

"""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   (						a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			GPTSanJapaneseTokenizer
					lowercase__												=			False
					lowercase__												=			{"do_clean_text": False, "add_prefix_space": False}







					def 			__lowerCAmelCase					(			self     :							Any						):
						super().setUp()

						# fmt: off
						lowerCAmelCase__      :		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
						lowerCAmelCase__      :		Optional[Any]    							=      {'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
						lowerCAmelCase__      :		Optional[Any]    							=      {'''unk_token''': '''<unk>'''}

						lowerCAmelCase__      :		str    							=      os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES['''vocab_file''']						)
						lowerCAmelCase__      :		List[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(lowercase_						)						)







					def 			__lowerCAmelCase					(			self     :							Optional[Any]  ,**lowercase_     :							str						):
						kwargs.update(self.special_tokens_map						)
						return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname  ,**lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Tuple  ,lowercase_     :							Optional[int]						):
						lowerCAmelCase__      :		int    							=      '''こんにちは、世界。 \nこんばんは、㔺界。😀'''
						lowerCAmelCase__      :		Union[str, Any]    							=      '''こんにちは、世界。 \nこんばんは、世界。😀'''
						return input_text, output_text







					def 			__lowerCAmelCase					(			self     :							Optional[Any]  ,lowercase_     :							Union[str, Any]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      self.get_input_output_texts(lowercase_						)
						lowerCAmelCase__      :		int    							=      tokenizer.encode(lowercase_  ,add_special_tokens=lowercase_						)
						lowerCAmelCase__      :		int    							=      tokenizer.decode(lowercase_  ,clean_up_tokenization_spaces=lowercase_						)
						return text, ids







					def 			__lowerCAmelCase					(			self     :							Dict						):
						pass  # TODO add if relevant







					def 			__lowerCAmelCase					(			self     :							int						):
						pass  # TODO add if relevant







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						pass  # TODO add if relevant







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Dict    							=      self.get_tokenizer()

						# Testing tokenization
						lowerCAmelCase__      :		List[str]    							=      '''こんにちは、世界。　こんばんは、㔺界。'''
						lowerCAmelCase__      :		List[Any]    							=      ['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
						lowerCAmelCase__      :		Dict    							=      tokenizer.tokenize(lowercase_						)
						self.assertListEqual(lowercase_  ,lowercase_						)

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

						# Testing conversion to ids with special tokens
						lowerCAmelCase__      :		Optional[Any]    							=      tokens + [tokenizer.unk_token]
						lowerCAmelCase__      :		Union[str, Any]    							=      [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
						lowerCAmelCase__      :		Any    							=      tokenizer.convert_tokens_to_ids(lowercase_						)
						self.assertListEqual(lowercase_  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		Union[str, Any]    							=      self.get_tokenizer()

						# Testing tokenization
						lowerCAmelCase__      :		Tuple    							=      '''こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'''
						lowerCAmelCase__      :		Optional[Any]    							=      '''こんにちは、、、、世界。こんばんは、、、、世界。'''
						lowerCAmelCase__      :		str    							=      tokenizer.encode(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      tokenizer.decode(lowercase_						)
						self.assertEqual(lowercase_  ,lowercase_						)







					@slow
					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		Optional[Any]    							=      self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''						)

						# Testing tokenization
						lowerCAmelCase__      :		Any    							=      '''こんにちは、世界。'''
						lowerCAmelCase__      :		Any    							=      '''こんばんは、㔺界。😀'''
						lowerCAmelCase__      :		List[str]    							=      '''こんにちは、世界。こんばんは、世界。😀'''
						lowerCAmelCase__      :		Any    							=      tokenizer.encode(prefix_text + input_text						)
						lowerCAmelCase__      :		Union[str, Any]    							=      tokenizer.encode(''''''  ,prefix_text=prefix_text + input_text						)
						lowerCAmelCase__      :		str    							=      tokenizer.encode(lowercase_  ,prefix_text=lowercase_						)
						lowerCAmelCase__      :		Any    							=      tokenizer.decode(lowercase_						)
						lowerCAmelCase__      :		int    							=      tokenizer.decode(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      tokenizer.decode(lowercase_						)
						self.assertEqual(lowercase_  ,lowercase_						)
						self.assertEqual(lowercase_  ,lowercase_						)
						self.assertEqual(lowercase_  ,lowercase_						)







					@slow
					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		Any    							=      self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''						)

						# Testing tokenization
						lowerCAmelCase__      :		Any    							=      '''こんにちは、世界。'''
						lowerCAmelCase__      :		List[str]    							=      '''こんばんは、㔺界。😀'''

						lowerCAmelCase__      :		List[str]    							=      len(tokenizer.encode(lowercase_						)						) - 2
						lowerCAmelCase__      :		Tuple    							=      len(tokenizer.encode(lowercase_						)						) - 2

						lowerCAmelCase__      :		Union[str, Any]    							=      [1] + [0] * (len_prefix + len_text + 1)
						lowerCAmelCase__      :		Optional[int]    							=      [1] * (len_prefix + len_text + 1) + [0]
						lowerCAmelCase__      :		Union[str, Any]    							=      [1] + [1] * (len_prefix) + [0] * (len_text + 1)

						lowerCAmelCase__      :		List[Any]    							=      tokenizer(prefix_text + input_text						).token_type_ids
						lowerCAmelCase__      :		List[str]    							=      tokenizer(''''''  ,prefix_text=prefix_text + input_text						).token_type_ids
						lowerCAmelCase__      :		Optional[int]    							=      tokenizer(lowercase_  ,prefix_text=lowercase_						).token_type_ids
						self.assertListEqual(lowercase_  ,lowercase_						)
						self.assertListEqual(lowercase_  ,lowercase_						)
						self.assertListEqual(lowercase_  ,lowercase_						)







