





import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class  __A   (   lowerCAmelCase      ,    unittest.TestCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						GPTNeoXJapaneseTokenizer
			lowerCAmelCase_												=						False
			lowerCAmelCase_												=						{"""do_clean_text""": False, """add_prefix_space""": False}





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				super().setUp()

				lowerCamelCase__        =		[
				    '''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>''',
				    '''<|startoftext|>''',
				    '''<|endoftext|>''',
				]
				lowerCamelCase__        =		{'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
				lowerCamelCase__        =		{'''unk_token''': '''<unk>'''}

				lowerCamelCase__        =		os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES['''vocab_file''']							)
				lowerCamelCase__        =		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(__lowerCAmelCase							)							)





			def 		__lowerCamelCase    (   self	,  **__lowerCAmelCase							):



				'''simple docstring'''







				kwargs.update(self.special_tokens_map							)
				return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname	,  **__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		'''こんにちは、世界。 \nこんばんは、㔺界。😀'''
				lowerCamelCase__        =		'''こんにちは、世界。 \nこんばんは、世界。😀'''
				return input_text, output_text





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__    ,     lowerCamelCase__        =		self.get_input_output_texts(__lowerCAmelCase							)
				lowerCamelCase__        =		tokenizer.encode(__lowerCAmelCase	,  add_special_tokens=__lowerCAmelCase							)
				lowerCamelCase__        =		tokenizer.decode(__lowerCAmelCase	,  clean_up_tokenization_spaces=__lowerCAmelCase							)
				return text, ids





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				pass  # TODO add if relevant





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				pass  # TODO add if relevant





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				pass  # TODO add if relevant





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.get_tokenizer()

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

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

				# Testing conversion to ids with special tokens
				lowerCamelCase__        =		tokens + [tokenizer.unk_token]
				lowerCamelCase__        =		[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
				lowerCamelCase__        =		tokenizer.convert_tokens_to_ids(__lowerCAmelCase							)
				self.assertListEqual(__lowerCAmelCase	,  __lowerCAmelCase							)





			@slow
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.tokenizer_class.from_pretrained('''abeja/gpt-neox-japanese-2.7b'''							)

				lowerCamelCase__        =		tokenizer.encode('''ありがとう。'''	,  add_special_tokens=__lowerCAmelCase							)
				lowerCamelCase__        =		tokenizer.encode('''どういたしまして。'''	,  add_special_tokens=__lowerCAmelCase							)

				lowerCamelCase__        =		tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase							)
				lowerCamelCase__        =		tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase	,  __lowerCAmelCase							)

				assert encoded_sentence == ids_a
				assert encoded_pair == ids_a + ids_a





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				pass










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 ViltImageProcessor






class  __A   (   unittest.TestCase			):

			'''simple docstring'''







			def __init__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase=7	,  __lowerCAmelCase=3	,  __lowerCAmelCase=1_8	,  __lowerCAmelCase=3_0	,  __lowerCAmelCase=4_0_0	,  __lowerCAmelCase=True	,  __lowerCAmelCase=None	,  __lowerCAmelCase=2	,  __lowerCAmelCase=True	,  __lowerCAmelCase=[0.5, 0.5, 0.5]	,  __lowerCAmelCase=[0.5, 0.5, 0.5]	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		size if size is not None else {'''shortest_edge''': 3_0}
				lowerCamelCase__        =		parent
				lowerCamelCase__        =		batch_size
				lowerCamelCase__        =		num_channels
				lowerCamelCase__        =		image_size
				lowerCamelCase__        =		min_resolution
				lowerCamelCase__        =		max_resolution
				lowerCamelCase__        =		do_resize
				lowerCamelCase__        =		size
				lowerCamelCase__        =		size_divisor
				lowerCamelCase__        =		do_normalize
				lowerCamelCase__        =		image_mean
				lowerCamelCase__        =		image_std





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "size_divisor": self.size_divisor,
				}





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase=False							):



