





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class    __SCREAMING_SNAKE_CASE			:







       def __init__(      self     :	Union[str, Any]		,					A     :	List[str]		,					A     :	str=sys.maxsize			)   ->str:
          lowerCamelCase__   :						str					   =							'''bilinear'''
          lowerCamelCase__   :						Tuple					   =							max_size
          lowerCamelCase__   :						Optional[Any]					   =							short_edge_length







       def __call__(      self     :	Optional[int]		,					A     :	List[Any]			)   ->int:
          lowerCamelCase__   :						Optional[int]					   =							[]
          for img in imgs:
             lowerCamelCase__   ,							lowerCamelCase__   :						str					   =							img.shape[:2]
             # later: provide list and randomly choose index for resize
             lowerCamelCase__   :						Union[str, Any]					   =							np.random.randint(self.short_edge_length[0]		,					self.short_edge_length[1] + 1			)
             if size == 0:
                return img
             lowerCamelCase__   :						List[str]					   =							size * 1.0 / min(A		,					A			)
             if h < w:
                lowerCamelCase__   ,							lowerCamelCase__   :						Any					   =							size, scale * w
             else:
                lowerCamelCase__   ,							lowerCamelCase__   :						List[Any]					   =							scale * h, size
             if max(A		,					A			) > self.max_size:
                lowerCamelCase__   :						List[Any]					   =							self.max_size * 1.0 / max(A		,					A			)
                lowerCamelCase__   :						List[str]					   =							newh * scale
                lowerCamelCase__   :						List[str]					   =							neww * scale
             lowerCamelCase__   :						Tuple					   =							int(neww + 0.5			)
             lowerCamelCase__   :						Dict					   =							int(newh + 0.5			)

             if img.dtype == np.uinta:
                lowerCamelCase__   :						Union[str, Any]					   =							Image.fromarray(A			)
                lowerCamelCase__   :						str					   =							pil_image.resize((neww, newh)		,					PILImageResampling.BILINEAR			)
                lowerCamelCase__   :						List[Any]					   =							np.asarray(A			)
             else:
                lowerCamelCase__   :						Union[str, Any]					   =							img.permute(2		,					0		,					1			).unsqueeze(0			)  # 3, 0, 1)  # hw(c) -> nchw
                lowerCamelCase__   :						Any					   =							nn.functional.interpolate(
                    A		,					(newh, neww)		,					mode=self.interp_method		,					align_corners=A			).squeeze(0			)
             img_augs.append(A			)

          return img_augs


class    __SCREAMING_SNAKE_CASE			:







       def __init__(      self     :	str		,					A     :	List[str]			)   ->Tuple:
          lowerCamelCase__   :						Union[str, Any]					   =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		,					cfg.INPUT.MAX_SIZE_TEST			)
          lowerCamelCase__   :						Tuple					   =							cfg.INPUT.FORMAT
          lowerCamelCase__   :						List[Any]					   =							cfg.SIZE_DIVISIBILITY
          lowerCamelCase__   :						Any					   =							cfg.PAD_VALUE
          lowerCamelCase__   :						List[str]					   =							cfg.INPUT.MAX_SIZE_TEST
          lowerCamelCase__   :						List[Any]					   =							cfg.MODEL.DEVICE
          lowerCamelCase__   :						Tuple					   =							torch.tensor(cfg.MODEL.PIXEL_STD			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)		,					1		,					1			)
          lowerCamelCase__   :						Optional[int]					   =							torch.tensor(cfg.MODEL.PIXEL_MEAN			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)		,					1		,					1			)
          lowerCamelCase__   :						List[str]					   =							lambda A			: (x - self.pixel_mean) / self.pixel_std







       def 						__lowerCamelCase    (      self     :	Dict		,					A     :	Union[str, Any]			)   ->List[str]:
          lowerCamelCase__   :						List[str]					   =							tuple(max(A			) for s in zip(*[img.shape for img in images]			)			)
          lowerCamelCase__   :						List[Any]					   =							[im.shape[-2:] for im in images]
          lowerCamelCase__   :						Union[str, Any]					   =							[
              nn.functional.pad(
                  A		,					[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,					value=self.pad_value		,					)
              for size, im in zip(A		,					A			)
          ]

          return torch.stack(A			), torch.tensor(A			)







       def __call__(      self     :	str		,					A     :	int		,					A     :	List[str]=False			)   ->str:
          with torch.no_grad():
             if not isinstance(A		,					A			):
                lowerCamelCase__   :						List[str]					   =							[images]
             if single_image:
                assert len(A			) == 1
             for i in range(len(A			)			):
                if isinstance(images[i]		,					torch.Tensor			):
                   images.insert(A		,					images.pop(A			).to(self.device			).float()			)
                elif not isinstance(images[i]		,					torch.Tensor			):
                   images.insert(
                       A		,					torch.as_tensor(img_tensorize(images.pop(A			)		,					input_format=self.input_format			)			)
                       .to(self.device			)
                       .float()		,					)
                      # resize smallest edge
             lowerCamelCase__   :						str					   =							torch.tensor([im.shape[:2] for im in images]			)
             lowerCamelCase__   :						str					   =							self.aug(A			)
             # transpose images and convert to torch tensors
             # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
             # now normalize before pad to avoid useless arithmetic
             lowerCamelCase__   :						Dict					   =							[self.normalizer(A			) for x in images]
             # now pad them to do the following operations
             lowerCamelCase__   ,							lowerCamelCase__   :						List[Any]					   =							self.pad(A			)
             # Normalize

             if self.size_divisibility > 0:
                raise NotImplementedError()
             # pad
             lowerCamelCase__   :						List[Any]					   =							torch.true_divide(A		,					A			)
             if single_image:
                return images[0], sizes[0], scales_yx[0]
             else:
                return images, sizes, scales_yx



def        _a						(    UpperCAmelCase			,  UpperCAmelCase     )				->				Tuple:







