


import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]				)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		str							=  SwinaSRConfig()

       if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
              a_					:		Tuple							=  4
       elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
              a_					:		int							=  4
              a_					:		str							=  48
              a_					:		Union[str, Any]							=  'pixelshuffle_aux'
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              a_					:		List[Any]							=  [6, 6, 6, 6]
              a_					:		Tuple							=  60
              a_					:		Union[str, Any]							=  [6, 6, 6, 6]
              a_					:		Optional[int]							=  'pixelshuffledirect'
       elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
              a_					:		Tuple							=  4
              a_					:		Optional[int]							=  'nearest+conv'
       elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
              a_					:		List[str]							=  1
              a_					:		Optional[int]							=  1
              a_					:		int							=  1_26
              a_					:		Optional[Any]							=  7
              a_					:		Optional[int]							=  255.0
              a_					:		Tuple							=  ''

       return config







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple  ,    __A :			Optional[int]				)	->		List[str]:

       """simple docstring"""

       if "patch_embed.proj" in name and "layers" not in name:
              a_					:		List[Any]							=  name.replace('patch_embed.proj'  ,    'embeddings.patch_embeddings.projection'				)
       if "patch_embed.norm" in name:
              a_					:		List[Any]							=  name.replace('patch_embed.norm'  ,    'embeddings.patch_embeddings.layernorm'				)
       if "layers" in name:
              a_					:		Any							=  name.replace('layers'  ,    'encoder.stages'				)
       if "residual_group.blocks" in name:
              a_					:		Tuple							=  name.replace('residual_group.blocks'  ,    'layers'				)
       if "attn.proj" in name:
              a_					:		Union[str, Any]							=  name.replace('attn.proj'  ,    'attention.output.dense'				)
       if "attn" in name:
              a_					:		Tuple							=  name.replace('attn'  ,    'attention.self'				)
       if "norm1" in name:
              a_					:		Optional[int]							=  name.replace('norm1'  ,    'layernorm_before'				)
       if "norm2" in name:
              a_					:		Union[str, Any]							=  name.replace('norm2'  ,    'layernorm_after'				)
       if "mlp.fc1" in name:
              a_					:		Union[str, Any]							=  name.replace('mlp.fc1'  ,    'intermediate.dense'				)
       if "mlp.fc2" in name:
              a_					:		List[str]							=  name.replace('mlp.fc2'  ,    'output.dense'				)
       if "q_bias" in name:
              a_					:		str							=  name.replace('q_bias'  ,    'query.bias'				)
       if "k_bias" in name:
              a_					:		Optional[int]							=  name.replace('k_bias'  ,    'key.bias'				)
       if "v_bias" in name:
              a_					:		Optional[int]							=  name.replace('v_bias'  ,    'value.bias'				)
       if "cpb_mlp" in name:
              a_					:		List[str]							=  name.replace('cpb_mlp'  ,    'continuous_position_bias_mlp'				)
       if "patch_embed.proj" in name:
              a_					:		List[Any]							=  name.replace('patch_embed.proj'  ,    'patch_embed.projection'				)

       if name == "norm.weight":
              a_					:		Union[str, Any]							=  'layernorm.weight'
       if name == "norm.bias":
              a_					:		Optional[int]							=  'layernorm.bias'

       if "conv_first" in name:
              a_					:		Tuple							=  name.replace('conv_first'  ,    'first_convolution'				)

       if (
           "upsample" in name
           or "conv_before_upsample" in name
           or "conv_bicubic" in name
           or "conv_up" in name
           or "conv_hr" in name
           or "conv_last" in name
           or "aux" in name
       ):
              # heads
              if "conv_last" in name:
                     a_					:		Optional[int]							=  name.replace('conv_last'  ,    'final_convolution'				)
              if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
                     if "conv_before_upsample.0" in name:
                            a_					:		Union[str, Any]							=  name.replace('conv_before_upsample.0'  ,    'conv_before_upsample'				)
                     if "upsample.0" in name:
                            a_					:		Union[str, Any]							=  name.replace('upsample.0'  ,    'upsample.convolution_0'				)
                     if "upsample.2" in name:
                            a_					:		Union[str, Any]							=  name.replace('upsample.2'  ,    'upsample.convolution_1'				)
                     a_					:		Optional[int]							=  'upsample.' + name
              elif config.upsampler == "pixelshuffledirect":
                     a_					:		Union[str, Any]							=  name.replace('upsample.0.weight'  ,    'upsample.conv.weight'				)
                     a_					:		Union[str, Any]							=  name.replace('upsample.0.bias'  ,    'upsample.conv.bias'				)
              else:
                     pass
       else:
              a_					:		List[str]							=  'swin2sr.' + name

       return name







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			List[str]				)	->		Dict:

