code: 
  '''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, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
  							import PIL
  
  
  __lowerCAmelCase				      =					logging.get_logger(__name__)
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  	UpperCamelCase_     :				List[str]								=  ['pixel_values']
  
  
  
  
  
  
  
  	def __init__(    self			:List[str]      ,    lowercase			:bool = True      ,    lowercase			:Dict[str, int] = None      ,    lowercase			:PILImageResampling = PIL.Image.NEAREST      ,    lowercase			:bool = False      ,    lowercase			:Dict[str, int] = None      ,    lowercase			:Union[int, float] = 1 / 2_5_5      ,    lowercase			:bool = False      ,    lowercase			:bool = True      ,    lowercase			:bool = True      ,    lowercase			:Optional[Union[float, List[float]]] = None      ,    lowercase			:Optional[Union[float, List[float]]] = None      ,    lowercase			:bool = True      ,    **lowercase			:Optional[Any]      ,    )		->			None:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__(**lowercase )
  				SCREAMING_SNAKE_CASE    	=   size if size is not None else {'''height''': 3_4_6, '''width''': 3_4_6}
  				SCREAMING_SNAKE_CASE    	=   get_size_dict(lowercase )
  				SCREAMING_SNAKE_CASE    	=   crop_size if crop_size is not None else {'''height''': 2_8_9, '''width''': 2_8_9}
  				SCREAMING_SNAKE_CASE    	=   get_size_dict(lowercase      ,    param_name='''crop_size''' )
  
  				SCREAMING_SNAKE_CASE    	=   do_resize
  				SCREAMING_SNAKE_CASE    	=   size
  				SCREAMING_SNAKE_CASE    	=   resample
  				SCREAMING_SNAKE_CASE    	=   do_center_crop
  				SCREAMING_SNAKE_CASE    	=   crop_size
  				SCREAMING_SNAKE_CASE    	=   do_rescale
  				SCREAMING_SNAKE_CASE    	=   rescale_factor
  				SCREAMING_SNAKE_CASE    	=   rescale_offset
  				SCREAMING_SNAKE_CASE    	=   do_normalize
  				SCREAMING_SNAKE_CASE    	=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  				SCREAMING_SNAKE_CASE    	=   image_std if image_std is not None else IMAGENET_STANDARD_STD
  				SCREAMING_SNAKE_CASE    	=   include_top
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any]      ,    lowercase			:np.ndarray      ,    lowercase			:Dict[str, int]      ,    lowercase			:PILImageResampling = PIL.Image.NEAREST      ,    lowercase			:Optional[Union[str, ChannelDimension]] = None      ,    **lowercase			:int      ,    )		->			np.ndarray:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   get_size_dict(lowercase )
  				if "height" not in size or "width" not in size:
  							raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
  				return resize(
  				    lowercase      ,    size=(size['''height'''], size['''width'''])      ,    resample=lowercase      ,    data_format=lowercase      ,    **lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:np.ndarray      ,    lowercase			:Dict[str, int]      ,    lowercase			:Optional[Union[str, ChannelDimension]] = None      ,    **lowercase			:Optional[Any]      ,    )		->			np.ndarray:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   get_size_dict(lowercase )
  				if "height" not in size or "width" not in size:
  							raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
  				return center_crop(lowercase      ,    size=(size['''height'''], size['''width'''])      ,    data_format=lowercase      ,    **lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any      ,    lowercase			:np.ndarray      ,    lowercase			:Union[int, float]      ,    lowercase			:bool = True      ,    lowercase			:Optional[Union[str, ChannelDimension]] = None      ,    **lowercase			:Union[str, Any]      ,    )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if offset:
  							SCREAMING_SNAKE_CASE    	=   (image - 1_2_7.5) * scale
  							if data_format is not None:
  										SCREAMING_SNAKE_CASE    	=   to_channel_dimension_format(lowercase      ,    lowercase )
  							SCREAMING_SNAKE_CASE    	=   rescaled_image.astype(np.floataa )
  				else:
  							SCREAMING_SNAKE_CASE    	=   rescale(lowercase      ,    scale=lowercase      ,    data_format=lowercase      ,    **lowercase )
  				return rescaled_image
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:np.ndarray      ,    lowercase			:Union[float, List[float]]      ,    lowercase			:Union[float, List[float]]      ,    lowercase			:Optional[Union[str, ChannelDimension]] = None      ,    **lowercase			:int      ,    )		->			np.ndarray:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return normalize(lowercase      ,    mean=lowercase      ,    std=lowercase      ,    data_format=lowercase      ,    **lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict      ,    lowercase			:ImageInput      ,    lowercase			:bool = None      ,    lowercase			:Dict[str, int] = None      ,    lowercase			:Optional[int]=None      ,    lowercase			:bool = None      ,    lowercase			:Dict[str, int] = None      ,    lowercase			:bool = None      ,    lowercase			:float = None      ,    lowercase			:bool = None      ,    lowercase			:bool = None      ,    lowercase			:Optional[Union[float, List[float]]] = None      ,    lowercase			:Optional[Union[float, List[float]]] = None      ,    lowercase			:bool = None      ,    lowercase			:Optional[Union[str, TensorType]] = None      ,    lowercase			:ChannelDimension = ChannelDimension.FIRST      ,    **lowercase			:Dict      ,    )		->			PIL.Image.Image:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   do_resize if do_resize is not None else self.do_resize
  				SCREAMING_SNAKE_CASE    	=   resample if resample is not None else self.resample
  				SCREAMING_SNAKE_CASE    	=   do_center_crop if do_center_crop is not None else self.do_center_crop
  				SCREAMING_SNAKE_CASE    	=   do_rescale if do_rescale is not None else self.do_rescale
  				SCREAMING_SNAKE_CASE    	=   rescale_factor if rescale_factor is not None else self.rescale_factor
  				SCREAMING_SNAKE_CASE    	=   rescale_offset if rescale_offset is not None else self.rescale_offset
  				SCREAMING_SNAKE_CASE    	=   do_normalize if do_normalize is not None else self.do_normalize
  				SCREAMING_SNAKE_CASE    	=   image_mean if image_mean is not None else self.image_mean
  				SCREAMING_SNAKE_CASE    	=   image_std if image_std is not None else self.image_std
  				SCREAMING_SNAKE_CASE    	=   include_top if include_top is not None else self.include_top
  
