



"""simple docstring"""




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetaImageProcessor






class      UpperCAmelCase_  (    unittest.TestCase						):



      def __init__(					self					:     int					,     A					:     str					,     A					:     Union[str, Any]=7					,     A					:     Union[str, Any]=3					,     A					:     Any=3_0					,     A					:     int=4_0_0					,     A					:     Dict=True					,     A					:     Optional[Any]=None					,     A					:     Union[str, Any]=True					,     A					:     Dict=[0.5, 0.5, 0.5]					,     A					:     List[Any]=[0.5, 0.5, 0.5]					,     A					:     Tuple=True					,     A					:     int=1 / 2_5_5					,     A					:     Union[str, Any]=True					,     ):
           # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
           _UpperCAmelCase      :     Optional[Any]        =   size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
           _UpperCAmelCase      :     Union[str, Any]        =   parent
           _UpperCAmelCase      :     Any        =   batch_size
           _UpperCAmelCase      :     List[Any]        =   num_channels
           _UpperCAmelCase      :     int        =   min_resolution
           _UpperCAmelCase      :     List[str]        =   max_resolution
           _UpperCAmelCase      :     Tuple        =   do_resize
           _UpperCAmelCase      :     List[str]        =   size
           _UpperCAmelCase      :     List[Any]        =   do_normalize
           _UpperCAmelCase      :     List[Any]        =   image_mean
           _UpperCAmelCase      :     Dict        =   image_std
           _UpperCAmelCase      :     Optional[int]        =   do_rescale
           _UpperCAmelCase      :     List[Any]        =   rescale_factor
           _UpperCAmelCase      :     Any        =   do_pad



      def 						snake_case_ (					self					:     Union[str, Any]						):
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
           }



      def 						snake_case_ (					self					:     List[str]					,     A					:     int					,     A					:     Optional[int]=False						):
           if not batched:
                _UpperCAmelCase      :     Union[str, Any]        =   image_inputs[0]
                if isinstance(A					,     Image.Image						):
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   image.size
                else:
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   image.shape[1], image.shape[2]
                if w < h:
                     _UpperCAmelCase      :     Tuple        =   int(self.size["shortest_edge"] * h / w						)
                     _UpperCAmelCase      :     List[Any]        =   self.size["shortest_edge"]
                elif w > h:
                     _UpperCAmelCase      :     List[Any]        =   self.size["shortest_edge"]
                     _UpperCAmelCase      :     List[str]        =   int(self.size["shortest_edge"] * w / h						)
                else:
                     _UpperCAmelCase      :     int        =   self.size["shortest_edge"]
                     _UpperCAmelCase      :     Dict        =   self.size["shortest_edge"]

           else:
                _UpperCAmelCase      :     str        =   []
                for image in image_inputs:
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   self.get_expected_values([image]						)
                     expected_values.append((expected_height, expected_width)						)
                _UpperCAmelCase      :     Optional[Any]        =   max(A					,     key=lambda A						: item[0]						)[0]
                _UpperCAmelCase      :     Any        =   max(A					,     key=lambda A						: item[1]						)[1]

           return expected_height, expected_width







@require_torch
@require_vision
class      UpperCAmelCase_  (    _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Tuple								=			DetaImageProcessor if is_vision_available() else None



      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase      :     int        =   DetaImageProcessingTester(self						)



      @property
      def 						snake_case_ (					self					:     List[Any]						):
           return self.image_processor_tester.prepare_image_processor_dict()



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     List[Any]        =   self.image_processing_class(**self.image_processor_dict						)
           self.assertTrue(hasattr(A					,     "image_mean"						)						)
           self.assertTrue(hasattr(A					,     "image_std"						)						)
           self.assertTrue(hasattr(A					,     "do_normalize"						)						)
           self.assertTrue(hasattr(A					,     "do_resize"						)						)
           self.assertTrue(hasattr(A					,     "do_rescale"						)						)
           self.assertTrue(hasattr(A					,     "do_pad"						)						)
           self.assertTrue(hasattr(A					,     "size"						)						)



