



'''simple docstring'''

import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 			__magic_name__		(					_UpperCamelCase    ,		unittest.TestCase     ):
							lowerCAmelCase :					Dict            =      BertweetTokenizer
							lowerCAmelCase :					str            =      False







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

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									_a  :   int				 =							['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
									_a  :   List[str]				 =							dict(zip(A_			,range(len(A_	)	)	)	)
									_a  :   Optional[int]				 =							['#version: 0.2', 'a m</w>']
									_a  :   Any				 =							{'unk_token': '<unk>'}

									_a  :   List[Any]				 =							os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['vocab_file']	)
									_a  :   Dict				 =							os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['merges_file']	)
									with open(self.vocab_file			,'w'			,encoding='utf-8'	) as fp:
											for token in vocab_tokens:
													fp.write(F"""{token} {vocab_tokens[token]}\n"""	)
									with open(self.merges_file			,'w'			,encoding='utf-8'	) as fp:
											fp.write('\n'.join(A_	)	)







							def 							__lowercase    (							self      : Dict			,**_UpperCAmelCase      : Any	):
									kwargs.update(self.special_tokens_map	)
									return BertweetTokenizer.from_pretrained(self.tmpdirname			,**A_	)







							def 							__lowercase    (							self      : Any			,_UpperCAmelCase      : Any	):
									_a  :   Any				 =							'I am VinAI Research'
									_a  :   Optional[Any]				 =							'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
									return input_text, output_text







							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[Any]				 =							BertweetTokenizer(self.vocab_file			,self.merges_file			,**self.special_tokens_map	)
									_a  :   Any				 =							'I am VinAI Research'
									_a  :   Dict				 =							'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
									_a  :   Union[str, Any]				 =							tokenizer.tokenize(A_	)
									self.assertListEqual(A_			,A_	)

									_a  :   List[Any]				 =							tokens + [tokenizer.unk_token]

									_a  :   Union[str, Any]				 =							[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)			,A_	)









'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__lowerCAmelCase		    =     logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					List[str]            =      ['pixel_values']







							def __init__(							self      : int			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : Dict[str, int] = None			,_UpperCAmelCase      : PILImageResampling = PILImageResampling.BILINEAR			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : bool = False			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : Union[int, float] = 1 / 255			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : Optional[Union[float, List[float]]] = None			,_UpperCAmelCase      : Optional[Union[float, List[float]]] = None			,**_UpperCAmelCase      : Optional[Any]			,):
									super().__init__(**_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							size if size is not None else {'height': 2560, 'width': 1920}
									if isinstance(_UpperCAmelCase			,(tuple, list)	):
											# The previous feature extractor size parameter was in (width, height) format
											_a  :   Union[str, Any]				 =							size[::-1]
									_a  :   Dict				 =							get_size_dict(_UpperCAmelCase	)

									_a  :   int				 =							do_resize
									_a  :   Any				 =							size
									_a  :   Any				 =							resample
									_a  :   int				 =							do_thumbnail
									_a  :   Tuple				 =							do_align_long_axis
									_a  :   Optional[Any]				 =							do_pad
									_a  :   Any				 =							do_rescale
									_a  :   List[str]				 =							rescale_factor
									_a  :   Any				 =							do_normalize
									_a  :   Union[str, Any]				 =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									_a  :   Any				 =							image_std if image_std is not None else IMAGENET_STANDARD_STD







							def 							__lowercase    (							self      : Optional[Any]			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Dict[str, int]			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None	):
									_a ,      _a  :   List[Any]				 =							get_image_size(_UpperCAmelCase	)
									_a ,      _a  :   List[str]				 =							size['height'], size['width']

									if (output_width < output_height and input_width > input_height) or (
									    output_width > output_height and input_width < input_height
									):
											_a  :   Optional[int]				 =							np.rotaa(_UpperCAmelCase			,3	)

									if data_format is not None:
											_a  :   Any				 =							to_channel_dimension_format(_UpperCAmelCase			,_UpperCAmelCase	)

									return image







							def 							__lowercase    (							self      : Any			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Dict[str, int]			,_UpperCAmelCase      : bool = False			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None			,):
									_a ,      _a  :   List[Any]				 =							size['height'], size['width']
									_a ,      _a  :   str				 =							get_image_size(_UpperCAmelCase	)

									_a  :   List[str]				 =							output_width - input_width
									_a  :   Union[str, Any]				 =							output_height - input_height

									if random_padding:
											_a  :   Union[str, Any]				 =							np.random.randint(low=0			,high=delta_height + 1	)
											_a  :   Any				 =							np.random.randint(low=0			,high=delta_width + 1	)
									else:
											_a  :   Tuple				 =							delta_height // 2
											_a  :   List[Any]				 =							delta_width // 2

									_a  :   Dict				 =							delta_height - pad_top
									_a  :   Optional[int]				 =							delta_width - pad_left

									_a  :   int				 =							((pad_top, pad_bottom), (pad_left, pad_right))
									return pad(_UpperCAmelCase			,_UpperCAmelCase			,data_format=_UpperCAmelCase	)







							def 							__lowercase    (							self      : List[Any]			,*_UpperCAmelCase      : int			,**_UpperCAmelCase      : List[str]	):
									logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'	)
									return self.pad_image(*_UpperCAmelCase			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : Dict			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Dict[str, int]			,_UpperCAmelCase      : PILImageResampling = PILImageResampling.BICUBIC			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None			,**_UpperCAmelCase      : Union[str, Any]			,):
									_a ,      _a  :   Any				 =							get_image_size(_UpperCAmelCase	)
									_a ,      _a  :   Union[str, Any]				 =							size['height'], size['width']

