'''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,
    get_resize_output_image_size,
    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, logging


a=  logging.get_logger(__name__)






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						['''pixel_values''']







     def __init__(		self     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = PILImageResampling.BILINEAR     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = True     ,			_lowerCamelCase = 1 / 2_5_5     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         super().__init__(**_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  size if size is not None else {'shortest_edge': 2_5_6}
         __UpperCamelCase	:	str		    =  get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         __UpperCamelCase	:	Tuple		    =  get_size_dict(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  do_resize
         __UpperCamelCase	:	Optional[Any]		    =  size
         __UpperCamelCase	:	Optional[int]		    =  resample
         __UpperCamelCase	:	Tuple		    =  do_center_crop
         __UpperCamelCase	:	List[str]		    =  crop_size
         __UpperCamelCase	:	List[str]		    =  do_rescale
         __UpperCamelCase	:	Dict		    =  rescale_factor
         __UpperCamelCase	:	str		    =  do_normalize
         __UpperCamelCase	:	Tuple		    =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __UpperCamelCase	:	str		    =  image_std if image_std is not None else IMAGENET_STANDARD_STD







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = PILImageResampling.BICUBIC     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	Optional[int]		    =  get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase    )
         if "shortest_edge" not in size:
             raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
         __UpperCamelCase	:	Union[str, Any]		    =  get_resize_output_image_size(_lowerCamelCase     ,			size=size['shortest_edge']     ,			default_to_square=_lowerCamelCase    )
         return resize(_lowerCamelCase     ,			size=_lowerCamelCase     ,			resample=_lowerCamelCase     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	Dict		    =  get_size_dict(_lowerCamelCase    )
         return center_crop(_lowerCamelCase     ,			size=(size['height'], size['width'])     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			**_lowerCamelCase    ):
         return rescale(_lowerCamelCase     ,			scale=_lowerCamelCase     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         return normalize(_lowerCamelCase     ,			mean=_lowerCamelCase     ,			std=_lowerCamelCase     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = ChannelDimension.FIRST     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	Dict		    =  do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase	:	Optional[int]		    =  size if size is not None else self.size
         __UpperCamelCase	:	Optional[int]		    =  get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  resample if resample is not None else self.resample
         __UpperCamelCase	:	List[Any]		    =  do_center_crop if do_center_crop is not None else self.do_center_crop
         __UpperCamelCase	:	Dict		    =  crop_size if crop_size is not None else self.crop_size
         __UpperCamelCase	:	Tuple		    =  get_size_dict(_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  do_rescale if do_rescale is not None else self.do_rescale
         __UpperCamelCase	:	Any		    =  rescale_factor if rescale_factor is not None else self.rescale_factor
         __UpperCamelCase	:	str		    =  do_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase	:	int		    =  image_mean if image_mean is not None else self.image_mean
         __UpperCamelCase	:	Tuple		    =  image_std if image_std is not None else self.image_std

         __UpperCamelCase	:	Any		    =  make_list_of_images(_lowerCamelCase    )

         if not valid_images(_lowerCamelCase    ):
             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_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.
         __UpperCamelCase	:	Union[str, Any]		    =  [to_numpy_array(_lowerCamelCase    ) for image in images]

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

         if do_center_crop:
             __UpperCamelCase	:	List[str]		    =  [self.center_crop(image=_lowerCamelCase     ,			size=_lowerCamelCase    ) for image in images]

         if do_rescale:
             __UpperCamelCase	:	Any		    =  [self.rescale(image=_lowerCamelCase     ,			scale=_lowerCamelCase    ) for image in images]

         if do_normalize:
             __UpperCamelCase	:	int		    =  [self.normalize(image=_lowerCamelCase     ,			mean=_lowerCamelCase     ,			std=_lowerCamelCase    ) for image in images]

         __UpperCamelCase	:	List[str]		    =  [to_channel_dimension_format(_lowerCamelCase     ,			_lowerCamelCase    ) for image in images]

         __UpperCamelCase	:	str		    =  {'pixel_values': images}
         return BatchFeature(data=_lowerCamelCase     ,			tensor_type=_lowerCamelCase    )


'''simple docstring'''
import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizer
     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						True







     def lowerCAmelCase     (		self    ):
         super().setUp()

         __UpperCamelCase	:	List[str]		    =  [
             '[UNK]',
             '[CLS]',
             '[SEP]',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         __UpperCamelCase	:	Union[str, Any]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'    ) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  'UNwant\u00E9d,running'
         __UpperCamelCase	:	Union[str, Any]		    =  'unwanted, running'
         return input_text, output_text







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.tokenizer_class(self.vocab_file    )

         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.tokenize('UNwant\u00E9d,running'    )
         self.assertListEqual(_lowerCamelCase     ,			['un', '##want', '##ed', ',', 'runn', '##ing']    )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			[7, 4, 5, 1_0, 8, 9]    )







     def lowerCAmelCase     (		self    ):
         pass



