






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


lowerCamelCase : Dict						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     ( lowercase_			):

					'''simple docstring'''

					_A						:			Dict									=      ['''pixel_values''']



					def __init__(  self				:	Dict		,     __a				:	str = True		,     __a				:	str = None		,     __a				:	List[Any] = PIL.Image.BICUBIC		,     __a				:	str = True		,     __a				:	List[Any] = None		,     __a				:	Tuple = 1 / 255		,     __a				:	int = True		,     __a				:	Dict = True		,     __a				:	str = None		,     __a				:	Dict = None		,     **__a				:	Union[str, Any]		,     )			->  Optional[int]:


												"""simple docstring"""

												super().__init__(**__a							)
												__lowercase      :							Union[str, Any]				=     size if size is not None else {"""height""": 256, """width""": 256}
												__lowercase      :							Tuple				=     get_size_dict(__a							)
												__lowercase      :							Optional[int]				=     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
												__lowercase      :							List[str]				=     get_size_dict(__a		,     param_name="""crop_size"""							)

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



					def       lowerCAmelCase				(  self				:	str		,     __a				:	int		,     __a				:	Optional[Any]		,     __a				:	List[Any] = PIL.Image.BICUBIC		,     __a				:	Optional[int] = None		,     **__a				:	List[Any]		,     )			->  Any:


												"""simple docstring"""

												__lowercase      :							str				=     get_size_dict(__a							)
												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(
												    __a		,     size=(size["""height"""], size["""width"""])		,     resample=__a		,     data_format=__a		,     **__a							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Dict		,     __a				:	int		,     __a				:	Optional[Any] = None		,     **__a				:	int		,     )			->  str:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     get_size_dict(__a							)
												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(__a		,     size=(size["""height"""], size["""width"""])		,     data_format=__a		,     **__a							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Optional[int]		,     __a				:	Optional[int]		,     __a				:	Tuple = None		,     **__a				:	List[str]		,     )			->  Union[str, Any]:


												"""simple docstring"""

												return rescale(__a		,     scale=__a		,     data_format=__a		,     **__a							)



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Dict		,     __a				:	Optional[int]		,     __a				:	List[Any]		,     __a				:	Tuple = None		,     **__a				:	Tuple		,     )			->  Union[str, Any]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Optional[int]		,     __a				:	Optional[int] = None		,     __a				:	Any = None		,     __a				:	int=None		,     __a				:	int = None		,     __a				:	Optional[int] = None		,     __a				:	Union[str, Any] = None		,     __a				:	Dict = None		,     __a				:	List[Any] = None		,     __a				:	List[Any] = None		,     __a				:	Optional[Any] = None		,     __a				:	Union[str, Any] = None		,     __a				:	Any = ChannelDimension.FIRST		,     **__a				:	Dict		,     )			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							Tuple				=     do_resize if do_resize is not None else self.do_resize
												__lowercase      :							str				=     resample if resample is not None else self.resample
												__lowercase      :							Tuple				=     do_center_crop if do_center_crop is not None else self.do_center_crop
												__lowercase      :							List[Any]				=     do_rescale if do_rescale is not None else self.do_rescale
												__lowercase      :							Tuple				=     rescale_factor if rescale_factor is not None else self.rescale_factor
												__lowercase      :							Dict				=     do_normalize if do_normalize is not None else self.do_normalize
												__lowercase      :							List[str]				=     image_mean if image_mean is not None else self.image_mean
												__lowercase      :							str				=     image_std if image_std is not None else self.image_std

												__lowercase      :							Dict				=     size if size is not None else self.size
												__lowercase      :							str				=     get_size_dict(__a							)
												__lowercase      :							Optional[int]				=     crop_size if crop_size is not None else self.crop_size
												__lowercase      :							Any				=     get_size_dict(__a		,     param_name="""crop_size"""							)

												__lowercase      :							Dict				=     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_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.
												__lowercase      :							Optional[int]				=     [to_numpy_array(__a							) for image in images]

												if do_resize:
																			__lowercase      :							int				=     [self.resize(image=__a		,     size=__a		,     resample=__a							) for image in images]

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

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

												if do_normalize:
																			__lowercase      :							Dict				=     [self.normalize(image=__a		,     mean=__a		,     std=__a							) for image in images]

												__lowercase      :							Tuple				=     [to_channel_dimension_format(__a		,     __a							) for image in images]

												__lowercase      :							Union[str, Any]				=     {"""pixel_values""": images}
												return BatchFeature(data=__a		,     tensor_type=__a							)













from torch import nn

class 		lowerCAmelCase     ( nn.Module			):

					'''simple docstring'''

					def __init__(  self				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]							)			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__()
												__lowercase      :							int				=     class_size
												__lowercase      :							int				=     embed_size
												# self.mlp1 = nn.Linear(embed_size, embed_size)
												# self.mlp2 = (nn.Linear(embed_size, class_size))
												__lowercase      :							str				=     nn.Linear(__a		,     __a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	int							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							str				=     self.mlp(__a							)
												return logits