



"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


_lowerCAmelCase    : Optional[int]							=							logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCAmelCase    : Tuple							=							"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[str]						,  SCREAMING_SNAKE_CASE__     :						List[str]						,  SCREAMING_SNAKE_CASE__     :						Any=8    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     _UpperCAmelCase      :     Dict        =   width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[str]						,  SCREAMING_SNAKE_CASE__     :						Dict=512						,  SCREAMING_SNAKE_CASE__     :						int=512    )  ->       str:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[int]        =   pil_image.resize((w, h)						,  resample=Image.BICUBIC						,  reducing_gap=1    )
     _UpperCAmelCase      :     str        =   np.array(pil_image.convert("RGB"    )    )
     _UpperCAmelCase      :     Any        =   arr.astype(np.floataa    ) / 127.5 - 1
     _UpperCAmelCase      :     List[Any]        =   np.transpose(SCREAMING_SNAKE_CASE__						,  [2, 0, 1]    )
     _UpperCAmelCase      :     int        =   torch.from_numpy(SCREAMING_SNAKE_CASE__    ).unsqueeze(0    )
     return image






class      UpperCAmelCase_  (    UpperCamelCase__						):



      def __init__(					self					:     List[Any]					,     A					:     UNetaDConditionModel					,     A					:     DDPMScheduler					,     A					:     VQModel					,     ):
           super().__init__()

           self.register_modules(
               unet=UpperCamelCase_					,     scheduler=UpperCamelCase_					,     movq=UpperCamelCase_					,     )
           _UpperCAmelCase      :     str        =   2 ** (len(self.movq.config.block_out_channels						) - 1)



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     str					,     A					:     Union[str, Any]					,     A					:     int						):
           _UpperCAmelCase      :     Any        =   min(int(num_inference_steps * strength						)					,     UpperCamelCase_						)

           _UpperCAmelCase      :     List[str]        =   max(num_inference_steps - init_timestep					,     0						)
           _UpperCAmelCase      :     Union[str, Any]        =   self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start



      def 						snake_case_ (					self					:     Any					,     A					:     Union[str, Any]					,     A					:     Union[str, Any]					,     A					:     Optional[Any]					,     A					:     Optional[int]					,     A					:     Dict					,     A					:     int					,     A					:     Dict=None						):







           if not isinstance(UpperCamelCase_					,     (torch.Tensor, PIL.Image.Image, list)						):
                raise ValueError(
                    f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCamelCase_						)}'						)

           _UpperCAmelCase      :     str        =   image.to(device=UpperCamelCase_					,     dtype=UpperCamelCase_						)

           _UpperCAmelCase      :     Optional[Any]        =   batch_size * num_images_per_prompt

           if image.shape[1] == 4:
                _UpperCAmelCase      :     int        =   image

           else:
                if isinstance(UpperCamelCase_					,     UpperCamelCase_						) and len(UpperCamelCase_						) != batch_size:
                     raise ValueError(
                         f'You have passed a list of generators of length {len(UpperCamelCase_						)}, but requested an effective batch'
                         f' size of {batch_size}. Make sure the batch size matches the length of the generators.'						)

                elif isinstance(UpperCamelCase_					,     UpperCamelCase_						):
                     _UpperCAmelCase      :     str        =   [
                         self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(UpperCamelCase_						)
                     ]
                     _UpperCAmelCase      :     int        =   torch.cat(UpperCamelCase_					,     dim=0						)
                else:
                     _UpperCAmelCase      :     str        =   self.movq.encode(UpperCamelCase_						).latent_dist.sample(UpperCamelCase_						)

                _UpperCAmelCase      :     Union[str, Any]        =   self.movq.config.scaling_factor * init_latents

           _UpperCAmelCase      :     List[str]        =   torch.cat([init_latents]					,     dim=0						)

           _UpperCAmelCase      :     Tuple        =   init_latents.shape
           _UpperCAmelCase      :     Optional[int]        =   randn_tensor(UpperCamelCase_					,     generator=UpperCamelCase_					,     device=UpperCamelCase_					,     dtype=UpperCamelCase_						)

           # get latents
           _UpperCAmelCase      :     Optional[Any]        =   self.scheduler.add_noise(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_						)

           _UpperCAmelCase      :     int        =   init_latents

           return latents



      def 						snake_case_ (					self					:     str					,     A					:     List[Any]=0						):







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

           _UpperCAmelCase      :     List[Any]        =   torch.device(f'cuda:{gpu_id}'						)

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



      def 						snake_case_ (					self					:     int					,     A					:     Optional[Any]=0						):







           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."						)

           _UpperCAmelCase      :     List[Any]        =   torch.device(f'cuda:{gpu_id}'						)

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

           _UpperCAmelCase      :     Optional[int]        =   None
           for cpu_offloaded_model in [self.unet, self.movq]:
                _UpperCAmelCase    ,	_UpperCAmelCase      :     Union[str, Any]        =   cpu_offload_with_hook(UpperCamelCase_					,     UpperCamelCase_					,     prev_module_hook=UpperCamelCase_						)

