





"""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     :		str       						= logging.get_logger(__name__)






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							['''pixel_values''']


					def __init__(					self			:List[str]     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:Optional[Dict[str, int]] = None     ,       __UpperCamelCase			:PILImageResampling = PILImageResampling.BICUBIC     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:Union[int, float] = 1 / 2_55     ,       __UpperCamelCase			:Dict[str, int] = None     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:Optional[Union[float, List[float]]] = None     ,       __UpperCamelCase			:Optional[Union[float, List[float]]] = None     ,       **__UpperCamelCase			:Union[str, Any]     ,       ):
												super().__init__(**__UpperCamelCase		)
												A		=  size if size is not None else {"height": 2_24, "width": 2_24}
												A		=  get_size_dict(__UpperCamelCase		)
												A		=  crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
												A		=  get_size_dict(__UpperCamelCase     ,       default_to_square=__UpperCamelCase     ,       param_name="crop_size"		)

												A		=  do_resize
												A		=  do_rescale
												A		=  do_normalize
												A		=  do_center_crop
												A		=  crop_size
												A		=  size
												A		=  resample
												A		=  rescale_factor
												A		=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												A		=  image_std if image_std is not None else IMAGENET_DEFAULT_STD


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:np.ndarray     ,       __UpperCamelCase			:Dict[str, int]     ,       __UpperCamelCase			:PILImageResampling = PILImageResampling.BILINEAR     ,       __UpperCamelCase			:Optional[Union[str, ChannelDimension]] = None     ,       **__UpperCamelCase			:int     ,       ):
												A		=  get_size_dict(__UpperCamelCase		)

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


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:np.ndarray     ,       __UpperCamelCase			:Dict[str, int]     ,       __UpperCamelCase			:Optional[Union[str, ChannelDimension]] = None     ,       **__UpperCamelCase			:Optional[Any]     ,       ):
												A		=  get_size_dict(__UpperCamelCase		)
												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(__UpperCamelCase     ,       size=(size["height"], size["width"])     ,       data_format=__UpperCamelCase     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:np.ndarray     ,       __UpperCamelCase			:float     ,       __UpperCamelCase			:Optional[Union[str, ChannelDimension]] = None     ,       **__UpperCamelCase			:str		):
												return rescale(__UpperCamelCase     ,       scale=__UpperCamelCase     ,       data_format=__UpperCamelCase     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:np.ndarray     ,       __UpperCamelCase			:Union[float, List[float]]     ,       __UpperCamelCase			:Union[float, List[float]]     ,       __UpperCamelCase			:Optional[Union[str, ChannelDimension]] = None     ,       **__UpperCamelCase			:Dict     ,       ):
												return normalize(__UpperCamelCase     ,       mean=__UpperCamelCase     ,       std=__UpperCamelCase     ,       data_format=__UpperCamelCase     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:ImageInput     ,       __UpperCamelCase			:Optional[bool] = None     ,       __UpperCamelCase			:Dict[str, int] = None     ,       __UpperCamelCase			:PILImageResampling = None     ,       __UpperCamelCase			:bool = None     ,       __UpperCamelCase			:int = None     ,       __UpperCamelCase			:Optional[bool] = None     ,       __UpperCamelCase			:Optional[float] = None     ,       __UpperCamelCase			:Optional[bool] = None     ,       __UpperCamelCase			:Optional[Union[float, List[float]]] = None     ,       __UpperCamelCase			:Optional[Union[float, List[float]]] = None     ,       __UpperCamelCase			:Optional[Union[str, TensorType]] = None     ,       __UpperCamelCase			:Union[str, ChannelDimension] = ChannelDimension.FIRST     ,       **__UpperCamelCase			:List[Any]     ,       ):
												A		=  do_resize if do_resize is not None else self.do_resize
												A		=  do_rescale if do_rescale is not None else self.do_rescale
												A		=  do_normalize if do_normalize is not None else self.do_normalize
												A		=  do_center_crop if do_center_crop is not None else self.do_center_crop
												A		=  crop_size if crop_size is not None else self.crop_size
												A		=  get_size_dict(__UpperCamelCase     ,       param_name="crop_size"     ,       default_to_square=__UpperCamelCase		)
												A		=  resample if resample is not None else self.resample
												A		=  rescale_factor if rescale_factor is not None else self.rescale_factor
												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		)

												if not is_batched(__UpperCamelCase		):
																			A		=  [images]

												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."		)

												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.
												A		=  [to_numpy_array(__UpperCamelCase		) for image in images]

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

												if do_center_crop:
																			A		=  [self.center_crop(image=__UpperCamelCase     ,       size=__UpperCamelCase		) for image in images]

												if do_rescale:
																			A		=  [self.rescale(image=__UpperCamelCase     ,       scale=__UpperCamelCase		) for image in images]

												if do_normalize:
																			A		=  [self.normalize(image=__UpperCamelCase     ,       mean=__UpperCamelCase     ,       std=__UpperCamelCase		) for image in images]

												A		=  [to_channel_dimension_format(__UpperCamelCase     ,       __UpperCamelCase		) for image in images]

												A		=  {"pixel_values": images}
												return BatchFeature(data=__UpperCamelCase     ,       tensor_type=__UpperCamelCase		)









