



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     _A       (	lowercase_    ):
       UpperCamelCase__  :    Optional[Any]											=						['''pixel_values''']



       def __init__(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		__SCREAMING_SNAKE_CASE							:     Optional[Dict[str, int]] = None     ,		__SCREAMING_SNAKE_CASE							:     PILImageResampling = PILImageResampling.BICUBIC     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		__SCREAMING_SNAKE_CASE							:     Union[int, float] = 1 / 255     ,		__SCREAMING_SNAKE_CASE							:     Dict[str, int] = None     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[float, List[float]]] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[float, List[float]]] = None     ,		**__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		):


           '''simple docstring'''

           super().__init__(**__UpperCamelCase)
           __a						       =				size if size is not None else {'''height''': 224, '''width''': 224}
           __a						       =				get_size_dict(__UpperCamelCase)
           __a						       =				crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           __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     ,		__SCREAMING_SNAKE_CASE							:     np.ndarray     ,		__SCREAMING_SNAKE_CASE							:     Dict[str, int]     ,		__SCREAMING_SNAKE_CASE							:     PILImageResampling = PILImageResampling.BILINEAR     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[str, ChannelDimension]] = None     ,		**__SCREAMING_SNAKE_CASE							:     int     ,		):


           '''simple docstring'''

           __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     ,		__SCREAMING_SNAKE_CASE							:     np.ndarray     ,		__SCREAMING_SNAKE_CASE							:     Dict[str, int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[str, ChannelDimension]] = None     ,		**__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		):


           '''simple docstring'''

           __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]     ,		__SCREAMING_SNAKE_CASE							:     np.ndarray     ,		__SCREAMING_SNAKE_CASE							:     float     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[str, ChannelDimension]] = None     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     np.ndarray     ,		__SCREAMING_SNAKE_CASE							:     Union[float, List[float]]     ,		__SCREAMING_SNAKE_CASE							:     Union[float, List[float]]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[str, ChannelDimension]] = None     ,		**__SCREAMING_SNAKE_CASE							:     Dict     ,		):


           '''simple docstring'''

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





       def 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     ImageInput     ,		__SCREAMING_SNAKE_CASE							:     Optional[bool] = None     ,		__SCREAMING_SNAKE_CASE							:     Dict[str, int] = None     ,		__SCREAMING_SNAKE_CASE							:     PILImageResampling = None     ,		__SCREAMING_SNAKE_CASE							:     bool = None     ,		__SCREAMING_SNAKE_CASE							:     int = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[bool] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[float] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[bool] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[float, List[float]]] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[float, List[float]]] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[str, TensorType]] = None     ,		__SCREAMING_SNAKE_CASE							:     Union[str, ChannelDimension] = ChannelDimension.FIRST     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]     ,		):


           '''simple docstring'''

           __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)		)


