





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


__A     =    logging.get_logger(__name__)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      ["pixel_values"]

 def __init__(self    :       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 / 255	,			UpperCAmelCase_    :       Dict[str, int] = None	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       Optional[Union[float, List[float]]] = None	,			UpperCAmelCase_    :       Optional[Union[float, List[float]]] = None	,			**UpperCAmelCase_    :       Optional[int]	,			)	->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:				List[Any]        =size if size is not None else {"height": 224, "width": 224}
   lowerCamelCase__:				List[str]        =get_size_dict(UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =crop_size if crop_size is not None else {"height": 224, "width": 224}
   lowerCamelCase__:				str        =get_size_dict(UpperCAmelCase_	,			default_to_square=UpperCAmelCase_	,			param_name="crop_size")

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       np.ndarray	,			UpperCAmelCase_    :       Dict[str, int]	,			UpperCAmelCase_    :       PILImageResampling = PILImageResampling.BILINEAR	,			UpperCAmelCase_    :       Optional[Union[str, ChannelDimension]] = None	,			**UpperCAmelCase_    :       int	,			)	->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =get_size_dict(UpperCAmelCase_)

   if "shortest_edge" in size:
     lowerCamelCase__:				List[str]        =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:
     lowerCamelCase__:				List[str]        =(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 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       np.ndarray	,			UpperCAmelCase_    :       Dict[str, int]	,			UpperCAmelCase_    :       Optional[Union[str, ChannelDimension]] = None	,			**UpperCAmelCase_    :       Union[str, Any]	,			)	->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =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 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       np.ndarray	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       Optional[Union[str, ChannelDimension]] = None	,			**UpperCAmelCase_    :       Optional[Any])	->np.ndarray:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       np.ndarray	,			UpperCAmelCase_    :       Union[float, List[float]]	,			UpperCAmelCase_    :       Union[float, List[float]]	,			UpperCAmelCase_    :       Optional[Union[str, ChannelDimension]] = None	,			**UpperCAmelCase_    :       Optional[int]	,			)	->np.ndarray:



   '''simple docstring'''



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







 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			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_    :       Tuple	,			)	->BatchFeature:



   '''simple docstring'''



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

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

   if not is_batched(UpperCAmelCase_):
     lowerCamelCase__:				Optional[Any]        =[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.
   lowerCamelCase__:				str        =[to_numpy_array(UpperCAmelCase_) for image in images]

   if do_resize:
     lowerCamelCase__:				Tuple        =[self.resize(image=UpperCAmelCase_	,			size=UpperCAmelCase_	,			resample=UpperCAmelCase_) for image in images]

   if do_center_crop:
     lowerCamelCase__:				Dict        =[self.center_crop(image=UpperCAmelCase_	,			size=UpperCAmelCase_) for image in images]

   if do_rescale:
     lowerCamelCase__:				List[Any]        =[self.rescale(image=UpperCAmelCase_	,			scale=UpperCAmelCase_) for image in images]

   if do_normalize:
     lowerCamelCase__:				Union[str, Any]        =[self.normalize(image=UpperCAmelCase_	,			mean=UpperCAmelCase_	,			std=UpperCAmelCase_) for image in images]

   lowerCamelCase__:				int        =[to_channel_dimension_format(UpperCAmelCase_	,			UpperCAmelCase_) for image in images]

   lowerCamelCase__:				int        ={"pixel_values": images}
   return BatchFeature(data=UpperCAmelCase_	,			tensor_type=UpperCAmelCase_)








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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def __init__(self    :       int	,			UpperCAmelCase_    :       Distribution	,			UpperCAmelCase_    :       Dict=None	,			UpperCAmelCase_    :       Tuple=None	,			UpperCAmelCase_    :       Tuple=0)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =1.0 if scale is None else scale
   lowerCamelCase__:				List[Any]        =0.0 if loc is None else loc

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

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->int:



   '''simple docstring'''



   return self.base_dist.mean * self.scale + self.loc

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Dict:



   '''simple docstring'''



   return self.base_dist.variance * self.scale**2







 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Optional[Any]:



   '''simple docstring'''



   return self.variance.sqrt()



class        _SCREAMING_SNAKE_CASE						(     nn.Module   ):







 '''simple docstring'''

 def __init__(self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Dict[str, int]	,			UpperCAmelCase_    :       Callable[..., Tuple[torch.Tensor]]	,			**UpperCAmelCase_    :       Dict)	->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =args_dim
   lowerCamelCase__:				Any        =nn.ModuleList([nn.Linear(UpperCAmelCase_	,			UpperCAmelCase_) for dim in args_dim.values()])
   lowerCamelCase__:				Any        =domain_map







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       torch.Tensor)	->Tuple[torch.Tensor]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =[proj(UpperCAmelCase_) for proj in self.proj]

   return self.domain_map(*UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						(     nn.Module   ):







 '''simple docstring'''

 def __init__(self    :       str	,			UpperCAmelCase_    :       Optional[Any])	->Optional[int]:



   '''simple docstring'''



   super().__init__()
   lowerCamelCase__:				Any        =function







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Optional[Any]	,			*UpperCAmelCase_    :       Union[str, Any])	->Union[str, Any]:



   '''simple docstring'''



   return self.function(UpperCAmelCase_	,			*UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      42

 def __init__(self    :       str	,			UpperCAmelCase_    :       int = 1)	->None:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =dim
   lowerCamelCase__:				int        ={k: dim * self.args_dim[k] for k in self.args_dim}

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       str)	->Optional[Any]:



   '''simple docstring'''



   if self.dim == 1:
     return self.distribution_class(*UpperCAmelCase_)
   else:
     return Independent(self.distribution_class(*UpperCAmelCase_)	,			1)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =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 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Tuple:



   '''simple docstring'''



   return () if self.dim == 1 else (self.dim,)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->int:



   '''simple docstring'''



   return len(self.event_shape)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->float:



   '''simple docstring'''



   return 0.0

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       int)	->nn.Module:



   '''simple docstring'''



   return ParameterProjection(
       in_features=UpperCAmelCase_	,			args_dim=self.args_dim	,			domain_map=LambdaLayer(self.domain_map)	,			)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			*UpperCAmelCase_    :       torch.Tensor)	->Any:



   '''simple docstring'''



   raise NotImplementedError()







 @staticmethod
 def 							SCREAMING_SNAKE_CASE_ (UpperCAmelCase_    :       torch.Tensor)	->torch.Tensor:



   '''simple docstring'''



   return (x + torch.sqrt(torch.square(UpperCAmelCase_) + 4.0)) / 2.0



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"df": 1, "loc": 1, "scale": 1}
 lowercase_		=      StudentT

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[int]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				str        =cls.squareplus(UpperCAmelCase_).clamp_min(torch.finfo(scale.dtype).eps)
   lowerCamelCase__:				Any        =2.0 + cls.squareplus(UpperCAmelCase_)
   return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"loc": 1, "scale": 1}
 lowercase_		=      Normal

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[Any]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =cls.squareplus(UpperCAmelCase_).clamp_min(torch.finfo(scale.dtype).eps)
   return loc.squeeze(-1), scale.squeeze(-1)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"total_count": 1, "logits": 1}
 lowercase_		=      NegativeBinomial

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[int]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =cls.squareplus(UpperCAmelCase_)
   return total_count.squeeze(-1), logits.squeeze(-1)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       Tuple)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Any        =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 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =distr_args

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

   return self._base_distribution((total_count, logits))