"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)






class 					_UpperCAmelCase   (	lowercase_ ):


					def __init__(					self			:int     ,       __UpperCamelCase			:Distribution     ,       __UpperCamelCase			:Dict=None     ,       __UpperCamelCase			:Optional[int]=None     ,       __UpperCamelCase			:List[str]=0		):
												A		=  1.0 if scale is None else scale
												A		=  0.0 if loc is None else loc

												super().__init__(__UpperCamelCase     ,       [AffineTransform(loc=self.loc     ,       scale=self.scale     ,       event_dim=__UpperCamelCase		)]		)


					@property
					def 	lowerCamelCase       (					self			:Any		):
												return self.base_dist.mean * self.scale + self.loc


					@property
					def 	lowerCamelCase       (					self			:Optional[int]		):
												return self.base_dist.variance * self.scale**2


					@property
					def 	lowerCamelCase       (					self			:Dict		):
												return self.variance.sqrt()







class 					_UpperCAmelCase   (	nn.Module ):


					def __init__(					self			:Dict     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:Dict[str, int]     ,       __UpperCamelCase			:Callable[..., Tuple[torch.Tensor]]     ,       **__UpperCamelCase			:str		):
												super().__init__(**__UpperCamelCase		)
												A		=  args_dim
												A		=  nn.ModuleList([nn.Linear(__UpperCamelCase     ,       __UpperCamelCase		) for dim in args_dim.values()]		)
												A		=  domain_map


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  [proj(__UpperCamelCase		) for proj in self.proj]

												return self.domain_map(*__UpperCamelCase		)







class 					_UpperCAmelCase   (	nn.Module ):


					def __init__(					self			:Dict     ,       __UpperCamelCase			:int		):
												super().__init__()
												A		=  function


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:Any     ,       *__UpperCamelCase			:Any		):
												return self.function(__UpperCamelCase     ,       *__UpperCamelCase		)







class 					_UpperCAmelCase   :
					UpperCamelCase      =							42
					UpperCamelCase      =							42
					UpperCamelCase      =							42


					def __init__(					self			:Any     ,       __UpperCamelCase			:int = 1		):
												A		=  dim
												A		=  {k: dim * self.args_dim[k] for k in self.args_dim}


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:Dict		):
												if self.dim == 1:
																			return self.distribution_class(*__UpperCamelCase		)
												else:
																			return Independent(self.distribution_class(*__UpperCamelCase		)     ,       1		)


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       ):
												A		=  self._base_distribution(__UpperCamelCase		)
												if loc is None and scale is None:
																			return distr
												else:
																			return AffineTransformed(__UpperCamelCase     ,       loc=__UpperCamelCase     ,       scale=__UpperCamelCase     ,       event_dim=self.event_dim		)


					@property
					def 	lowerCamelCase       (					self			:List[Any]		):
												return () if self.dim == 1 else (self.dim,)


					@property
					def 	lowerCamelCase       (					self			:Tuple		):
												return len(self.event_shape		)


					@property
					def 	lowerCamelCase       (					self			:int		):
												return 0.0


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:int		):
												return ParameterProjection(
												    in_features=__UpperCamelCase     ,       args_dim=self.args_dim     ,       domain_map=LambdaLayer(self.domain_map		)     ,       )


					def 	lowerCamelCase       (					self			:List[Any]     ,       *__UpperCamelCase			:torch.Tensor		):
												raise NotImplementedError()


					@staticmethod
					def 	lowerCamelCase       (					__UpperCamelCase			:torch.Tensor		):
												return (x + torch.sqrt(torch.square(__UpperCamelCase		) + 4.0		)) / 2.0







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"df": 1, "loc": 1, "scale": 1}
					UpperCamelCase      =							StudentT


					@classmethod
					def 	lowerCamelCase       (					cls			:List[str]     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
												A		=  2.0 + cls.squareplus(__UpperCamelCase		)
												return df.squeeze(-1		), loc.squeeze(-1		), scale.squeeze(-1		)







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"loc": 1, "scale": 1}
					UpperCamelCase      =							Normal


					@classmethod
					def 	lowerCamelCase       (					cls			:List[Any]     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
												return loc.squeeze(-1		), scale.squeeze(-1		)







class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							{"total_count": 1, "logits": 1}
					UpperCamelCase      =							NegativeBinomial


					@classmethod
					def 	lowerCamelCase       (					cls			:str     ,       __UpperCamelCase			:torch.Tensor     ,       __UpperCamelCase			:torch.Tensor		):
												A		=  cls.squareplus(__UpperCamelCase		)
												return total_count.squeeze(-1		), logits.squeeze(-1		)


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:List[str]		):
												A,	A		=  distr_args
												if self.dim == 1:
																			return self.distribution_class(total_count=__UpperCamelCase     ,       logits=__UpperCamelCase		)
												else:
																			return Independent(self.distribution_class(total_count=__UpperCamelCase     ,       logits=__UpperCamelCase		)     ,       1		)


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Optional[torch.Tensor] = None     ,       __UpperCamelCase			:Optional[torch.Tensor] = None		):
												A,	A		=  distr_args

												if scale is not None:
																			# See scaling property of Gamma.
																			logits += scale.log()

												return self._base_distribution((total_count, logits)		)