  				SCREAMING_SNAKE_CASE    	=   size if size is not None else self.size
  				SCREAMING_SNAKE_CASE    	=   get_size_dict(lowercase )
  				SCREAMING_SNAKE_CASE    	=   crop_size if crop_size is not None else self.crop_size
  				SCREAMING_SNAKE_CASE    	=   get_size_dict(lowercase      ,    param_name='''crop_size''' )
  
  				SCREAMING_SNAKE_CASE    	=   make_list_of_images(lowercase )
  
  				if not valid_images(lowercase ):
  							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 or resample is None:
  							raise ValueError('''Size and resample 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.''' )
  
  				if do_normalize and (image_mean is None or image_std is None):
  							raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
  
  				# All transformations expect numpy arrays.
  				SCREAMING_SNAKE_CASE    	=   [to_numpy_array(lowercase ) for image in images]
  
  				if do_resize:
  							SCREAMING_SNAKE_CASE    	=   [self.resize(image=lowercase      ,    size=lowercase      ,    resample=lowercase ) for image in images]
  
  				if do_center_crop:
  							SCREAMING_SNAKE_CASE    	=   [self.center_crop(image=lowercase      ,    size=lowercase ) for image in images]
  
  				if do_rescale:
  							SCREAMING_SNAKE_CASE    	=   [self.rescale(image=lowercase      ,    scale=lowercase      ,    offset=lowercase ) for image in images]
  
  				if do_normalize:
  							SCREAMING_SNAKE_CASE    	=   [self.normalize(image=lowercase      ,    mean=lowercase      ,    std=lowercase ) for image in images]
  
  				if include_top:
  							SCREAMING_SNAKE_CASE    	=   [self.normalize(image=lowercase      ,    mean=[0, 0, 0]      ,    std=lowercase ) for image in images]
  
