



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__       (       a__  ,			unittest.TestCase       ):


					"""simple docstring"""



					A__  :				Optional[Any]					 =				GPTNeoXJapaneseTokenizer
					A__  :				Dict					 =				False
					A__  :				List[str]					 =				{'''do_clean_text''': False, '''add_prefix_space''': False}

					def 				__UpperCAmelCase		(		self     :Tuple    ):
										super().setUp()

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

										lowercase						   =       os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['vocab_file']    )
										lowercase						   =       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 				__UpperCAmelCase		(		self     :Optional[Any]      ,  **lowercase__     :int    ):
										kwargs.update(self.special_tokens_map    )
										return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname      ,  **_lowerCamelCase    )

					def 				__UpperCAmelCase		(		self     :Tuple      ,  lowercase__     :Any    ):
										lowercase						   =       '''こんにちは、世界。 \nこんばんは、㔺界。😀'''
										lowercase						   =       '''こんにちは、世界。 \nこんばんは、世界。😀'''
										return input_text, output_text

					def 				__UpperCAmelCase		(		self     :Optional[int]      ,  lowercase__     :int    ):
										lowercase						   =       self.get_input_output_texts(_lowerCamelCase    )
										lowercase						   =       tokenizer.encode(_lowerCamelCase      ,  add_special_tokens=_lowerCamelCase    )
										lowercase						   =       tokenizer.decode(_lowerCamelCase      ,  clean_up_tokenization_spaces=_lowerCamelCase    )
										return text, ids

					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										pass  # TODO add if relevant

					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										pass  # TODO add if relevant

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

					def 				__UpperCAmelCase		(		self     :Any    ):
										lowercase						   =       self.get_tokenizer()

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

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

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

					@slow
					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase						   =       self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b'    )

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

										lowercase						   =       tokenizer.build_inputs_with_special_tokens(_lowerCamelCase    )
										lowercase						   =       tokenizer.build_inputs_with_special_tokens(_lowerCamelCase      ,  _lowerCamelCase    )

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

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



					def 				__UpperCAmelCase		(		self     :List[str]    ):
										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


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import SwinaSRImageProcessor
							from transformers.image_transforms import get_image_size






class      UpperCamelCase_  (unittest.TestCase  ):







						"""simple docstring"""


						def __init__(  self   :							Tuple				,			_lowerCamelCase   :							str				,			_lowerCamelCase   :							str=7				,			_lowerCamelCase   :							Tuple=3				,			_lowerCamelCase   :							Any=18				,			_lowerCamelCase   :							Optional[int]=30				,			_lowerCamelCase   :							List[Any]=400				,			_lowerCamelCase   :							int=True				,			_lowerCamelCase   :							Dict=1 / 255				,			_lowerCamelCase   :							List[str]=True				,			_lowerCamelCase   :							Optional[int]=8				,			):





											"""simple docstring"""







											A_			:  Tuple			=				parent
											A_			:  str			=				batch_size
											A_			:  Optional[Any]			=				num_channels
											A_			:  Union[str, Any]			=				image_size
											A_			:  Optional[int]			=				min_resolution
											A_			:  List[Any]			=				max_resolution
											A_			:  int			=				do_rescale
											A_			:  Optional[Any]			=				rescale_factor
											A_			:  Any			=				do_pad
											A_			:  Tuple			=				pad_size







						def    _a     (  self   :							List[Any]     ):





											"""simple docstring"""







											return {
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_pad": self.do_pad,
											    "pad_size": self.pad_size,
											}







						def    _a     (  self   :							Tuple				,			_lowerCamelCase   :							List[str]=False				,			_lowerCamelCase   :							Tuple=False				,			_lowerCamelCase   :							str=False     ):