   """simple docstring"""
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes




def        _a						(    UpperCAmelCase			,  UpperCAmelCase     )				->				Optional[int]:







   """simple docstring"""
   assert torch.isfinite(__snake_case     ).all(), "Box tensor contains infinite or NaN!"
   lowerCamelCase__   ,							lowerCamelCase__   :						int					   =							box_size
   tensor[:, 0].clamp_(min=0			,  max=__snake_case     )
   tensor[:, 1].clamp_(min=0			,  max=__snake_case     )
   tensor[:, 2].clamp_(min=0			,  max=__snake_case     )
   tensor[:, 3].clamp_(min=0			,  max=__snake_case     )







from typing import Any
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			list:







 """simple docstring"""


 _validation(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)
 # Creates data structures and fill initial step
 _lowercase							      ={}
 _lowercase							      ={}
 for state in states_space:
  _lowercase							      =observations_space[0]
  _lowercase							      =(
      initial_probabilities[state] * emission_probabilities[state][observation]
  )
  _lowercase							      =None

 # Fills the data structure with the probabilities of
 # different transitions and pointers to previous states
 for o in range(1     ,						len(__snake_case						)						):
  _lowercase							      =observations_space[o]
  _lowercase							      =observations_space[o - 1]
  for state in states_space:
   # Calculates the argmax for probability function
   _lowercase							      =''''''
   _lowercase							      =-1
   for k_state in states_space:
    _lowercase							      =(
        probabilities[(k_state, prior_observation)]
        * transition_probabilities[k_state][state]
        * emission_probabilities[state][observation]
    )
    if probability > max_probability:
     _lowercase							      =probability
     _lowercase							      =k_state

            # Update probabilities and pointers dicts
   _lowercase							      =(
       probabilities[(arg_max, prior_observation)]
       * transition_probabilities[arg_max][state]
       * emission_probabilities[state][observation]
   )

   _lowercase							      =arg_max

    # The final observation
 _lowercase							      =observations_space[len(__snake_case						) - 1]

 # argmax for given final observation
 _lowercase							      =''''''
 _lowercase							      =-1
 for k_state in states_space:
  _lowercase							      =probabilities[(k_state, final_observation)]
  if probability > max_probability:
   _lowercase							      =probability
   _lowercase							      =k_state
 _lowercase							      =arg_max

 # Process pointers backwards
 _lowercase							      =last_state
 _lowercase							      =[]
 for o in range(len(__snake_case						) - 1     ,						-1     ,						-1						):
  result.append(__snake_case						)
  _lowercase							      =pointers[previous, observations_space[o]]
 result.reverse()

 return result
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 _validate_not_empty(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)
 _validate_lists(__snake_case     ,						__snake_case						)
 _validate_dicts(
     __snake_case     ,						__snake_case     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 if not all(
     [
         observations_space,
         states_space,
         initial_probabilities,
         transition_probabilities,
         emission_probabilities,
     ]						):
  raise ValueError('''There\'s an empty parameter'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 _validate_list(__snake_case     ,						'''observations_space'''						)
 _validate_list(__snake_case     ,						'''states_space'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 if not isinstance(_object     ,						__snake_case						):
  _lowercase							      =F"{var_name} must be a list"
  raise ValueError(__snake_case						)
 else:
  for x in _object:
   if not isinstance(__snake_case     ,						__snake_case						):
    _lowercase							      =F"{var_name} must be a list of strings"
    raise ValueError(__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 _validate_dict(__snake_case     ,						'''initial_probabilities'''     ,						__snake_case						)
 _validate_nested_dict(__snake_case     ,						'''transition_probabilities'''						)
 _validate_nested_dict(__snake_case     ,						'''emission_probabilities'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 _validate_dict(_object     ,						__snake_case     ,						__snake_case						)
 for x in _object.values():
  _validate_dict(__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case = False						)     ->			None:







 """simple docstring"""


 if not isinstance(_object     ,						__snake_case						):
  _lowercase							      =F"{var_name} must be a dict"
  raise ValueError(__snake_case						)
 if not all(isinstance(__snake_case     ,						__snake_case						) for x in _object						):
  _lowercase							      =F"{var_name} all keys must be strings"
  raise ValueError(__snake_case						)
 if not all(isinstance(__snake_case     ,						__snake_case						) for x in _object.values()						):
  _lowercase							      ='''nested dictionary ''' if nested else ''''''
  _lowercase							      =F"{var_name} {nested_text}all values must be {value_type.__name__}"
  raise ValueError(__snake_case						)


if __name__ == "__main__":
  from doctest import testmod

  testmod()