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     List[str]        =   self.image_processing_class.from_dict(self.image_processor_dict						)
           self.assertEqual(image_processor.size					,     {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}						)
           self.assertEqual(image_processor.do_pad					,     A						)



      def 						snake_case_ (					self					:     str						):
           pass



      def 						snake_case_ (					self					:     str						):
           # Initialize image_processing
           _UpperCAmelCase      :     Tuple        =   self.image_processing_class(**self.image_processor_dict						)
           # create random PIL images
           _UpperCAmelCase      :     Tuple        =   prepare_image_inputs(self.image_processor_tester					,     equal_resolution=A						)
           for image in image_inputs:
                self.assertIsInstance(A					,     Image.Image						)

           # Test not batched input
           _UpperCAmelCase      :     List[Any]        =   image_processing(image_inputs[0]					,     return_tensors="pt"						).pixel_values

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[Any]        =   self.image_processor_tester.get_expected_values(A						)

           self.assertEqual(
               encoded_images.shape					,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,     )

           # Test batched
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   self.image_processor_tester.get_expected_values(A					,     batched=A						)

           _UpperCAmelCase      :     Optional[int]        =   image_processing(A					,     return_tensors="pt"						).pixel_values
           self.assertEqual(
               encoded_images.shape					,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,     )



      def 						snake_case_ (					self					:     Union[str, Any]						):
           # Initialize image_processing
           _UpperCAmelCase      :     Optional[Any]        =   self.image_processing_class(**self.image_processor_dict						)
           # create random numpy tensors
           _UpperCAmelCase      :     int        =   prepare_image_inputs(self.image_processor_tester					,     equal_resolution=A					,     numpify=A						)
           for image in image_inputs:
                self.assertIsInstance(A					,     np.ndarray						)

           # Test not batched input
           _UpperCAmelCase      :     str        =   image_processing(image_inputs[0]					,     return_tensors="pt"						).pixel_values

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   self.image_processor_tester.get_expected_values(A						)

           self.assertEqual(
               encoded_images.shape					,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,     )

           # Test batched
           _UpperCAmelCase      :     int        =   image_processing(A					,     return_tensors="pt"						).pixel_values

           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   self.image_processor_tester.get_expected_values(A					,     batched=A						)

           self.assertEqual(
               encoded_images.shape					,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,     )



      def 						snake_case_ (					self					:     Tuple						):
           # Initialize image_processing
           _UpperCAmelCase      :     List[str]        =   self.image_processing_class(**self.image_processor_dict						)
           # create random PyTorch tensors
           _UpperCAmelCase      :     Dict        =   prepare_image_inputs(self.image_processor_tester					,     equal_resolution=A					,     torchify=A						)
           for image in image_inputs:
                self.assertIsInstance(A					,     torch.Tensor						)

           # Test not batched input
           _UpperCAmelCase      :     Optional[int]        =   image_processing(image_inputs[0]					,     return_tensors="pt"						).pixel_values

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Union[str, Any]        =   self.image_processor_tester.get_expected_values(A						)

           self.assertEqual(
               encoded_images.shape					,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,     )

           # Test batched
           _UpperCAmelCase      :     List[str]        =   image_processing(A					,     return_tensors="pt"						).pixel_values

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Any        =   self.image_processor_tester.get_expected_values(A					,     batched=A						)

           self.assertEqual(
               encoded_images.shape					,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )					,     )



      @slow
      def 						snake_case_ (					self					:     Optional[int]						):
           # prepare image and target
           _UpperCAmelCase      :     str        =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
           with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"					,     "r"						) as f:
                _UpperCAmelCase      :     int        =   json.loads(f.read()						)

           _UpperCAmelCase      :     str        =   {"image_id": 3_9_7_6_9, "annotations": target}

           # encode them
           _UpperCAmelCase      :     Union[str, Any]        =   DetaImageProcessor()
           _UpperCAmelCase      :     Optional[int]        =   image_processing(images=A					,     annotations=A					,     return_tensors="pt"						)