					@slow
					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		str    							=      self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''						)

						lowerCAmelCase__      :		Tuple    							=      tokenizer.encode('''あンいワ'''						)
						lowerCAmelCase__      :		int    							=      tokenizer.encode(''''''  ,prefix_text='''あンいワ'''						)
						lowerCAmelCase__      :		List[Any]    							=      tokenizer.encode('''いワ'''  ,prefix_text='''あン'''						)

						self.assertEqual(tokenizer.decode(lowercase_						)  ,tokenizer.decode(lowercase_						)						)
						self.assertEqual(tokenizer.decode(lowercase_						)  ,tokenizer.decode(lowercase_						)						)
						self.assertNotEqual(lowercase_  ,lowercase_						)
						self.assertNotEqual(lowercase_  ,lowercase_						)
						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 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		List[Any]    							=      self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese'''						)

						lowerCAmelCase__      :		Tuple    							=      [['''武田信玄''', '''は、'''], ['''織田信長''', '''の配下の、''']]
						lowerCAmelCase__      :		List[str]    							=      tokenizer(lowercase_  ,padding=lowercase_						)
						lowerCAmelCase__      :		str    							=      tokenizer.batch_encode_plus(lowercase_  ,padding=lowercase_						)

						# fmt: off
						lowerCAmelCase__      :		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]]
						lowerCAmelCase__      :		Tuple    							=      [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
						lowerCAmelCase__      :		str    							=      [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
						# fmt: on
						self.assertListEqual(x_token.input_ids  ,lowercase_						)
						self.assertListEqual(x_token.token_type_ids  ,lowercase_						)
						self.assertListEqual(x_token.attention_mask  ,lowercase_						)
						self.assertListEqual(x_token_a.input_ids  ,lowercase_						)
						self.assertListEqual(x_token_a.token_type_ids  ,lowercase_						)
						self.assertListEqual(x_token_a.attention_mask  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							str						):
						# Intentionally convert some words to accommodate character fluctuations unique to Japanese
						pass







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







"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 LevitImageProcessor







class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,lowercase_     :							Dict  ,lowercase_     :							List[str]=7  ,lowercase_     :							Tuple=3  ,lowercase_     :							List[str]=1_8  ,lowercase_     :							Optional[Any]=3_0  ,lowercase_     :							List[Any]=4_0_0  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Any=None  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=None  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,):
						lowerCAmelCase__      :		Any    							=      size if size is not None else {'''shortest_edge''': 1_8}
						lowerCAmelCase__      :		Union[str, Any]    							=      crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
						lowerCAmelCase__      :		Dict    							=      parent
						lowerCAmelCase__      :		Dict    							=      batch_size
						lowerCAmelCase__      :		List[str]    							=      num_channels
						lowerCAmelCase__      :		Any    							=      image_size
						lowerCAmelCase__      :		Union[str, Any]    							=      min_resolution
						lowerCAmelCase__      :		Dict    							=      max_resolution
						lowerCAmelCase__      :		List[str]    							=      do_resize
						lowerCAmelCase__      :		Optional[Any]    							=      size
						lowerCAmelCase__      :		Tuple    							=      do_center_crop
						lowerCAmelCase__      :		Optional[int]    							=      crop_size
						lowerCAmelCase__      :		List[str]    							=      do_normalize
						lowerCAmelCase__      :		Tuple    							=      image_mean
						lowerCAmelCase__      :		int    							=      image_std







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "do_center_crop": self.do_center_crop,
						    "size": self.size,
						    "crop_size": self.crop_size,
						}







@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE   (						a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			LevitImageProcessor if is_vision_available() else None







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[Any]    							=      LevitImageProcessingTester(self						)







					@property
					def 			__lowerCAmelCase					(			self     :							Any						):
						return self.image_processor_tester.prepare_image_processor_dict()







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		Optional[int]    							=      self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(lowercase_  ,'''image_mean'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''image_std'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_normalize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_resize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_center_crop'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''size'''						)						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		int    							=      self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size  ,{'''shortest_edge''': 1_8}						)
						self.assertEqual(image_processor.crop_size  ,{'''height''': 1_8, '''width''': 1_8}						)

						lowerCAmelCase__      :		List[str]    							=      self.image_processing_class.from_dict(self.image_processor_dict  ,size=4_2  ,crop_size=8_4						)
						self.assertEqual(image_processor.size  ,{'''shortest_edge''': 4_2}						)
						self.assertEqual(image_processor.crop_size  ,{'''height''': 8_4, '''width''': 8_4}						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						pass







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						# Initialize image_processing
						lowerCAmelCase__      :		List[str]    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						lowerCAmelCase__      :		Union[str, Any]    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,Image.Image						)

						# Test not batched input
						lowerCAmelCase__      :		Any    							=      image_processing(image_inputs[0]  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)

						# Test batched
						lowerCAmelCase__      :		Optional[int]    							=      image_processing(lowercase_  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)







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

						# Test not batched input
						lowerCAmelCase__      :		Tuple    							=      image_processing(image_inputs[0]  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)

						# Test batched
						lowerCAmelCase__      :		Union[str, Any]    							=      image_processing(lowercase_  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)







					def 			__lowerCAmelCase					(			self     :							int						):
						# Initialize image_processing
						lowerCAmelCase__      :		Tuple    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						lowerCAmelCase__      :		Dict    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_  ,torchify=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,torch.Tensor						)

						# Test not batched input
						lowerCAmelCase__      :		Dict    							=      image_processing(image_inputs[0]  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)

						# Test batched
						lowerCAmelCase__      :		Optional[int]    							=      image_processing(lowercase_  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)