											"""simple docstring"""







											assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

											if equal_resolution:
																A_			:  List[Any]			=				[]
																for i in range(self.batch_size     ):
																					image_inputs.append(
																					    np.random.randint(
																					        255				,			size=(self.num_channels, self.max_resolution, self.max_resolution)				,			dtype=np.uinta     )     )
											else:
																A_			:  List[Any]			=				[]
																for i in range(self.batch_size     ):
																					A_			,A_			:  List[str]			=				np.random.choice(np.arange(self.min_resolution				,			self.max_resolution     )				,			2     )
																					image_inputs.append(np.random.randint(255				,			size=(self.num_channels, width, height)				,			dtype=np.uinta     )     )

											if not numpify and not torchify:
																# PIL expects the channel dimension as last dimension
																A_			:  Any			=				[Image.fromarray(np.moveaxis(_lowerCamelCase				,			0				,			-1     )     ) for x in image_inputs]

											if torchify:
																A_			:  str			=				[torch.from_numpy(_lowerCamelCase     ) for x in image_inputs]

											return image_inputs






@require_torch
@require_vision
class      UpperCamelCase_  (a__,   unittest.TestCase  ):







						"""simple docstring"""


						_lowerCAmelCase         =       SwinaSRImageProcessor if is_vision_available() else None







						def    _a     (  self   :							List[str]     ):





											"""simple docstring"""







											A_			:  int			=				SwinaSRImageProcessingTester(self     )







						@property
						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											return self.image_processor_tester.prepare_image_processor_dict()







						def    _a     (  self   :							Any     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''do_rescale'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''rescale_factor'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''do_pad'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''pad_size'''     )     )







						def    _a     (  self   :							Tuple     ):





											"""simple docstring"""







											pass







						def    _a     (  self   :							Optional[int]				,			_lowerCamelCase   :							Optional[int]     ):





											"""simple docstring"""







											A_			,A_			:  Any			=				get_image_size(_lowerCamelCase     )
											A_			:  List[Any]			=				self.image_processor_tester.pad_size

											A_			:  Dict			=				(old_height // size + 1) * size - old_height
											A_			:  Optional[Any]			=				(old_width // size + 1) * size - old_width
											return old_height + pad_height, old_width + pad_width







						def    _a     (  self   :							int     ):





											"""simple docstring"""







											A_			:  Dict			=				self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											A_			:  Optional[int]			=				self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase     )
											for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase				,			Image.Image     )

											# Test not batched input
											A_			:  Any			=				image_processor(image_inputs[0]				,			return_tensors='''pt'''     ).pixel_values
											A_			,A_			:  List[Any]			=				self.calculate_expected_size(np.array(image_inputs[0]     )     )
											self.assertEqual(
											    encoded_images.shape				,			(
											        1,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )				,			)







						def    _a     (  self   :							Optional[Any]     ):





											"""simple docstring"""







											A_			:  Optional[Any]			=				self.image_processing_class(**self.image_processor_dict     )
											# create random numpy tensors
											A_			:  Dict			=				self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase				,			numpify=_lowerCamelCase     )
											for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase				,			np.ndarray     )

											# Test not batched input
											A_			:  Union[str, Any]			=				image_processor(image_inputs[0]				,			return_tensors='''pt'''     ).pixel_values
											A_			,A_			:  Union[str, Any]			=				self.calculate_expected_size(image_inputs[0]     )
											self.assertEqual(
											    encoded_images.shape				,			(
											        1,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )				,			)







						def    _a     (  self   :							int     ):





											"""simple docstring"""







											A_			:  Union[str, Any]			=				self.image_processing_class(**self.image_processor_dict     )
											# create random PyTorch tensors
											A_			:  List[Any]			=				self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase				,			torchify=_lowerCamelCase     )
											for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase				,			torch.Tensor     )

											# Test not batched input
											A_			:  int			=				image_processor(image_inputs[0]				,			return_tensors='''pt'''     ).pixel_values
											A_			,A_			:  str			=				self.calculate_expected_size(image_inputs[0]     )
											self.assertEqual(
											    encoded_images.shape				,			(
											        1,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )				,			)



