



"""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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


_lowerCAmelCase    :str     =					logging.get_logger(__name__)





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  int											=    b.T
    _UpperCAmelCase							:  Optional[int]											=    np.sum(np.square(UpperCamelCase__							)     ,       axis=1							)
    _UpperCAmelCase							:  Any											=    np.sum(np.square(UpperCamelCase__							)     ,       axis=0							)
    _UpperCAmelCase							:  Any											=    np.matmul(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    aa[:, None] - 2 * ab + ba[None, :]
    return d





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		Any							):
    _UpperCAmelCase							:  Union[str, Any]											=    x.reshape(-1     ,       3							)
    _UpperCAmelCase							:  Optional[Any]											=    squared_euclidean_distance(UpperCamelCase__     ,       UpperCamelCase__							)
    return np.argmin(UpperCamelCase__     ,       axis=1							)


class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=['''pixel_values''']





     def __init__(					self       ,	A = None       ,	A = True       ,	A = None       ,	A = PILImageResampling.BILINEAR       ,	A = True       ,	A = True       ,	**A       ,	)							->					None:
         super().__init__(**A		)
         _UpperCAmelCase							:  List[Any]											=    size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
         _UpperCAmelCase							:  Any											=    get_size_dict(A		)
         _UpperCAmelCase							:  Union[str, Any]											=    np.array(A		) if clusters is not None else None
         _UpperCAmelCase							:  str											=    do_resize
         _UpperCAmelCase							:  Tuple											=    size
         _UpperCAmelCase							:  Any											=    resample
         _UpperCAmelCase							:  Dict											=    do_normalize
         _UpperCAmelCase							:  Optional[int]											=    do_color_quantize





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A = PILImageResampling.BILINEAR       ,	A = None       ,	**A       ,	)							->					np.ndarray:
         _UpperCAmelCase							:  int											=    get_size_dict(A		)
         if "height" not in size or "width" not in size:
             raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'		)
         return resize(
             A       ,	size=(size['''height'''], size['''width'''])       ,	resample=A       ,	data_format=A       ,	**A		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None       ,	)							->					np.ndarray:
         _UpperCAmelCase							:  Optional[Any]											=    rescale(image=A       ,	scale=1 / 127.5       ,	data_format=A		)
         _UpperCAmelCase							:  List[str]											=    image - 1
         return image





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	A = ChannelDimension.FIRST       ,	**A       ,	)							->					PIL.Image.Image:
         _UpperCAmelCase							:  Tuple											=    do_resize if do_resize is not None else self.do_resize
         _UpperCAmelCase							:  List[Any]											=    size if size is not None else self.size
         _UpperCAmelCase							:  Union[str, Any]											=    get_size_dict(A		)
         _UpperCAmelCase							:  List[Any]											=    resample if resample is not None else self.resample
         _UpperCAmelCase							:  List[Any]											=    do_normalize if do_normalize is not None else self.do_normalize
         _UpperCAmelCase							:  Optional[int]											=    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         _UpperCAmelCase							:  Dict											=    clusters if clusters is not None else self.clusters
         _UpperCAmelCase							:  Union[str, Any]											=    np.array(A		)

         _UpperCAmelCase							:  str											=    make_list_of_images(A		)

         if not valid_images(A		):
             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_color_quantize and clusters is None:
             raise ValueError('''Clusters must be specified if do_color_quantize is True.'''		)

         # All transformations expect numpy arrays.
         _UpperCAmelCase							:  Tuple											=    [to_numpy_array(A		) for image in images]

         if do_resize:
             _UpperCAmelCase							:  Dict											=    [self.resize(image=A       ,	size=A       ,	resample=A		) for image in images]

         if do_normalize:
             _UpperCAmelCase							:  str											=    [self.normalize(image=A		) for image in images]

         if do_color_quantize:
             _UpperCAmelCase							:  Optional[int]											=    [to_channel_dimension_format(A       ,	ChannelDimension.LAST		) for image in images]
             # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
             _UpperCAmelCase							:  int											=    np.array(A		)
             _UpperCAmelCase							:  Tuple											=    color_quantize(A       ,	A		).reshape(images.shape[:-1]		)

             # flatten to (batch_size, height*width)
             _UpperCAmelCase							:  List[Any]											=    images.shape[0]
             _UpperCAmelCase							:  int											=    images.reshape(A       ,	-1		)

             # We need to convert back to a list of images to keep consistent behaviour across processors.
             _UpperCAmelCase							:  int											=    list(A		)
         else:
             _UpperCAmelCase							:  List[str]											=    [to_channel_dimension_format(A       ,	A		) for image in images]

         _UpperCAmelCase							:  Optional[Any]											=    {'''input_ids''': images}
         return BatchFeature(data=A       ,	tensor_type=A		)







"""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