									# We always resize to the smallest of either the input or output size.
									_a  :   Dict				 =							min(_UpperCAmelCase			,_UpperCAmelCase	)
									_a  :   List[Any]				 =							min(_UpperCAmelCase			,_UpperCAmelCase	)

									if height == input_height and width == input_width:
											return image

									if input_height > input_width:
											_a  :   Any				 =							int(input_width * height / input_height	)
									elif input_width > input_height:
											_a  :   int				 =							int(input_height * width / input_width	)

									return resize(
									    _UpperCAmelCase			,size=(height, width)			,resample=_UpperCAmelCase			,reducing_gap=2.0			,data_format=_UpperCAmelCase			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : List[str]			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Dict[str, int]			,_UpperCAmelCase      : PILImageResampling = PILImageResampling.BICUBIC			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None			,**_UpperCAmelCase      : List[Any]			,):
									_a  :   List[Any]				 =							get_size_dict(_UpperCAmelCase	)
									_a  :   int				 =							min(size['height']			,size['width']	)
									_a  :   Dict				 =							get_resize_output_image_size(_UpperCAmelCase			,size=_UpperCAmelCase			,default_to_square=_UpperCAmelCase	)
									_a  :   int				 =							resize(_UpperCAmelCase			,size=_UpperCAmelCase			,resample=_UpperCAmelCase			,data_format=_UpperCAmelCase			,**_UpperCAmelCase	)
									return resized_image







							def 							__lowercase    (							self      : int			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Union[int, float]			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None			,**_UpperCAmelCase      : Optional[Any]			,):
									return rescale(_UpperCAmelCase			,scale=_UpperCAmelCase			,data_format=_UpperCAmelCase			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : List[str]			,_UpperCAmelCase      : np.ndarray			,_UpperCAmelCase      : Union[float, List[float]]			,_UpperCAmelCase      : Union[float, List[float]]			,_UpperCAmelCase      : Optional[Union[str, ChannelDimension]] = None			,**_UpperCAmelCase      : Dict			,):
									return normalize(_UpperCAmelCase			,mean=_UpperCAmelCase			,std=_UpperCAmelCase			,data_format=_UpperCAmelCase			,**_UpperCAmelCase	)







							def 							__lowercase    (							self      : Optional[int]			,_UpperCAmelCase      : ImageInput			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : Dict[str, int] = None			,_UpperCAmelCase      : PILImageResampling = None			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : bool = False			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : float = None			,_UpperCAmelCase      : bool = None			,_UpperCAmelCase      : Optional[Union[float, List[float]]] = None			,_UpperCAmelCase      : Optional[Union[float, List[float]]] = None			,_UpperCAmelCase      : Optional[Union[str, TensorType]] = None			,_UpperCAmelCase      : Optional[ChannelDimension] = ChannelDimension.FIRST			,**_UpperCAmelCase      : int			,):
									_a  :   List[str]				 =							do_resize if do_resize is not None else self.do_resize
									_a  :   Optional[Any]				 =							size if size is not None else self.size
									if isinstance(_UpperCAmelCase			,(tuple, list)	):
											# Previous feature extractor had size in (width, height) format
											_a  :   Optional[int]				 =							size[::-1]
									_a  :   Tuple				 =							get_size_dict(_UpperCAmelCase	)
									_a  :   Optional[int]				 =							resample if resample is not None else self.resample
									_a  :   Union[str, Any]				 =							do_thumbnail if do_thumbnail is not None else self.do_thumbnail
									_a  :   int				 =							do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
									_a  :   Optional[Any]				 =							do_pad if do_pad is not None else self.do_pad
									_a  :   str				 =							do_rescale if do_rescale is not None else self.do_rescale
									_a  :   Dict				 =							rescale_factor if rescale_factor is not None else self.rescale_factor
									_a  :   Dict				 =							do_normalize if do_normalize is not None else self.do_normalize
									_a  :   Optional[int]				 =							image_mean if image_mean is not None else self.image_mean
									_a  :   Dict				 =							image_std if image_std is not None else self.image_std

									_a  :   Optional[Any]				 =							make_list_of_images(_UpperCAmelCase	)

									if not valid_images(_UpperCAmelCase	):
											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_rescale and rescale_factor is None:
											raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

									if do_pad and size is None:
											raise ValueError('Size must be specified if do_pad 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.
									_a  :   Dict				 =							[to_numpy_array(_UpperCAmelCase	) for image in images]

									if do_align_long_axis:
											_a  :   Optional[Any]				 =							[self.align_long_axis(_UpperCAmelCase			,size=_UpperCAmelCase	) for image in images]

									if do_resize:
											_a  :   List[Any]				 =							[self.resize(image=_UpperCAmelCase			,size=_UpperCAmelCase			,resample=_UpperCAmelCase	) for image in images]

									if do_thumbnail:
											_a  :   Dict				 =							[self.thumbnail(image=_UpperCAmelCase			,size=_UpperCAmelCase	) for image in images]

									if do_pad:
											_a  :   List[str]				 =							[self.pad_image(image=_UpperCAmelCase			,size=_UpperCAmelCase			,random_padding=_UpperCAmelCase	) for image in images]

									if do_rescale:
											_a  :   List[str]				 =							[self.rescale(image=_UpperCAmelCase			,scale=_UpperCAmelCase	) for image in images]

									if do_normalize:
											_a  :   List[str]				 =							[self.normalize(image=_UpperCAmelCase			,mean=_UpperCAmelCase			,std=_UpperCAmelCase	) for image in images]

									_a  :   Optional[Any]				 =							[to_channel_dimension_format(_UpperCAmelCase			,_UpperCAmelCase	) for image in images]

									_a  :   List[Any]				 =							{'pixel_values': images}
									return BatchFeature(data=_UpperCAmelCase			,tensor_type=_UpperCAmelCase	)




