
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 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		GPTSanJapaneseTokenizer
       UpperCamelCase_   =		False
       UpperCamelCase_   =		{'''do_clean_text''': False, '''add_prefix_space''': False}
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         super().setUp()

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

         _lowercase:			Optional[int]       =     os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['''vocab_file''']       )
         _lowercase:			Optional[int]       =     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 							lowercase_							(     self ,				**A_       )     ->	int:





         """simple docstring"""




         kwargs.update(self.special_tokens_map       )
         return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname ,				**A_       )
       def 							lowercase_							(     self ,				A_       )     ->	int:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     '''こんにちは、世界。 \nこんばんは、㔺界。😀'''
         _lowercase:			Any       =     '''こんにちは、世界。 \nこんばんは、世界。😀'''
         return input_text, output_text
       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         _lowercase:			Tuple       =     self.get_input_output_texts(A_       )
         _lowercase:			List[Any]       =     tokenizer.encode(A_ ,				add_special_tokens=A_       )
         _lowercase:			Dict       =     tokenizer.decode(A_ ,				clean_up_tokenization_spaces=A_       )
         return text, ids
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         pass  # TODO add if relevant
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         pass  # TODO add if relevant
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         pass  # TODO add if relevant
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.get_tokenizer()

         # Testing tokenization
         _lowercase:			Tuple       =     '''こんにちは、世界。　こんばんは、㔺界。'''
         _lowercase:			int       =     ['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
         _lowercase:			Union[str, Any]       =     tokenizer.tokenize(A_       )
         self.assertListEqual(A_ ,				A_       )

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

         # Testing conversion to ids with special tokens
         _lowercase:			int       =     tokens + [tokenizer.unk_token]
         _lowercase:			str       =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
         _lowercase:			Any       =     tokenizer.convert_tokens_to_ids(A_       )
         self.assertListEqual(A_ ,				A_       )
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.get_tokenizer()

         # Testing tokenization
         _lowercase:			Tuple       =     '''こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'''
         _lowercase:			List[str]       =     '''こんにちは、、、、世界。こんばんは、、、、世界。'''
         _lowercase:			List[str]       =     tokenizer.encode(A_       )
         _lowercase:			Dict       =     tokenizer.decode(A_       )
         self.assertEqual(A_ ,				A_       )
       @slow
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''       )

         # Testing tokenization
         _lowercase:			Union[str, Any]       =     '''こんにちは、世界。'''
         _lowercase:			Dict       =     '''こんばんは、㔺界。😀'''
         _lowercase:			Union[str, Any]       =     '''こんにちは、世界。こんばんは、世界。😀'''
         _lowercase:			Optional[int]       =     tokenizer.encode(prefix_text + input_text       )
         _lowercase:			List[str]       =     tokenizer.encode('''''' ,				prefix_text=prefix_text + input_text       )
         _lowercase:			Tuple       =     tokenizer.encode(A_ ,				prefix_text=A_       )
         _lowercase:			Any       =     tokenizer.decode(A_       )
         _lowercase:			Optional[int]       =     tokenizer.decode(A_       )
         _lowercase:			List[str]       =     tokenizer.decode(A_       )
         self.assertEqual(A_ ,				A_       )
         self.assertEqual(A_ ,				A_       )
         self.assertEqual(A_ ,				A_       )
       @slow
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			str       =     self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''       )

         # Testing tokenization
         _lowercase:			str       =     '''こんにちは、世界。'''
         _lowercase:			str       =     '''こんばんは、㔺界。😀'''

         _lowercase:			List[str]       =     len(tokenizer.encode(A_       )       ) - 2
         _lowercase:			List[Any]       =     len(tokenizer.encode(A_       )       ) - 2

         _lowercase:			Any       =     [1] + [0] * (len_prefix + len_text + 1)
         _lowercase:			Dict       =     [1] * (len_prefix + len_text + 1) + [0]
         _lowercase:			Union[str, Any]       =     [1] + [1] * (len_prefix) + [0] * (len_text + 1)

         _lowercase:			Optional[int]       =     tokenizer(prefix_text + input_text       ).token_type_ids
         _lowercase:			Union[str, Any]       =     tokenizer('''''' ,				prefix_text=prefix_text + input_text       ).token_type_ids
         _lowercase:			Dict       =     tokenizer(A_ ,				prefix_text=A_       ).token_type_ids
         self.assertListEqual(A_ ,				A_       )
         self.assertListEqual(A_ ,				A_       )
         self.assertListEqual(A_ ,				A_       )
       @slow
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''       )

         _lowercase:			Optional[Any]       =     tokenizer.encode('''あンいワ'''       )
         _lowercase:			int       =     tokenizer.encode('''''' ,				prefix_text='''あンいワ'''       )
         _lowercase:			Tuple       =     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 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''       )

         _lowercase:			Dict       =     [['''武田信玄''', '''は、'''], ['''織田信長''', '''の配下の、''']]
         _lowercase:			Tuple       =     tokenizer(A_ ,				padding=A_       )
         _lowercase:			Optional[int]       =     tokenizer.batch_encode_plus(A_ ,				padding=A_       )