				'''simple docstring'''







				if not batched:
					lowerCamelCase__        =		self.size['''shortest_edge''']
					lowerCamelCase__        =		image_inputs[0]
					if isinstance(__lowerCAmelCase	,  Image.Image							):
						lowerCamelCase__    ,     lowerCamelCase__        =		image.size
					else:
						lowerCamelCase__    ,     lowerCamelCase__        =		image.shape[1], image.shape[2]
					lowerCamelCase__        =		size / min(__lowerCAmelCase	,  __lowerCAmelCase							)
					if h < w:
						lowerCamelCase__    ,     lowerCamelCase__        =		size, scale * w
					else:
						lowerCamelCase__    ,     lowerCamelCase__        =		scale * h, size

					lowerCamelCase__        =		int((1_3_3_3 / 8_0_0) * size							)
					if max(__lowerCAmelCase	,  __lowerCAmelCase							) > max_size:
						lowerCamelCase__        =		max_size / max(__lowerCAmelCase	,  __lowerCAmelCase							)
						lowerCamelCase__        =		newh * scale
						lowerCamelCase__        =		neww * scale

					lowerCamelCase__    ,     lowerCamelCase__        =		int(newh + 0.5							), int(neww + 0.5							)
					lowerCamelCase__    ,     lowerCamelCase__        =		(
					    newh // self.size_divisor * self.size_divisor,
					    neww // self.size_divisor * self.size_divisor,
					)

				else:
					lowerCamelCase__        =		[]
					for image in image_inputs:
						lowerCamelCase__    ,     lowerCamelCase__        =		self.get_expected_values([image]							)
						expected_values.append((expected_height, expected_width)							)
					lowerCamelCase__        =		max(__lowerCAmelCase	,  key=lambda __lowerCAmelCase							: item[0]							)[0]
					lowerCamelCase__        =		max(__lowerCAmelCase	,  key=lambda __lowerCAmelCase							: item[1]							)[1]

				return expected_height, expected_width






@require_torch
@require_vision
class  __A   (   lowerCAmelCase      ,    unittest.TestCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						ViltImageProcessor if is_vision_available() else None





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		ViltImageProcessingTester(self							)





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''image_mean'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''image_std'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''do_normalize'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''do_resize'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''size'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase	,  '''size_divisor'''							)							)





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.image_processing_class.from_dict(self.image_processor_dict							)
				self.assertEqual(image_processor.size	,  {'''shortest_edge''': 3_0}							)

				lowerCamelCase__        =		self.image_processing_class.from_dict(self.image_processor_dict	,  size=4_2							)
				self.assertEqual(image_processor.size	,  {'''shortest_edge''': 4_2}							)





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.image_processing_class(**self.image_processor_dict							)
				# create random PIL images
				lowerCamelCase__        =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__lowerCAmelCase							)
				for image in image_inputs:
					self.assertIsInstance(__lowerCAmelCase	,  Image.Image							)

				# Test not batched input
				lowerCamelCase__        =		image_processing(image_inputs[0]	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

				# Test batched
				lowerCamelCase__        =		image_processing(__lowerCAmelCase	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase	,  batched=__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,  )





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.image_processing_class(**self.image_processor_dict							)
				# create random numpy tensors
				lowerCamelCase__        =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__lowerCAmelCase	,  numpify=__lowerCAmelCase							)
				for image in image_inputs:
					self.assertIsInstance(__lowerCAmelCase	,  np.ndarray							)

				# Test not batched input
				lowerCamelCase__        =		image_processing(image_inputs[0]	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

				# Test batched
				lowerCamelCase__        =		image_processing(__lowerCAmelCase	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase	,  batched=__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,  )





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.image_processing_class(**self.image_processor_dict							)
				# create random PyTorch tensors
				lowerCamelCase__        =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__lowerCAmelCase	,  torchify=__lowerCAmelCase							)
				for image in image_inputs:
					self.assertIsInstance(__lowerCAmelCase	,  torch.Tensor							)

				# Test not batched input
				lowerCamelCase__        =		image_processing(image_inputs[0]	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

				# Test batched
				lowerCamelCase__        =		image_processing(__lowerCAmelCase	,  return_tensors='''pt'''							).pixel_values

				lowerCamelCase__    ,     lowerCamelCase__        =		self.image_processor_tester.get_expected_values(__lowerCAmelCase	,  batched=__lowerCAmelCase							)
				self.assertEqual(
				    encoded_images.shape	,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,  )



