






"""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 						__lowerCamelCase							(					snake_case__     ):





		'''simple docstring'''






		def __init__(  self				:      List[Any]		,     a_				:      Distribution		,     a_				:      Optional[Any]=None		,     a_				:      Tuple=None		,     a_				:      int=0      ):
				lowerCAmelCase_  :				Dict   							=     1.0 if scale is None else scale
				lowerCAmelCase_  :				Tuple   							=     0.0 if loc is None else loc

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





		@property
		def   lowerCamelCase			(  self				:      Union[str, Any]      ):
				return self.base_dist.mean * self.scale + self.loc





		@property
		def   lowerCamelCase			(  self				:      Dict      ):
				return self.base_dist.variance * self.scale**2





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







class 						__lowerCamelCase							(					nn.Module     ):





		'''simple docstring'''






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





		def   lowerCamelCase			(  self				:      Any		,     a_				:      torch.Tensor      ):
				lowerCAmelCase_  :				List[Any]   							=     [proj(__lowercase      ) for proj in self.proj]

				return self.domain_map(*__lowercase      )







class 						__lowerCamelCase							(					nn.Module     ):





		'''simple docstring'''






		def __init__(  self				:      str		,     a_				:      Optional[Any]      ):
				super().__init__()
				lowerCAmelCase_  :				Tuple   							=     function





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







class 						__lowerCamelCase							:





		'''simple docstring'''






		a_      :							type
		a_      :							int
		a_      :							Dict[str, int]





		def __init__(  self				:      Union[str, Any]		,     a_				:      int = 1      ):
				lowerCAmelCase_  :				Union[str, Any]   							=     dim
				lowerCAmelCase_  :				Tuple   							=     {k: dim * self.args_dim[k] for k in self.args_dim}





		def   lowerCamelCase			(  self				:      Optional[Any]		,     a_				:      Optional[Any]      ):
				if self.dim == 1:
						return self.distribution_class(*__lowercase      )
				else:
						return Independent(self.distribution_class(*__lowercase      )		,     1      )





		def   lowerCamelCase			(  self				:      Any		,     a_				:      Optional[int]		,     a_				:      Optional[torch.Tensor] = None		,     a_				:      Optional[torch.Tensor] = None		,     ):
				lowerCAmelCase_  :				Tuple   							=     self._base_distribution(__lowercase      )
				if loc is None and scale is None:
						return distr
				else:
						return AffineTransformed(__lowercase		,     loc=__lowercase		,     scale=__lowercase		,     event_dim=self.event_dim      )





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





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





		@property
		def   lowerCamelCase			(  self				:      List[str]      ):
				return 0.0





		def   lowerCamelCase			(  self				:      List[str]		,     a_				:      int      ):
				return ParameterProjection(
				    in_features=__lowercase		,     args_dim=self.args_dim		,     domain_map=LambdaLayer(self.domain_map      )		,     )





		def   lowerCamelCase			(  self				:      Any		,     *a_				:      torch.Tensor      ):
				raise NotImplementedError()





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







class 						__lowerCamelCase							(					snake_case__     ):





		'''simple docstring'''






		a_      :							Dict[str, int]  =				{"df": 1, "loc": 1, "scale": 1}
		a_      :							type  =				StudentT





		@classmethod
		def   lowerCamelCase			(  cls				:      Dict		,     a_				:      torch.Tensor		,     a_				:      torch.Tensor		,     a_				:      torch.Tensor      ):
				lowerCAmelCase_  :				int   							=     cls.squareplus(__lowercase      ).clamp_min(torch.finfo(scale.dtype      ).eps      )
				lowerCAmelCase_  :				List[Any]   							=     2.0 + cls.squareplus(__lowercase      )
				return df.squeeze(-1      ), loc.squeeze(-1      ), scale.squeeze(-1      )







class 						__lowerCamelCase							(					snake_case__     ):





		'''simple docstring'''






		a_      :							Dict[str, int]  =				{"loc": 1, "scale": 1}
		a_      :							type  =				Normal





		@classmethod
		def   lowerCamelCase			(  cls				:      Union[str, Any]		,     a_				:      torch.Tensor		,     a_				:      torch.Tensor      ):
				lowerCAmelCase_  :				Any   							=     cls.squareplus(__lowercase      ).clamp_min(torch.finfo(scale.dtype      ).eps      )
				return loc.squeeze(-1      ), scale.squeeze(-1      )







class 						__lowerCamelCase							(					snake_case__     ):





		'''simple docstring'''






		a_      :							Dict[str, int]  =				{"total_count": 1, "logits": 1}
		a_      :							type  =				NegativeBinomial





