,     x_token_a[-1]						)  # SEG token
           self.assertEqual(x_token_a[1]					,     x_token_a[3]						)  # SEG token



      @slow
      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Optional[Any]        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           _UpperCAmelCase      :     Tuple        =   [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
           _UpperCAmelCase      :     Tuple        =   tokenizer(A					,     padding=A						)
           _UpperCAmelCase      :     str        =   tokenizer.batch_encode_plus(A					,     padding=A						)

           # fmt: off
           _UpperCAmelCase      :     str        =   [[3_5_9_9_3, 8_6_4_0, 2_5_9_4_8, 3_5_9_9_8, 3_0_6_4_7, 3_5_6_7_5, 3_5_9_9_9, 3_5_9_9_9], [3_5_9_9_3, 1_0_3_8_2, 9_8_6_8, 3_5_9_9_8, 3_0_6_4_6, 9_4_5_9, 3_0_6_4_6, 3_5_6_7_5]]
           _UpperCAmelCase      :     str        =   [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
           _UpperCAmelCase      :     int        =   [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
           # fmt: on
           self.assertListEqual(x_token.input_ids					,     A						)
           self.assertListEqual(x_token.token_type_ids					,     A						)
           self.assertListEqual(x_token.attention_mask					,     A						)
           self.assertListEqual(x_token_a.input_ids					,     A						)
           self.assertListEqual(x_token_a.token_type_ids					,     A						)
           self.assertListEqual(x_token_a.attention_mask					,     A						)



      def 						snake_case_ (					self					:     List[Any]						):
           # Intentionally convert some words to accommodate character fluctuations unique to Japanese
           pass



      def 						snake_case_ (					self					:     int						):
           # tokenizer has no padding token
           pass