           # verify pixel values
           _UpperCAmelCase      :     Optional[int]        =   torch.Size([1, 3, 8_0_0, 1_0_6_6]						)
           self.assertEqual(encoding["pixel_values"].shape					,     A						)

           _UpperCAmelCase      :     Optional[int]        =   torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
           self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,     A					,     atol=1e-4						)						)

           # verify area
           _UpperCAmelCase      :     Union[str, Any]        =   torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,     A						)						)
           # verify boxes
           _UpperCAmelCase      :     Dict        =   torch.Size([6, 4]						)
           self.assertEqual(encoding["labels"][0]["boxes"].shape					,     A						)
           _UpperCAmelCase      :     List[str]        =   torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,     A					,     atol=1e-3						)						)
           # verify image_id
           _UpperCAmelCase      :     Union[str, Any]        =   torch.tensor([3_9_7_6_9]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,     A						)						)
           # verify is_crowd
           _UpperCAmelCase      :     List[str]        =   torch.tensor([0, 0, 0, 0, 0, 0]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,     A						)						)
           # verify class_labels
           _UpperCAmelCase      :     Any        =   torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,     A						)						)
           # verify orig_size
           _UpperCAmelCase      :     Any        =   torch.tensor([4_8_0, 6_4_0]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,     A						)						)
           # verify size
           _UpperCAmelCase      :     Dict        =   torch.tensor([8_0_0, 1_0_6_6]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,     A						)						)



      @slow
      def 						snake_case_ (					self					:     Union[str, Any]						):
           # prepare image, target and masks_path
           _UpperCAmelCase      :     Optional[int]        =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
           with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"					,     "r"						) as f:
                _UpperCAmelCase      :     Dict        =   json.loads(f.read()						)

           _UpperCAmelCase      :     List[str]        =   {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

           _UpperCAmelCase      :     List[Any]        =   pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

           # encode them
           _UpperCAmelCase      :     List[str]        =   DetaImageProcessor(format="coco_panoptic"						)
           _UpperCAmelCase      :     int        =   image_processing(images=A					,     annotations=A					,     masks_path=A					,     return_tensors="pt"						)

           # verify pixel values
           _UpperCAmelCase      :     Tuple        =   torch.Size([1, 3, 8_0_0, 1_0_6_6]						)
           self.assertEqual(encoding["pixel_values"].shape					,     A						)

           _UpperCAmelCase      :     Optional[Any]        =   torch.tensor([0.2_796, 0.3_138, 0.3_481]						)
           self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]					,     A					,     atol=1e-4						)						)

           # verify area
           _UpperCAmelCase      :     Optional[int]        =   torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["area"]					,     A						)						)
           # verify boxes
           _UpperCAmelCase      :     Tuple        =   torch.Size([6, 4]						)
           self.assertEqual(encoding["labels"][0]["boxes"].shape					,     A						)
           _UpperCAmelCase      :     int        =   torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]					,     A					,     atol=1e-3						)						)
           # verify image_id
           _UpperCAmelCase      :     List[str]        =   torch.tensor([3_9_7_6_9]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]					,     A						)						)
           # verify is_crowd
           _UpperCAmelCase      :     int        =   torch.tensor([0, 0, 0, 0, 0, 0]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]					,     A						)						)
           # verify class_labels
           _UpperCAmelCase      :     str        =   torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]					,     A						)						)
           # verify masks
           _UpperCAmelCase      :     Union[str, Any]        =   8_2_2_8_7_3
           self.assertEqual(encoding["labels"][0]["masks"].sum().item()					,     A						)
           # verify orig_size
           _UpperCAmelCase      :     List[str]        =   torch.tensor([4_8_0, 6_4_0]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]					,     A						)						)
           # verify size
           _UpperCAmelCase      :     int        =   torch.tensor([8_0_0, 1_0_6_6]						)
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]					,     A						)						)





"""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