         # fmt: off
         _lowercase:			Union[str, Any]       =     [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
         _lowercase:			Optional[int]       =     [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
         _lowercase:			Optional[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 ,				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 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         pass



       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         pass


"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A__   :					Dict							=   logging.get_logger(__name__)


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		['''pixel_values''']
       def __init__(     self ,				A_ = True ,				A_ = None ,				A_ = PILImageResampling.BICUBIC ,				A_ = True ,				A_ = True ,				A_ = 1 / 255 ,				A_ = None ,				A_ = True ,				A_ = None ,				A_ = None ,				**A_ ,				)     ->	None:





         """simple docstring"""




         super().__init__(**A_       )
         _lowercase:			List[str]       =     size if size is not None else {'''height''': 224, '''width''': 224}
         _lowercase:			Dict       =     get_size_dict(A_       )
         _lowercase:			Any       =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         _lowercase:			List[str]       =     get_size_dict(A_ ,				default_to_square=A_ ,				param_name='''crop_size'''       )

         _lowercase:			List[Any]       =     do_resize
         _lowercase:			Tuple       =     do_rescale
         _lowercase:			Union[str, Any]       =     do_normalize
         _lowercase:			Optional[int]       =     do_center_crop
         _lowercase:			List[str]       =     crop_size
         _lowercase:			int       =     size
         _lowercase:			List[Any]       =     resample
         _lowercase:			Optional[Any]       =     rescale_factor
         _lowercase:			str       =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         _lowercase:			Dict       =     image_std if image_std is not None else IMAGENET_DEFAULT_STD
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = PILImageResampling.BILINEAR ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     get_size_dict(A_       )

         if "shortest_edge" in size:
           _lowercase:			Optional[int]       =     get_resize_output_image_size(A_ ,				size=size['''shortest_edge'''] ,				default_to_square=A_       )
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
           _lowercase:			Dict       =     (size['''height'''], size['''width'''])
         else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''       )
         return resize(A_ ,				size=A_ ,				resample=A_ ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




         _lowercase:			Dict       =     get_size_dict(A_       )
         if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''       )
         return center_crop(A_ ,				size=(size['''height'''], size['''width''']) ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = None ,				**A_       )     ->	np.ndarray:





         """simple docstring"""




         return rescale(A_ ,				scale=A_ ,				data_format=A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ = None ,				**A_ ,				)     ->	np.ndarray:





         """simple docstring"""




         return normalize(A_ ,				mean=A_ ,				std=A_ ,				data_format=A_ ,				**A_       )



       def 							lowercase_							(     self ,				A_ ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = ChannelDimension.FIRST ,				**A_ ,				)     ->	BatchFeature:





         """simple docstring"""




         _lowercase:			List[Any]       =     do_resize if do_resize is not None else self.do_resize
         _lowercase:			Tuple       =     do_rescale if do_rescale is not None else self.do_rescale
         _lowercase:			Optional[Any]       =     do_normalize if do_normalize is not None else self.do_normalize
         _lowercase:			Optional[int]       =     do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowercase:			Optional[int]       =     crop_size if crop_size is not None else self.crop_size
         _lowercase:			List[Any]       =     get_size_dict(A_ ,				param_name='''crop_size''' ,				default_to_square=A_       )
         _lowercase:			List[str]       =     resample if resample is not None else self.resample
         _lowercase:			List[str]       =     rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowercase:			List[Any]       =     image_mean if image_mean is not None else self.image_mean
         _lowercase:			List[str]       =     image_std if image_std is not None else self.image_std

         _lowercase:			List[Any]       =     size if size is not None else self.size
         _lowercase:			List[str]       =     get_size_dict(A_       )

         if not is_batched(A_       ):
           _lowercase:			Optional[Any]       =     [images]

         if not valid_images(A_       ):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''       )

         if do_resize and size is None:
           raise ValueError('''Size must be specified if do_resize is True.'''       )

         if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''       )

         if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

         # All transformations expect numpy arrays.
         _lowercase:			Dict       =     [to_numpy_array(A_       ) for image in images]

         if do_resize:
           _lowercase:			int       =     [self.resize(image=A_ ,				size=A_ ,				resample=A_       ) for image in images]

         if do_center_crop:
           _lowercase:			str       =     [self.center_crop(image=A_ ,				size=A_       ) for image in images]

         if do_rescale:
           _lowercase:			Optional[Any]       =     [self.rescale(image=A_ ,				scale=A_       ) for image in images]

         if do_normalize:
           _lowercase:			List[Any]       =     [self.normalize(image=A_ ,				mean=A_ ,				std=A_       ) for image in images]

         _lowercase:			Optional[Any]       =     [to_channel_dimension_format(A_ ,				A_       ) for image in images]

         _lowercase:			Any       =     {'''pixel_values''': images}
         return BatchFeature(data=A_ ,				tensor_type=A_       )