		@classmethod
		def   lowerCamelCase			(  cls				:      Optional[int]		,     a_				:      torch.Tensor		,     a_				:      torch.Tensor      ):
				lowerCAmelCase_  :				int   							=     cls.squareplus(__lowercase      )
				return total_count.squeeze(-1      ), logits.squeeze(-1      )





		def   lowerCamelCase			(  self				:      Dict		,     a_				:      str      ):
				lowerCAmelCase_  :				Tuple   							=     distr_args
				if self.dim == 1:
						return self.distribution_class(total_count=__lowercase		,     logits=__lowercase      )
				else:
						return Independent(self.distribution_class(total_count=__lowercase		,     logits=__lowercase      )		,     1      )





		def   lowerCamelCase			(  self				:      int		,     a_				:      Tuple		,     a_				:      Optional[torch.Tensor] = None		,     a_				:      Optional[torch.Tensor] = None      ):
				lowerCAmelCase_  :				Tuple   							=     distr_args

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

				return self._base_distribution((total_count, logits)      )







"""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_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					      =   logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							str									=		["""pixel_values"""]




				def __init__(  self	:str    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = PILImageResampling.BILINEAR    ,     __lowercase	:bool = True    ,     __lowercase	:Union[int, float] = 1 / 255    ,     __lowercase	:bool = True    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     **__lowercase	:Tuple    ,     ):
											super().__init__(**__lowercase      )
											__lowerCamelCase   :   int       =size if size is not None else {'''shortest_edge''': 384}
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Any       =do_resize
											__lowerCamelCase   :   List[str]       =size
											# Default value set here for backwards compatibility where the value in config is None
											__lowerCamelCase   :   Tuple       =crop_pct if crop_pct is not None else 224 / 256
											__lowerCamelCase   :   Any       =resample
											__lowerCamelCase   :   List[str]       =do_rescale
											__lowerCamelCase   :   Dict       =rescale_factor
											__lowerCamelCase   :   Any       =do_normalize
											__lowerCamelCase   :   int       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase   :   List[Any]       =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        __lowercase	(  self	:Any    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:float    ,     __lowercase	:PILImageResampling = PILImageResampling.BICUBIC    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:int    ,     ):
											__lowerCamelCase   :   int       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											if "shortest_edge" not in size:
																		raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'      )
											__lowerCamelCase   :   Optional[int]       =size['''shortest_edge''']

											if shortest_edge < 384:
																		# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																		__lowerCamelCase   :   int       =int(shortest_edge / crop_pct      )
																		__lowerCamelCase   :   str       =get_resize_output_image_size(__lowercase    ,     size=__lowercase    ,     default_to_square=__lowercase      )
																		__lowerCamelCase   :   str       =resize(image=__lowercase    ,     size=__lowercase    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )
																		# then crop to (shortest_edge, shortest_edge)
																		return center_crop(image=__lowercase    ,     size=(shortest_edge, shortest_edge)    ,     data_format=__lowercase    ,     **__lowercase      )
											else:
																		# warping (no cropping) when evaluated at 384 or larger
																		return resize(
																		    __lowercase    ,     size=(shortest_edge, shortest_edge)    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




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




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




				def        __lowercase	(  self	:int    ,     __lowercase	:ImageInput    ,     __lowercase	:bool = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = None    ,     __lowercase	:bool = None    ,     __lowercase	:float = None    ,     __lowercase	:bool = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[str, TensorType]] = None    ,     __lowercase	:ChannelDimension = ChannelDimension.FIRST    ,     **__lowercase	:Optional[Any]    ,     ):
											__lowerCamelCase   :   Optional[Any]       =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase   :   int       =crop_pct if crop_pct is not None else self.crop_pct
											__lowerCamelCase   :   List[Any]       =resample if resample is not None else self.resample
											__lowerCamelCase   :   List[Any]       =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase   :   List[Any]       =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase   :   List[Any]       =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase   :   Optional[Any]       =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase   :   str       =image_std if image_std is not None else self.image_std

											__lowerCamelCase   :   str       =size if size is not None else self.size
											__lowerCamelCase   :   Optional[int]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Optional[int]       =make_list_of_images(__lowercase      )

											if not valid_images(__lowercase      ):
																		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_resize and size["shortest_edge"] < 384 and crop_pct is None:
																		raise ValueError('''crop_pct must be specified if size < 384.'''      )

											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.
											__lowerCamelCase   :   List[Any]       =[to_numpy_array(__lowercase      ) for image in images]

											if do_resize:
																		__lowerCamelCase   :   Union[str, Any]       =[self.resize(image=__lowercase    ,     size=__lowercase    ,     crop_pct=__lowercase    ,     resample=__lowercase      ) for image in images]

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

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

											__lowerCamelCase   :   Union[str, Any]       =[to_channel_dimension_format(__lowercase    ,     __lowercase      ) for image in images]

											__lowerCamelCase   :   Tuple       ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase    ,     tensor_type=__lowercase      )