  				SCREAMING_SNAKE_CASE    	=   [to_channel_dimension_format(lowercase      ,    lowercase ) for image in images]
  
  				SCREAMING_SNAKE_CASE    	=   {'''pixel_values''': images}
  				return BatchFeature(data=lowercase      ,    tensor_type=lowercase )
style_context: 
  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 						lowerCamelCase			(					__lowerCamelCase   ,		unittest.TestCase				):
  	UpperCamelCase_     :				List[str]								=  GPTNeoXJapaneseTokenizer
  	UpperCamelCase_     :				List[Any]								=  False
  	UpperCamelCase_     :				int								=  {'do_clean_text': False, 'add_prefix_space': False}
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int] )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().setUp()
  
  				SCREAMING_SNAKE_CASE    	=   [
  				    '''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|>''',
  				]
  				SCREAMING_SNAKE_CASE    	=   {'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
  				SCREAMING_SNAKE_CASE    	=   {'''unk_token''': '''<unk>'''}
  
  				SCREAMING_SNAKE_CASE    	=   os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES['''vocab_file'''] )
  				SCREAMING_SNAKE_CASE    	=   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    snake_case__    (    self			:Dict      ,    **lowercase			:Optional[int] )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				kwargs.update(self.special_tokens_map )
  				return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname      ,    **lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict      ,    lowercase			:Union[str, Any] )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   '''こんにちは、世界。 \nこんばんは、㔺界。😀'''
  				SCREAMING_SNAKE_CASE    	=   '''こんにちは、世界。 \nこんばんは、世界。😀'''
  				return input_text, output_text
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:Union[str, Any] )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   self.get_input_output_texts(lowercase )
  				SCREAMING_SNAKE_CASE    	=   tokenizer.encode(lowercase      ,    add_special_tokens=lowercase )
  				SCREAMING_SNAKE_CASE    	=   tokenizer.decode(lowercase      ,    clean_up_tokenization_spaces=lowercase )
  				return text, ids
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				pass  # TODO add if relevant
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str] )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				pass  # TODO add if relevant
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[Any] )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				pass  # TODO add if relevant
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[Any] )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.get_tokenizer()
  
  				# Testing tokenization
  				SCREAMING_SNAKE_CASE    	=   '''こんにちは、世界。　こんばんは、㔺界。'''
  				SCREAMING_SNAKE_CASE    	=   ['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
  				SCREAMING_SNAKE_CASE    	=   tokenizer.tokenize(lowercase )
  				self.assertListEqual(lowercase      ,    lowercase )
  
  				# Testing conversion to ids without special tokens
  				SCREAMING_SNAKE_CASE    	=   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
  				SCREAMING_SNAKE_CASE    	=   tokenizer.convert_tokens_to_ids(lowercase )
  				self.assertListEqual(lowercase      ,    lowercase )
  
  				# Testing conversion to ids with special tokens
  				SCREAMING_SNAKE_CASE    	=   tokens + [tokenizer.unk_token]
  				SCREAMING_SNAKE_CASE    	=   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
  				SCREAMING_SNAKE_CASE    	=   tokenizer.convert_tokens_to_ids(lowercase )
  				self.assertListEqual(lowercase      ,    lowercase )
  
  
  
  
  
  
  
  	@slow
  	def    snake_case__    (    self			:str )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.tokenizer_class.from_pretrained('''abeja/gpt-neox-japanese-2.7b''' )
  
  				SCREAMING_SNAKE_CASE    	=   tokenizer.encode('''ありがとう。'''      ,    add_special_tokens=lowercase )
  				SCREAMING_SNAKE_CASE    	=   tokenizer.encode('''どういたしまして。'''      ,    add_special_tokens=lowercase )
  
  				SCREAMING_SNAKE_CASE    	=   tokenizer.build_inputs_with_special_tokens(lowercase )
  				SCREAMING_SNAKE_CASE    	=   tokenizer.build_inputs_with_special_tokens(lowercase      ,    lowercase )
  
  				assert encoded_sentence == ids_a
  				assert encoded_pair == ids_a + ids_a
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any] )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				pass
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				pass