



'''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


UpperCamelCase__:   int								=			logging.get_logger(__name__)

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       ["""pixel_values"""]







	def __init__(						self			:     int		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = PIL.Image.BICUBIC		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__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:
								super().__init__(**__snake_case		)
								UpperCAmelCase :       Any 							=		size if size is not None else {'''height''': 256, '''width''': 256}
								UpperCAmelCase :       int 							=		get_size_dict(__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								UpperCAmelCase :       str 							=		get_size_dict(__snake_case		,			param_name='''crop_size'''		)

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







	def      A	(						self			:     Any		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     PILImageResampling = PIL.Image.BICUBIC		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Optional[int]		,			)			->		np.ndarray:
								UpperCAmelCase :       Dict 							=		get_size_dict(__snake_case		)
								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(
								    __snake_case		,			size=(size['''height'''], size['''width'''])		,			resample=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Any		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     int		,			)			->		np.ndarray:
								UpperCAmelCase :       Optional[int] 							=		get_size_dict(__snake_case		)
								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(__snake_case		,			size=(size['''height'''], size['''width'''])		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     np.ndarray		,			__snake_case			:     Union[int, float]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Optional[int]		,			)			->		List[Any]:
								return rescale(__snake_case		,			scale=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Optional[int]		,			__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			:     Any		,			)			->		np.ndarray:
								return normalize(__snake_case		,			mean=__snake_case		,			std=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     ImageInput		,			__snake_case			:     bool = None		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     Tuple=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			:     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			:     List[Any]		,			)			->		PIL.Image.Image:
								UpperCAmelCase :       List[Any] 							=		do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase :       Any 							=		resample if resample is not None else self.resample
								UpperCAmelCase :       Optional[int] 							=		do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase :       Optional[Any] 							=		do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase :       Optional[Any] 							=		rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase :       List[str] 							=		do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase :       List[Any] 							=		image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase :       Any 							=		image_std if image_std is not None else self.image_std

								UpperCAmelCase :       str 							=		size if size is not None else self.size
								UpperCAmelCase :       Tuple 							=		get_size_dict(__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase :       Optional[Any] 							=		get_size_dict(__snake_case		,			param_name='''crop_size'''		)

								UpperCAmelCase :       Any 							=		make_list_of_images(__snake_case		)

								if not valid_images(__snake_case		):
															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.
								UpperCAmelCase :       Any 							=		[to_numpy_array(__snake_case		) for image in images]

								if do_resize:
															UpperCAmelCase :       Optional[int] 							=		[self.resize(image=__snake_case		,			size=__snake_case		,			resample=__snake_case		) for image in images]

								if do_center_crop:
															UpperCAmelCase :       List[Any] 							=		[self.center_crop(image=__snake_case		,			size=__snake_case		) for image in images]

								if do_rescale:
															UpperCAmelCase :       Tuple 							=		[self.rescale(image=__snake_case		,			scale=__snake_case		) for image in images]

								if do_normalize:
															UpperCAmelCase :       Any 							=		[self.normalize(image=__snake_case		,			mean=__snake_case		,			std=__snake_case		) for image in images]

								UpperCAmelCase :       Optional[int] 							=		[to_channel_dimension_format(__snake_case		,			__snake_case		) for image in images]

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










"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase    :int     =					logging.get_logger(__name__)

_lowerCAmelCase    :Union[str, Any]     =					{
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''mgp-str'''





     def __init__(					self       ,	A=[3_2, 1_2_8]       ,	A=4       ,	A=3       ,	A=2_7       ,	A=3_8       ,	A=5_0_2_5_7       ,	A=3_0_5_2_2       ,	A=7_6_8       ,	A=1_2       ,	A=1_2       ,	A=4.0       ,	A=True       ,	A=False       ,	A=1E-5       ,	A=0.0       ,	A=0.0       ,	A=0.0       ,	A=False       ,	A=0.02       ,	**A       ,	)							->					Union[str, Any]:
         super().__init__(**A		)

         _UpperCAmelCase							:  Any											=    image_size
         _UpperCAmelCase							:  str											=    patch_size
         _UpperCAmelCase							:  Dict											=    num_channels
         _UpperCAmelCase							:  Dict											=    max_token_length
         _UpperCAmelCase							:  Optional[Any]											=    num_character_labels
         _UpperCAmelCase							:  int											=    num_bpe_labels
         _UpperCAmelCase							:  List[str]											=    num_wordpiece_labels
         _UpperCAmelCase							:  Optional[int]											=    hidden_size
         _UpperCAmelCase							:  Any											=    num_hidden_layers
         _UpperCAmelCase							:  List[Any]											=    num_attention_heads
         _UpperCAmelCase							:  List[Any]											=    mlp_ratio
         _UpperCAmelCase							:  List[str]											=    distilled
         _UpperCAmelCase							:  Optional[int]											=    layer_norm_eps
         _UpperCAmelCase							:  str											=    drop_rate
         _UpperCAmelCase							:  List[Any]											=    qkv_bias
         _UpperCAmelCase							:  List[str]											=    attn_drop_rate
         _UpperCAmelCase							:  Dict											=    drop_path_rate
         _UpperCAmelCase							:  Union[str, Any]											=    output_aa_attentions
         _UpperCAmelCase							:  List[str]											=    initializer_range