       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              a_					:		List[Any]							=  orig_state_dict.pop(__A				)

              if "qkv" in key:
                     a_					:		List[Any]							=  key.split('.'				)
                     a_					:		Optional[Any]							=  int(key_split[1]				)
                     a_					:		Optional[Any]							=  int(key_split[4]				)
                     a_					:		Tuple							=  config.embed_dim

                     if "weight" in key:
                            a_					:		Union[str, Any]							=  val[:dim, :]
                            a_					:		Tuple							=  val[dim : dim * 2, :]
                            a_					:		Union[str, Any]							=  val[-dim:, :]
                     else:
                            a_					:		Tuple							=  val[:dim]
                            a_					:		Optional[Any]							=  val[dim : dim * 2]
                            a_					:		Any							=  val[-dim:]
                     pass
              else:
                     a_					:		Union[str, Any]							=  val

       return orig_state_dict







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple  ,    __A :			Optional[int]  ,    __A :			List[Any]				)	->		Optional[int]:

       """simple docstring"""

       a_					:		Any							=  get_config(__A				)
       a_					:		Any							=  SwinaSRForImageSuperResolution(__A				)
       model.eval()

       a_					:		List[str]							=  torch.hub.load_state_dict_from_url(__A  ,    map_location='cpu'				)
       a_					:		List[Any]							=  convert_state_dict(__A  ,    __A				)
       a_							,		a_					:		Tuple							=  model.load_state_dict(__A  ,    strict=__A				)

       if len(__A				) > 0:
              raise ValueError('Missing keys when converting: {}'.format(__A				)				)
       for key in unexpected_keys:
              if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
                     raise ValueError(F"""Unexpected key {key} in state_dict"""				)

    # verify values
       a_					:		Union[str, Any]							=  'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
       a_					:		Tuple							=  Image.open(requests.get(__A  ,    stream=__A				).raw				).convert('RGB'				)
       a_					:		Union[str, Any]							=  SwinaSRImageProcessor()
       # pixel_values = processor(image, return_tensors="pt").pixel_values

       a_					:		Any							=  1_26 if 'Jpeg' in checkpoint_url else 2_56
       a_					:		Optional[int]							=  Compose(
           [
               Resize((image_size, image_size)				),
               ToTensor(),
               Normalize(mean=[0.485, 0.456, 0.406]  ,    std=[0.229, 0.224, 0.225]				),
           ]				)
       a_					:		str							=  transforms(__A				).unsqueeze(0				)

       if config.num_channels == 1:
              a_					:		int							=  pixel_values[:, 0, :, :].unsqueeze(1				)

       a_					:		int							=  model(__A				)

       # assert values
       if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
              a_					:		Optional[int]							=  torch.Size([1, 3, 5_12, 5_12]				)
              a_					:		str							=  torch.tensor(
                  [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]				)
       elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
              a_					:		Dict							=  torch.Size([1, 3, 10_24, 10_24]				)
              a_					:		Union[str, Any]							=  torch.tensor(
                  [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]				)
       elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
              # TODO values didn't match exactly here
              a_					:		Union[str, Any]							=  torch.Size([1, 3, 10_24, 10_24]				)
              a_					:		int							=  torch.tensor(
                  [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]				)
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              a_					:		Union[str, Any]							=  torch.Size([1, 3, 5_12, 5_12]				)
              a_					:		Optional[int]							=  torch.tensor(
                  [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]				)
       elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
              a_					:		Any							=  torch.Size([1, 3, 10_24, 10_24]				)
              a_					:		Tuple							=  torch.tensor(
                  [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]				)

       assert (
           outputs.reconstruction.shape == expected_shape
       ), F"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
       assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]  ,    __A  ,    atol=1e-3				)
       print('Looks ok!'				)

       a_					:		int							=  {
           'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
               'swin2SR-classical-sr-x2-64'
           ),
           'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
               'swin2SR-classical-sr-x4-64'
           ),
           'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
               'swin2SR-compressed-sr-x4-48'
           ),
           'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
               'swin2SR-lightweight-x2-64'
           ),
           'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
               'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
           ),
       }
       a_					:		List[str]							=  url_to_name[checkpoint_url]

       if pytorch_dump_folder_path is not None:
              print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
              model.save_pretrained(__A				)
              print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
              processor.save_pretrained(__A				)

       if push_to_hub:
              model.push_to_hub(F"""caidas/{model_name}"""				)
              processor.push_to_hub(F"""caidas/{model_name}"""				)


if __name__ == "__main__":
   UpperCAmelCase_      : Tuple  					=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--checkpoint_url',
       default='https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth',
       type=str,
       help='URL of the original Swin2SR checkpoint you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the converted model to the hub.')

