


'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor






class  __magic_name__							(		unittest.TestCase    ):






       def __init__(      self  ,							snake_case  ,							snake_case=7  ,							snake_case=3  ,							snake_case=1_8  ,							snake_case=3_0  ,							snake_case=4_0_0  ,							snake_case=True  ,							snake_case=None  ,							snake_case=True  ,							snake_case=None  ,							snake_case=True  ,							snake_case=[0.5, 0.5, 0.5]  ,							snake_case=[0.5, 0.5, 0.5]  ,							)						->  List[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =size if size is not None else {"shortest_edge": 1_8}
          _UpperCAmelCase       :       Tuple							   =crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
          _UpperCAmelCase       :       Tuple							   =parent
          _UpperCAmelCase       :       Optional[int]							   =batch_size
          _UpperCAmelCase       :       Optional[Any]							   =num_channels
          _UpperCAmelCase       :       List[Any]							   =image_size
          _UpperCAmelCase       :       Union[str, Any]							   =min_resolution
          _UpperCAmelCase       :       Dict							   =max_resolution
          _UpperCAmelCase       :       Dict							   =do_resize
          _UpperCAmelCase       :       Optional[int]							   =size
          _UpperCAmelCase       :       Any							   =do_center_crop
          _UpperCAmelCase       :       str							   =crop_size
          _UpperCAmelCase       :       List[Any]							   =do_normalize
          _UpperCAmelCase       :       Optional[Any]							   =image_mean
          _UpperCAmelCase       :       List[Any]							   =image_std






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "do_center_crop": self.do_center_crop,
              "size": self.size,
              "crop_size": self.crop_size,
          }






@require_torch
@require_vision
class  __magic_name__							(		lowercase__ ,unittest.TestCase    ):
       UpperCAmelCase										=LevitImageProcessor if is_vision_available() else None






       def       lowerCAmelCase		(      self)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =LevitImageProcessingTester(self)






       @property
       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          return self.image_processor_tester.prepare_image_processor_dict()






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(lowercase_  ,							'image_mean'))
          self.assertTrue(hasattr(lowercase_  ,							'image_std'))
          self.assertTrue(hasattr(lowercase_  ,							'do_normalize'))
          self.assertTrue(hasattr(lowercase_  ,							'do_resize'))
          self.assertTrue(hasattr(lowercase_  ,							'do_center_crop'))
          self.assertTrue(hasattr(lowercase_  ,							'size'))






       def       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size  ,							{'shortest_edge': 1_8})
          self.assertEqual(image_processor.crop_size  ,							{'height': 1_8, 'width': 1_8})

          _UpperCAmelCase       :       Tuple							   =self.image_processing_class.from_dict(self.image_processor_dict  ,							size=4_2  ,							crop_size=8_4)
          self.assertEqual(image_processor.size  ,							{'shortest_edge': 4_2})
          self.assertEqual(image_processor.crop_size  ,							{'height': 8_4, 'width': 8_4})






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          pass






       def       lowerCAmelCase		(      self)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _UpperCAmelCase       :       Union[str, Any]							   =prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=lowercase_)
          for image in image_inputs:
             self.assertIsInstance(lowercase_  ,							Image.Image)

          # Test not batched input
          _UpperCAmelCase       :       Optional[Any]							   =image_processing(image_inputs[0]  ,							return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape  ,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)

          # Test batched
          _UpperCAmelCase       :       str							   =image_processing(lowercase_  ,							return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape  ,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _UpperCAmelCase       :       Optional[Any]							   =prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=lowercase_  ,							numpify=lowercase_)
          for image in image_inputs:
             self.assertIsInstance(lowercase_  ,							np.ndarray)

          # Test not batched input
          _UpperCAmelCase       :       Tuple							   =image_processing(image_inputs[0]  ,							return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape  ,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)

          # Test batched
          _UpperCAmelCase       :       int							   =image_processing(lowercase_  ,							return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape  ,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _UpperCAmelCase       :       Union[str, Any]							   =prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=lowercase_  ,							torchify=lowercase_)
          for image in image_inputs:
             self.assertIsInstance(lowercase_  ,							torch.Tensor)

