






from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A__	:			Any			   = logging.get_logger(__name__)  # pylint: disable=invalid-name

A__	:			Optional[Any]			   = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_=8				):






				'''simple docstring'''
				lowercase__    =			height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				lowercase__    =			width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor




class       _UpperCAmelCase					(       lowercase__	):

				"""simple docstring"""




				def __init__(     self						:    List[Any],  lowerCamelCase						:    UNetaDConditionModel,  lowerCamelCase						:    DDPMScheduler,  lowerCamelCase						:    VQModel,  ):

								'''simple docstring'''
								super().__init__()

								self.register_modules(
								    unet=SCREAMING_SNAKE_CASE__,  scheduler=SCREAMING_SNAKE_CASE__,  movq=SCREAMING_SNAKE_CASE__,  )
								lowercase__    =			2 ** (len(self.movq.config.block_out_channels  ) - 1)




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    str,  lowerCamelCase						:    int,  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								if latents is None:
												lowercase__    =			randn_tensor(SCREAMING_SNAKE_CASE__,  generator=SCREAMING_SNAKE_CASE__,  device=SCREAMING_SNAKE_CASE__,  dtype=SCREAMING_SNAKE_CASE__  )
								else:
												if latents.shape != shape:
																raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
												lowercase__    =			latents.to(SCREAMING_SNAKE_CASE__  )

								lowercase__    =			latents * scheduler.init_noise_sigma
								return latents




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    Optional[Any]=0  ):

								'''simple docstring'''
								if is_accelerate_available():
												from accelerate import cpu_offload
								else:
												raise ImportError('''Please install accelerate via `pip install accelerate`'''  )

								lowercase__    =			torch.device(F"""cuda:{gpu_id}"""  )

								lowercase__    =			[
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																cpu_offload(SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__  )




				def 				lowercase__   (     self						:    Tuple,  lowerCamelCase						:    Union[str, Any]=0  ):

								'''simple docstring'''
								if is_accelerate_available() and is_accelerate_version('''>=''',  '''0.17.0.dev0'''  ):
												from accelerate import cpu_offload_with_hook
								else:
												raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''  )

								lowercase__    =			torch.device(F"""cuda:{gpu_id}"""  )

								if self.device.type != "cpu":
												self.to('''cpu''',  silence_dtype_warnings=SCREAMING_SNAKE_CASE__  )
												torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

								lowercase__    =			None
								for cpu_offloaded_model in [self.unet, self.movq]:
												lowercase__    =			cpu_offload_with_hook(SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__,  prev_module_hook=SCREAMING_SNAKE_CASE__  )

								# We'll offload the last model manually.
								lowercase__    =			hook




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								if not hasattr(self.unet,  '''_hf_hook'''  ):
												return self.device
								for module in self.unet.modules():
												if (
												    hasattr(SCREAMING_SNAKE_CASE__,  '''_hf_hook'''  )
												    and hasattr(module._hf_hook,  '''execution_device'''  )
												    and module._hf_hook.execution_device is not None
												):
																return torch.device(module._hf_hook.execution_device  )
								return self.device




				@torch.no_grad()
				@replace_example_docstring(SCREAMING_SNAKE_CASE__  )
				def __call__(     self						:    List[Any],  lowerCamelCase						:    Union[torch.FloatTensor, List[torch.FloatTensor]],  lowerCamelCase						:    Union[torch.FloatTensor, List[torch.FloatTensor]],  lowerCamelCase						:    torch.FloatTensor,  lowerCamelCase						:    int = 512,  lowerCamelCase						:    int = 512,  lowerCamelCase						:    int = 100,  lowerCamelCase						:    float = 4.0,  lowerCamelCase						:    int = 1,  lowerCamelCase						:    Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase						:    Optional[torch.FloatTensor] = None,  lowerCamelCase						:    Optional[str] = "pil",  lowerCamelCase						:    bool = True,  ):

								'''simple docstring'''
								lowercase__    =			self._execution_device

								lowercase__    =			guidance_scale > 1.0

								if isinstance(SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__  ):
												lowercase__    =			torch.cat(SCREAMING_SNAKE_CASE__,  dim=0  )
								if isinstance(SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__  ):
												lowercase__    =			torch.cat(SCREAMING_SNAKE_CASE__,  dim=0  )
								if isinstance(SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__  ):
												lowercase__    =			torch.cat(SCREAMING_SNAKE_CASE__,  dim=0  )

								lowercase__    =			image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
												lowercase__    =			image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__,  dim=0  )
												lowercase__    =			negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__,  dim=0  )
												lowercase__    =			hint.repeat_interleave(SCREAMING_SNAKE_CASE__,  dim=0  )

								lowercase__    =			torch.cat([negative_image_embeds, image_embeds],  dim=0  ).to(dtype=self.unet.dtype,  device=SCREAMING_SNAKE_CASE__  )
								lowercase__    =			torch.cat([hint, hint],  dim=0  ).to(dtype=self.unet.dtype,  device=SCREAMING_SNAKE_CASE__  )
								self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__,  device=SCREAMING_SNAKE_CASE__  )
								lowercase__    =			self.scheduler.timesteps

								lowercase__    =			self.movq.config.latent_channels

								lowercase__    =			downscale_height_and_width(SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__,  self.movq_scale_factor  )

								# create initial latent
								lowercase__    =			self.prepare_latents(
								    (batch_size, num_channels_latents, height, width),  image_embeds.dtype,  SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__,  self.scheduler,  )

								for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__  )  ):
												# expand the latents if we are doing classifier free guidance
												lowercase__    =			torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

												lowercase__    =			{'image_embeds': image_embeds, 'hint': hint}
												lowercase__    =			self.unet(
												    sample=SCREAMING_SNAKE_CASE__,  timestep=SCREAMING_SNAKE_CASE__,  encoder_hidden_states=SCREAMING_SNAKE_CASE__,  added_cond_kwargs=SCREAMING_SNAKE_CASE__,  return_dict=SCREAMING_SNAKE_CASE__,  )[0]

												if do_classifier_free_guidance:
																lowercase__    =			noise_pred.split(latents.shape[1],  dim=1  )
																lowercase__    =			noise_pred.chunk(2  )
																lowercase__    =			variance_pred.chunk(2  )
																lowercase__    =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																lowercase__    =			torch.cat([noise_pred, variance_pred_text],  dim=1  )

												if not (
												    hasattr(self.scheduler.config,  '''variance_type'''  )
												    and self.scheduler.config.variance_type in ["learned", "learned_range"]
												):
																lowercase__    =			noise_pred.split(latents.shape[1],  dim=1  )

												# compute the previous noisy sample x_t -> x_t-1
												lowercase__    =			self.scheduler.step(
												    SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__,  generator=SCREAMING_SNAKE_CASE__,  )[0]
								# post-processing
								lowercase__    =			self.movq.decode(SCREAMING_SNAKE_CASE__,  force_not_quantize=SCREAMING_SNAKE_CASE__  )['sample']

								if output_type not in ["pt", "np", "pil"]:
												raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""  )

								if output_type in ["np", "pil"]:
												lowercase__    =			image * 0.5 + 0.5
												lowercase__    =			image.clamp(0,  1  )
												lowercase__    =			image.cpu().permute(0,  2,  3,  1  ).float().numpy()

								if output_type == "pil":
												lowercase__    =			self.numpy_to_pil(SCREAMING_SNAKE_CASE__  )

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__  )








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


