





'''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 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =GPTSanJapaneseTokenizer
				__a            =False
				__a            ={'do_clean_text': False, 'add_prefix_space': False}




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								super().setUp()

								# fmt: off
								_a								= ["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								= {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
								_a								= {"unk_token": "<unk>"}

								_a								= os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES["vocab_file"]					)
								_a								= 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   UpperCamelCase__   (  self     :					Dict					,				**__a     :					str					):
								kwargs.update(self.special_tokens_map					)
								return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname					,				**__a					)




				def   UpperCamelCase__   (  self     :					str					,				__a     :					Any					):
								_a								= "こんにちは、世界。 \nこんばんは、㔺界。😀"
								_a								= "こんにちは、世界。 \nこんばんは、世界。😀"
								return input_text, output_text




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					List[str]					):
								_a	,					_a								= self.get_input_output_texts(__a					)
								_a								= tokenizer.encode(__a					,				add_special_tokens=__a					)
								_a								= tokenizer.decode(__a					,				clean_up_tokenization_spaces=__a					)
								return text, ids




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




				def   UpperCamelCase__   (  self     :					Tuple					):
								pass  # TODO add if relevant




				def   UpperCamelCase__   (  self     :					str					):
								pass  # TODO add if relevant




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= self.get_tokenizer()

								# Testing tokenization
								_a								= "こんにちは、世界。　こんばんは、㔺界。"
								_a								= ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
								_a								= tokenizer.tokenize(__a					)
								self.assertListEqual(__a					,				__a					)

								# Testing conversion to ids without special tokens
								_a								= [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
								_a								= tokenizer.convert_tokens_to_ids(__a					)
								self.assertListEqual(__a					,				__a					)

								# Testing conversion to ids with special tokens
								_a								= tokens + [tokenizer.unk_token]
								_a								= [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
								_a								= tokenizer.convert_tokens_to_ids(__a					)
								self.assertListEqual(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= self.get_tokenizer()

								# Testing tokenization
								_a								= "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
								_a								= "こんにちは、、、、世界。こんばんは、、、、世界。"
								_a								= tokenizer.encode(__a					)
								_a								= tokenizer.decode(__a					)
								self.assertEqual(__a					,				__a					)




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

								# Testing tokenization
								_a								= "こんにちは、世界。"
								_a								= "こんばんは、㔺界。😀"
								_a								= "こんにちは、世界。こんばんは、世界。😀"
								_a								= tokenizer.encode(prefix_text + input_text					)
								_a								= tokenizer.encode(""					,				prefix_text=prefix_text + input_text					)
								_a								= tokenizer.encode(__a					,				prefix_text=__a					)
								_a								= tokenizer.decode(__a					)
								_a								= tokenizer.decode(__a					)
								_a								= tokenizer.decode(__a					)
								self.assertEqual(__a					,				__a					)
								self.assertEqual(__a					,				__a					)
								self.assertEqual(__a					,				__a					)




				@slow
				def   UpperCamelCase__   (  self     :					Any					):
								_a								= self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"					)

								# Testing tokenization
								_a								= "こんにちは、世界。"
								_a								= "こんばんは、㔺界。😀"

								_a								= len(tokenizer.encode(__a					)					) - 2
								_a								= len(tokenizer.encode(__a					)					) - 2

								_a								= [1] + [0] * (len_prefix + len_text + 1)
								_a								= [1] * (len_prefix + len_text + 1) + [0]
								_a								= [1] + [1] * (len_prefix) + [0] * (len_text + 1)

								_a								= tokenizer(prefix_text + input_text					).token_type_ids
								_a								= tokenizer(""					,				prefix_text=prefix_text + input_text					).token_type_ids
								_a								= tokenizer(__a					,				prefix_text=__a					).token_type_ids
								self.assertListEqual(__a					,				__a					)
								self.assertListEqual(__a					,				__a					)
								self.assertListEqual(__a					,				__a					)




				@slow
				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"					)

								_a								= tokenizer.encode("あンいワ"					)
								_a								= tokenizer.encode(""					,				prefix_text="あンいワ"					)
								_a								= 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   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"					)

								_a								= [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
								_a								= tokenizer(__a					,				padding=__a					)
								_a								= tokenizer.batch_encode_plus(__a					,				padding=__a					)

