



'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case_					:      Dict    			=    logging.get_logger(__name__)






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     ["""pixel_values"""]





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







							'''simple docstring'''
							super().__init__(**lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  size if size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase   :						Dict												=  get_size_dict(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase   :						Dict												=  get_size_dict(lowerCamelCase__					,default_to_square=lowerCamelCase__					,param_name='crop_size'					)

							_UpperCamelCase   :						List[str]												=  do_resize
							_UpperCamelCase   :						Any												=  do_rescale
							_UpperCamelCase   :						List[str]												=  do_normalize
							_UpperCamelCase   :						Dict												=  do_center_crop
							_UpperCamelCase   :						str												=  crop_size
							_UpperCamelCase   :						List[Any]												=  size
							_UpperCamelCase   :						List[Any]												=  resample
							_UpperCamelCase   :						Optional[int]												=  rescale_factor
							_UpperCamelCase   :						Optional[int]												=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_UpperCamelCase   :						str												=  image_std if image_std is not None else IMAGENET_DEFAULT_STD





	def       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Dict[str, int]					,lowerCamelCase__   :							PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							Dict					,):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  get_size_dict(lowerCamelCase__					)

							if "shortest_edge" in size:
													_UpperCamelCase   :						Any												=  get_resize_output_image_size(lowerCamelCase__					,size=size['shortest_edge']					,default_to_square=lowerCamelCase__					)
													# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
													_UpperCamelCase   :						int												=  (size['height'], size['width'])
							else:
													raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'					)
							return resize(lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Dict[str, int]					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  get_size_dict(lowerCamelCase__					)
							if "height" not in size or "width" not in size:
													raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'					)
							return center_crop(lowerCamelCase__					,size=(size['height'], size['width'])					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							float					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							str					):







							'''simple docstring'''
							return rescale(lowerCamelCase__					,scale=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Union[float, List[float]]					,lowerCamelCase__   :							Union[float, List[float]]					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							int					,):







							'''simple docstring'''
							return normalize(lowerCamelCase__					,mean=lowerCamelCase__					,std=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							ImageInput					,lowerCamelCase__   :							Optional[bool] = None					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = None					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							int = None					,lowerCamelCase__   :							Optional[bool] = None					,lowerCamelCase__   :							Optional[float] = None					,lowerCamelCase__   :							Optional[bool] = None					,lowerCamelCase__   :							Optional[Union[float, List[float]]] = None					,lowerCamelCase__   :							Optional[Union[float, List[float]]] = None					,lowerCamelCase__   :							Optional[Union[str, TensorType]] = None					,lowerCamelCase__   :							Union[str, ChannelDimension] = ChannelDimension.FIRST					,**lowerCamelCase__   :							str					,):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase   :						List[str]												=  do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase   :						Tuple												=  do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase   :						Tuple												=  do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase   :						Any												=  crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase   :						Optional[int]												=  get_size_dict(lowerCamelCase__					,param_name='crop_size'					,default_to_square=lowerCamelCase__					)
							_UpperCamelCase   :						Optional[int]												=  resample if resample is not None else self.resample
							_UpperCamelCase   :						List[str]												=  rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase   :						str												=  image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase   :						Optional[int]												=  image_std if image_std is not None else self.image_std

							_UpperCamelCase   :						Optional[int]												=  size if size is not None else self.size
							_UpperCamelCase   :						Dict												=  get_size_dict(lowerCamelCase__					)

							if not is_batched(lowerCamelCase__					):
													_UpperCamelCase   :						Any												=  [images]

							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.'					)

							# All transformations expect numpy arrays.
							_UpperCamelCase   :						Optional[Any]												=  [to_numpy_array(lowerCamelCase__					) for image in images]

							if do_resize:
													_UpperCamelCase   :						List[str]												=  [self.resize(image=lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					) for image in images]

							if do_center_crop:
													_UpperCamelCase   :						Optional[Any]												=  [self.center_crop(image=lowerCamelCase__					,size=lowerCamelCase__					) for image in images]

							if do_rescale:
													_UpperCamelCase   :						Union[str, Any]												=  [self.rescale(image=lowerCamelCase__					,scale=lowerCamelCase__					) for image in images]

							if do_normalize:
													_UpperCamelCase   :						Tuple												=  [self.normalize(image=lowerCamelCase__					,mean=lowerCamelCase__					,std=lowerCamelCase__					) for image in images]

							_UpperCamelCase   :						Dict												=  [to_channel_dimension_format(lowerCamelCase__					,lowerCamelCase__					) for image in images]

							_UpperCamelCase   :						Dict												=  {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__					,tensor_type=lowerCamelCase__					)







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


snake_case_					:      Tuple    			=    {
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      int    			=    ['LayoutLMv2TokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Union[str, Any]    			=    ['LayoutLMv2FeatureExtractor']
					snake_case_					:      str    			=    ['LayoutLMv2ImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Optional[int]    			=    [
					    'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'LayoutLMv2ForQuestionAnswering',
					    'LayoutLMv2ForSequenceClassification',
					    'LayoutLMv2ForTokenClassification',
					    'LayoutLMv2Layer',
					    'LayoutLMv2Model',
					    'LayoutLMv2PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaLayer,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)
else:
					import sys

					snake_case_					:      int    			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


