



"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
	import PIL

A    =			logging.get_logger(__name__)

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Dict:


							if isinstance(_UpperCamelCase				,      (list, tuple)			) and isinstance(videos[0]				,      (list, tuple)			) and is_valid_image(videos[0][0]			):
														return videos

							elif isinstance(_UpperCamelCase				,      (list, tuple)			) and is_valid_image(videos[0]			):
														return [videos]

							elif is_valid_image(_UpperCamelCase			):
														return [[videos]]

							raise ValueError(f"""Could not make batched video from {videos}"""			)






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     ["""pixel_values"""]



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






									'''simple docstring'''

									super().__init__(**UpperCAmelCase__				)
									A         =					size if size is not None else {'shortest_edge': 256}
									A         =					get_size_dict(UpperCAmelCase__      ,							default_to_square=UpperCAmelCase__				)
									A         =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
									A         =					get_size_dict(UpperCAmelCase__      ,							param_name='crop_size'				)

									A         =					do_resize
									A         =					size
									A         =					do_center_crop
									A         =					crop_size
									A         =					resample
									A         =					do_rescale
									A         =					rescale_factor
									A         =					offset
									A         =					do_normalize
									A         =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									A         =					image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    PILImageResampling = PILImageResampling.BILINEAR      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    List[str]      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(UpperCAmelCase__      ,							default_to_square=UpperCAmelCase__				)
									if "shortest_edge" in size:
																A         =					get_resize_output_image_size(UpperCAmelCase__      ,							size['shortest_edge']      ,							default_to_square=UpperCAmelCase__				)
									elif "height" in size and "width" in size:
																A         =					(size['height'], size['width'])
									else:
																raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
									return resize(UpperCAmelCase__      ,							size=UpperCAmelCase__      ,							resample=UpperCAmelCase__      ,							data_format=UpperCAmelCase__      ,							**UpperCAmelCase__				)



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Dict      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(UpperCAmelCase__				)
									if "height" not in size or "width" not in size:
																raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""				)
									return center_crop(UpperCAmelCase__      ,							size=(size['height'], size['width'])      ,							data_format=UpperCAmelCase__      ,							**UpperCAmelCase__				)



		def 					A_		( self  :    Dict      ,							snake_case  :    np.ndarray      ,							snake_case  :    Union[int, float]      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    List[Any]      ,							)			->						Optional[int]:






									'''simple docstring'''

									A         =					image.astype(np.floataa				)
									if offset:
																A         =					image - (scale / 2)
									return rescale(UpperCAmelCase__      ,							scale=UpperCAmelCase__      ,							data_format=UpperCAmelCase__      ,							**UpperCAmelCase__				)



		def 					A_		( self  :    Any      ,							snake_case  :    np.ndarray      ,							snake_case  :    Union[float, List[float]]      ,							snake_case  :    Union[float, List[float]]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    str      ,							)			->						np.ndarray:






									'''simple docstring'''

									return normalize(UpperCAmelCase__      ,							mean=UpperCAmelCase__      ,							std=UpperCAmelCase__      ,							data_format=UpperCAmelCase__      ,							**UpperCAmelCase__				)



		def 					A_		( self  :    int      ,							snake_case  :    ImageInput      ,							snake_case  :    bool = None      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    PILImageResampling = None      ,							snake_case  :    bool = None      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    bool = None      ,							snake_case  :    float = None      ,							snake_case  :    bool = None      ,							snake_case  :    bool = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[ChannelDimension] = ChannelDimension.FIRST      ,							)			->						np.ndarray:






									'''simple docstring'''

									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.'				)

									if offset and not do_rescale:
																raise ValueError('For offset, do_rescale must also be set to True.'				)

									# All transformations expect numpy arrays.
									A         =					to_numpy_array(UpperCAmelCase__				)

									if do_resize:
																A         =					self.resize(image=UpperCAmelCase__      ,							size=UpperCAmelCase__      ,							resample=UpperCAmelCase__				)

									if do_center_crop:
																A         =					self.center_crop(UpperCAmelCase__      ,							size=UpperCAmelCase__				)

									if do_rescale:
																A         =					self.rescale(image=UpperCAmelCase__      ,							scale=UpperCAmelCase__      ,							offset=UpperCAmelCase__				)

									if do_normalize:
																A         =					self.normalize(image=UpperCAmelCase__      ,							mean=UpperCAmelCase__      ,							std=UpperCAmelCase__				)