           # We'll offload the last model manually.
           _UpperCAmelCase      :     Any        =   hook



      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 						snake_case_ (					self					:     List[str]						):







           if not hasattr(self.unet					,     "_hf_hook"						):
                return self.device
           for module in self.unet.modules():
                if (
                    hasattr(UpperCamelCase_					,     "_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(UpperCamelCase_						)
      def __call__(					self					:     List[Any]					,     A					:     Union[torch.FloatTensor, List[torch.FloatTensor]]					,     A					:     Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]					,     A					:     Union[torch.FloatTensor, List[torch.FloatTensor]]					,     A					:     int = 5_1_2					,     A					:     int = 5_1_2					,     A					:     int = 1_0_0					,     A					:     float = 4.0					,     A					:     float = 0.3					,     A					:     int = 1					,     A					:     Optional[Union[torch.Generator, List[torch.Generator]]] = None					,     A					:     Optional[str] = "pil"					,     A					:     bool = True					,     ):
           _UpperCAmelCase      :     List[Any]        =   self._execution_device

           _UpperCAmelCase      :     Tuple        =   guidance_scale > 1.0

           if isinstance(UpperCamelCase_					,     UpperCamelCase_						):
                _UpperCAmelCase      :     int        =   torch.cat(UpperCamelCase_					,     dim=0						)
           _UpperCAmelCase      :     Optional[Any]        =   image_embeds.shape[0]
           if isinstance(UpperCamelCase_					,     UpperCamelCase_						):
                _UpperCAmelCase      :     Any        =   torch.cat(UpperCamelCase_					,     dim=0						)

           if do_classifier_free_guidance:
                _UpperCAmelCase      :     Tuple        =   image_embeds.repeat_interleave(UpperCamelCase_					,     dim=0						)
                _UpperCAmelCase      :     List[str]        =   negative_image_embeds.repeat_interleave(UpperCamelCase_					,     dim=0						)

           _UpperCAmelCase      :     Tuple        =   torch.cat([negative_image_embeds, image_embeds]					,     dim=0						).to(dtype=self.unet.dtype					,     device=UpperCamelCase_						)

           if not isinstance(UpperCamelCase_					,     UpperCamelCase_						):
                _UpperCAmelCase      :     Tuple        =   [image]
           if not all(isinstance(UpperCamelCase_					,     (PIL.Image.Image, torch.Tensor)						) for i in image						):
                raise ValueError(
                    f'Input is in incorrect format: {[type(UpperCamelCase_						) for i in image]}. Currently, we only support  PIL image and pytorch tensor'						)

           _UpperCAmelCase      :     Any        =   torch.cat([prepare_image(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_						) for i in image]					,     dim=0						)
           _UpperCAmelCase      :     List[Any]        =   image.to(dtype=image_embeds.dtype					,     device=UpperCamelCase_						)

           _UpperCAmelCase      :     Any        =   self.movq.encode(UpperCamelCase_						)["latents"]
           _UpperCAmelCase      :     Optional[Any]        =   latents.repeat_interleave(UpperCamelCase_					,     dim=0						)
           self.scheduler.set_timesteps(UpperCamelCase_					,     device=UpperCamelCase_						)
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   self.get_timesteps(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_						)
           _UpperCAmelCase      :     Optional[int]        =   timesteps[:1].repeat(batch_size * num_images_per_prompt						)
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   downscale_height_and_width(UpperCamelCase_					,     UpperCamelCase_					,     self.movq_scale_factor						)
           _UpperCAmelCase      :     Optional[int]        =   self.prepare_latents(
               UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     image_embeds.dtype					,     UpperCamelCase_					,     UpperCamelCase_						)
           for i, t in enumerate(self.progress_bar(UpperCamelCase_						)						):
                # expand the latents if we are doing classifier free guidance
                _UpperCAmelCase      :     str        =   torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

                _UpperCAmelCase      :     Union[str, Any]        =   {"image_embeds": image_embeds}
                _UpperCAmelCase      :     str        =   self.unet(
                    sample=UpperCamelCase_					,     timestep=UpperCamelCase_					,     encoder_hidden_states=UpperCamelCase_					,     added_cond_kwargs=UpperCamelCase_					,     return_dict=UpperCamelCase_					,     )[0]

                if do_classifier_free_guidance:
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   noise_pred.split(latents.shape[1]					,     dim=1						)
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     Any        =   noise_pred.chunk(2						)
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   variance_pred.chunk(2						)
                     _UpperCAmelCase      :     List[Any]        =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                     _UpperCAmelCase      :     Optional[Any]        =   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"]
                ):
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     Any        =   noise_pred.split(latents.shape[1]					,     dim=1						)

                # compute the previous noisy sample x_t -> x_t-1
                _UpperCAmelCase      :     List[str]        =   self.scheduler.step(
                    UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     generator=UpperCamelCase_					,     )[0]

           # post-processing
           _UpperCAmelCase      :     Dict        =   self.movq.decode(UpperCamelCase_					,     force_not_quantize=UpperCamelCase_						)["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"]:
                _UpperCAmelCase      :     int        =   image * 0.5 + 0.5
                _UpperCAmelCase      :     Dict        =   image.clamp(0					,     1						)
                _UpperCAmelCase      :     Union[str, Any]        =   image.cpu().permute(0					,     2					,     3					,     1						).float().numpy()

           if output_type == "pil":
                _UpperCAmelCase      :     Optional[int]        =   self.numpy_to_pil(UpperCamelCase_						)

           if not return_dict:
                return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_						)





"""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