								# fmt: off
								_a								= [[3_59_93, 86_40, 2_59_48, 3_59_98, 3_06_47, 3_56_75, 3_59_99, 3_59_99], [3_59_93, 1_03_82, 98_68, 3_59_98, 3_06_46, 94_59, 3_06_46, 3_56_75]]
								_a								= [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
								_a								= [[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   UpperCamelCase__   (  self     :					List[str]					):
								# Intentionally convert some words to accommodate character fluctuations unique to Japanese
								pass




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







'''simple docstring'''







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =['image_processor', 'tokenizer']
				__a            ='LayoutLMv3ImageProcessor'
				__a            =('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')




				def __init__(  self     :					Tuple					,				__a     :					int=None					,				__a     :					Union[str, Any]=None					,				**__a     :					Optional[Any]					):
								_a								= None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead."					,				__a					,				)
												_a								= kwargs.pop("feature_extractor"					)

								_a								= image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError("You need to specify an `image_processor`."					)
								if tokenizer is None:
												raise ValueError("You need to specify a `tokenizer`."					)

								super().__init__(__a					,				__a					)




				def __call__(  self     :					Any					,				__a     :					List[str]					,				__a     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,				__a     :					Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,				__a     :					Union[List[List[int]], List[List[List[int]]]] = None					,				__a     :					Optional[Union[List[int], List[List[int]]]] = None					,				__a     :					bool = True					,				__a     :					Union[bool, str, PaddingStrategy] = False					,				__a     :					Union[bool, str, TruncationStrategy] = None					,				__a     :					Optional[int] = None					,				__a     :					int = 0					,				__a     :					Optional[int] = None					,				__a     :					Optional[bool] = None					,				__a     :					Optional[bool] = None					,				__a     :					bool = False					,				__a     :					bool = False					,				__a     :					bool = False					,				__a     :					bool = False					,				__a     :					bool = True					,				__a     :					Optional[Union[str, TensorType]] = None					,				**__a     :					Dict					,				):
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
												raise ValueError(
												    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."					)

								if self.image_processor.apply_ocr and (word_labels is not None):
												raise ValueError(
												    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."					)

								# first, apply the image processor
								_a								= self.image_processor(images=__a					,				return_tensors=__a					)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
												if isinstance(__a					,				__a					):
																_a								= [text]  # add batch dimension (as the image processor always adds a batch dimension)
												_a								= features["words"]

								_a								= self.tokenizer(
								    text=text if text is not None else features["words"]					,				text_pair=text_pair if text_pair is not None else None					,				boxes=boxes if boxes is not None else features["boxes"]					,				word_labels=__a					,				add_special_tokens=__a					,				padding=__a					,				truncation=__a					,				max_length=__a					,				stride=__a					,				pad_to_multiple_of=__a					,				return_token_type_ids=__a					,				return_attention_mask=__a					,				return_overflowing_tokens=__a					,				return_special_tokens_mask=__a					,				return_offsets_mapping=__a					,				return_length=__a					,				verbose=__a					,				return_tensors=__a					,				**__a					,				)

								# add pixel values
								_a								= features.pop("pixel_values"					)
								if return_overflowing_tokens is True:
												_a								= self.get_overflowing_images(__a					,				encoded_inputs["overflow_to_sample_mapping"]					)
								_a								= images

								return encoded_inputs




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					List[Any]					):
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								_a								= []
								for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx]					)

								if len(__a					) != len(__a					):
												raise ValueError(
												    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
												    f' {len(__a					)} and {len(__a					)}'					)

								return images_with_overflow




				def   UpperCamelCase__   (  self     :					int					,				*__a     :					str					,				**__a     :					Tuple					):
								return self.tokenizer.batch_decode(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					str					,				*__a     :					List[Any]					,				**__a     :					List[str]					):
								return self.tokenizer.decode(*__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					Tuple					):
								return ["input_ids", "bbox", "attention_mask", "pixel_values"]




				@property
				def   UpperCamelCase__   (  self     :					int					):
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,				__a					,				)
								return self.image_processor_class




				@property
				def   UpperCamelCase__   (  self     :					List[str]					):
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,				__a					,				)
								return self.image_processor