									A         =					to_channel_dimension_format(UpperCAmelCase__      ,							UpperCAmelCase__				)
									return image



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    ImageInput      ,							snake_case  :    bool = None      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    PILImageResampling = None      ,							snake_case  :    bool = None      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    bool = None      ,							snake_case  :    float = None      ,							snake_case  :    bool = None      ,							snake_case  :    bool = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[str, TensorType]] = None      ,							snake_case  :    ChannelDimension = ChannelDimension.FIRST      ,							**snake_case  :    Union[str, Any]      ,							)			->						PIL.Image.Image:






									'''simple docstring'''

									A         =					do_resize if do_resize is not None else self.do_resize
									A         =					resample if resample is not None else self.resample
									A         =					do_center_crop if do_center_crop is not None else self.do_center_crop
									A         =					do_rescale if do_rescale is not None else self.do_rescale
									A         =					rescale_factor if rescale_factor is not None else self.rescale_factor
									A         =					offset if offset is not None else self.offset
									A         =					do_normalize if do_normalize is not None else self.do_normalize
									A         =					image_mean if image_mean is not None else self.image_mean
									A         =					image_std if image_std is not None else self.image_std

									A         =					size if size is not None else self.size
									A         =					get_size_dict(UpperCAmelCase__      ,							default_to_square=UpperCAmelCase__				)
									A         =					crop_size if crop_size is not None else self.crop_size
									A         =					get_size_dict(UpperCAmelCase__      ,							param_name='crop_size'				)

									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.'				)

									A         =					make_batched(UpperCAmelCase__				)

									A         =					[
									    [
									        self._preprocess_image(
									            image=UpperCAmelCase__      ,							do_resize=UpperCAmelCase__      ,							size=UpperCAmelCase__      ,							resample=UpperCAmelCase__      ,							do_center_crop=UpperCAmelCase__      ,							crop_size=UpperCAmelCase__      ,							do_rescale=UpperCAmelCase__      ,							rescale_factor=UpperCAmelCase__      ,							offset=UpperCAmelCase__      ,							do_normalize=UpperCAmelCase__      ,							image_mean=UpperCAmelCase__      ,							image_std=UpperCAmelCase__      ,							data_format=UpperCAmelCase__      ,							)
									        for img in video
									    ]
									    for video in videos
									]

									A         =					{'pixel_values': videos}
									return BatchFeature(data=UpperCAmelCase__      ,							tensor_type=UpperCAmelCase__				)








"""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      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Tuple 							=     LayoutLMTokenizer
		lowerCAmelCase_       :	Any 							=     LayoutLMTokenizerFast
		lowerCAmelCase_       :	Optional[int] 							=     True
		lowerCAmelCase_       :	List[Any] 							=     True



		def 					A_		( self  :    Optional[Any]				)			->						Optional[Any]:






									'''simple docstring'''

									super().setUp()

									A         =					[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									A         =					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 					A_		( self  :    str      ,							**snake_case  :    Tuple				)			->						Union[str, Any]:






									'''simple docstring'''

									return LayoutLMTokenizer.from_pretrained(self.tmpdirname      ,							**snake_case				)



		def 					A_		( self  :    List[str]      ,							snake_case  :    int				)			->						List[Any]:






									'''simple docstring'''

									A         =					'UNwant\u00E9d,running'
									A         =					'unwanted, running'
									return input_text, output_text



		def 					A_		( self  :    Tuple				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.tokenizer_class(self.vocab_file				)

									A         =					tokenizer.tokenize('UNwant\u00E9d,running'				)
									self.assertListEqual(snake_case      ,							['un', '##want', '##ed', ',', 'runn', '##ing']				)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case				)      ,							[7, 4, 5, 10, 8, 9]				)



		def 					A_		( self  :    Any				)			->						List[Any]:






									'''simple docstring'''

									pass