          # Test not batched input
          _UpperCAmelCase       :       Any							   =image_processing(image_inputs[0]  ,							return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape  ,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)

          # Test batched
          _UpperCAmelCase       :       Dict							   =image_processing(lowercase_  ,							return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape  ,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)





'''simple docstring'''






from typing import Any





def lowerCamelCase__      (						__lowerCamelCase	:							list	,   __lowerCamelCase	:							list	,   __lowerCamelCase	:							dict	,   __lowerCamelCase	:							dict	,   __lowerCamelCase	:							dict	,   ):







   '''simple docstring'''




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

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

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

         _UpperCAmelCase       :       List[Any]							   =arg_max

    # The final observation
   _UpperCAmelCase       :       int							   =observations_space[len(__lowerCamelCase							) - 1]

   # argmax for given final observation
   _UpperCAmelCase       :       Any							   =''
   _UpperCAmelCase       :       Union[str, Any]							   =-1
   for k_state in states_space:
      _UpperCAmelCase       :       Optional[int]							   =probabilities[(k_state, final_observation)]
      if probability > max_probability:
         _UpperCAmelCase       :       Union[str, Any]							   =probability
         _UpperCAmelCase       :       int							   =k_state
   _UpperCAmelCase       :       int							   =arg_max

   # Process pointers backwards
   _UpperCAmelCase       :       List[str]							   =last_state
   _UpperCAmelCase       :       Optional[int]							   =[]
   for o in range(len(__lowerCamelCase							) - 1	,   -1	,   -1							):
      result.append(__lowerCamelCase							)
      _UpperCAmelCase       :       Optional[Any]							   =pointers[previous, observations_space[o]]
   result.reverse()

   return result





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   ):







   '''simple docstring'''




   _validate_not_empty(
       __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   )
   _validate_lists(__lowerCamelCase	,   __lowerCamelCase							)
   _validate_dicts(
       __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   ):







   '''simple docstring'''




   if not all(
       [
           observations_space,
           states_space,
           initial_probabilities,
           transition_probabilities,
           emission_probabilities,
       ]							):
      raise ValueError('There\'s an empty parameter'							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any							):







   '''simple docstring'''




   _validate_list(__lowerCamelCase	,   'observations_space'							)
   _validate_list(__lowerCamelCase	,   'states_space'							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							str							):







   '''simple docstring'''




   if not isinstance(_object	,   __lowerCamelCase							):
      _UpperCAmelCase       :       Any							   =f"{var_name} must be a list"
      raise ValueError(__lowerCamelCase							)
   else:
      for x in _object:
         if not isinstance(__lowerCamelCase	,   __lowerCamelCase							):
            _UpperCAmelCase       :       Optional[int]							   =f"{var_name} must be a list of strings"
            raise ValueError(__lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   ):







   '''simple docstring'''




   _validate_dict(__lowerCamelCase	,   'initial_probabilities'	,   __lowerCamelCase							)
   _validate_nested_dict(__lowerCamelCase	,   'transition_probabilities'							)
   _validate_nested_dict(__lowerCamelCase	,   'emission_probabilities'							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							str							):







   '''simple docstring'''




   _validate_dict(_object	,   __lowerCamelCase	,   __lowerCamelCase							)
   for x in _object.values():
      _validate_dict(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							type	,   __lowerCamelCase	:							bool = False							):







   '''simple docstring'''




   if not isinstance(_object	,   __lowerCamelCase							):
      _UpperCAmelCase       :       List[str]							   =f"{var_name} must be a dict"
      raise ValueError(__lowerCamelCase							)
   if not all(isinstance(__lowerCamelCase	,   __lowerCamelCase							) for x in _object							):
      _UpperCAmelCase       :       str							   =f"{var_name} all keys must be strings"
      raise ValueError(__lowerCamelCase							)
   if not all(isinstance(__lowerCamelCase	,   __lowerCamelCase							) for x in _object.values()							):
      _UpperCAmelCase       :       int							   ='nested dictionary ' if nested else ''
      _UpperCAmelCase       :       Optional[int]							   =f"{var_name} {nested_text}all values must be {value_type.__name__}"
      raise ValueError(__lowerCamelCase							)


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

 testmod()