   UpperCAmelCase_      : List[str]  					=       parser.parse_args()
   convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






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  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      snake_case__				:     Any        =						GPTSanJapaneseTokenizer
      snake_case__				:     Tuple        =						False
      snake_case__				:     str        =						{'''do_clean_text''': False, '''add_prefix_space''': False}
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			str:
             super().setUp()

             # fmt: off
             a_					:		Union[str, 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
             a_					:		int							=  {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
             a_					:		List[Any]							=  {'unk_token': '<unk>'}

             a_					:		Any							=  os.path.join(self.tmpdirname     ,							VOCAB_FILES_NAMES['vocab_file']     )
             a_					:		List[str]							=  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(SCREAMING_SNAKE_CASE__     )     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							**SCREAMING_SNAKE_CASE__				:       Tuple     )		->			int:
             kwargs.update(self.special_tokens_map     )
             return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			int:
             a_					:		Optional[int]							=  'こんにちは、世界。 \nこんばんは、㔺界。😀'
             a_					:		List[str]							=  'こんにちは、世界。 \nこんばんは、世界。😀'
             return input_text, output_text
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Dict:
             a_							,		a_					:		Union[str, Any]							=  self.get_input_output_texts(SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  tokenizer.decode(SCREAMING_SNAKE_CASE__     ,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     )
             return text, ids
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             pass  # TODO add if relevant
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Any:
             pass  # TODO add if relevant
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Tuple:
             pass  # TODO add if relevant
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			List[str]:
             a_					:		List[str]							=  self.get_tokenizer()

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

             # Testing conversion to ids without special tokens
             a_					:		Tuple							=  [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
             a_					:		List[Any]							=  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             # Testing conversion to ids with special tokens
             a_					:		int							=  tokens + [tokenizer.unk_token]
             a_					:		int							=  [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
             a_					:		Tuple							=  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Dict:
             a_					:		Union[str, Any]							=  self.get_tokenizer()

             # Testing tokenization
             a_					:		Dict							=  'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
             a_					:		List[Any]							=  'こんにちは、、、、世界。こんばんは、、、、世界。'
             a_					:		Any							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[Any]							=  tokenizer.decode(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Dict:
             a_					:		Tuple							=  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'     )

             # Testing tokenization
             a_					:		List[Any]							=  'こんにちは、世界。'
             a_					:		int							=  'こんばんは、㔺界。😀'
             a_					:		Dict							=  'こんにちは、世界。こんばんは、世界。😀'
             a_					:		Optional[int]							=  tokenizer.encode(prefix_text + input_text     )
             a_					:		Any							=  tokenizer.encode(''     ,							prefix_text=prefix_text + input_text     )
             a_					:		Union[str, Any]							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     ,							prefix_text=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  tokenizer.decode(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  tokenizer.decode(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  tokenizer.decode(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             self.assertEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             self.assertEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Optional[int]:
             a_					:		Tuple							=  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'     )

             # Testing tokenization
             a_					:		str							=  'こんにちは、世界。'
             a_					:		List[str]							=  'こんばんは、㔺界。😀'

             a_					:		str							=  len(tokenizer.encode(SCREAMING_SNAKE_CASE__     )     ) - 2
             a_					:		Tuple							=  len(tokenizer.encode(SCREAMING_SNAKE_CASE__     )     ) - 2

             a_					:		Optional[Any]							=  [1] + [0] * (len_prefix + len_text + 1)
             a_					:		Optional[Any]							=  [1] * (len_prefix + len_text + 1) + [0]
             a_					:		Tuple							=  [1] + [1] * (len_prefix) + [0] * (len_text + 1)

             a_					:		List[str]							=  tokenizer(prefix_text + input_text     ).token_type_ids
             a_					:		Union[str, Any]							=  tokenizer(''     ,							prefix_text=prefix_text + input_text     ).token_type_ids
             a_					:		Any							=  tokenizer(SCREAMING_SNAKE_CASE__     ,							prefix_text=SCREAMING_SNAKE_CASE__     ).token_type_ids
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			int:
             a_					:		str							=  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'     )

             a_					:		Optional[int]							=  tokenizer.encode('あンいワ'     )
             a_					:		Dict							=  tokenizer.encode(''     ,							prefix_text='あンいワ'     )
             a_					:		Dict							=  tokenizer.encode('いワ'     ,							prefix_text='あン'     )

             self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE__     )     ,							tokenizer.decode(SCREAMING_SNAKE_CASE__     )     )
             self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE__     )     ,							tokenizer.decode(SCREAMING_SNAKE_CASE__     )     )
             self.assertNotEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             self.assertNotEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             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        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[Any]:
             a_					:		List[str]							=  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'     )

             a_					:		Optional[Any]							=  [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
             a_					:		List[str]							=  tokenizer(SCREAMING_SNAKE_CASE__     ,							padding=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE__     ,							padding=SCREAMING_SNAKE_CASE__     )

             # fmt: off
             a_					:		List[Any]							=  [[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]]
             a_					:		Any							=  [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
             a_					:		List[Any]							=  [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
             # fmt: on
             self.assertListEqual(x_token.input_ids     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(x_token.token_type_ids     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(x_token.attention_mask     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(x_token_a.input_ids     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(x_token_a.token_type_ids     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(x_token_a.attention_mask     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict:
             # Intentionally convert some words to accommodate character fluctuations unique to Japanese
             pass

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             # tokenizer has no padding token
             pass


