
import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor




class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					size if size is not None else {'''height''': 2_0, '''width''': 2_0}
           UpperCamelCase__     : List[Any]											=					crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
           UpperCamelCase__     : Optional[Any]											=					parent
           UpperCamelCase__     : Optional[int]											=					batch_size
           UpperCamelCase__     : Tuple											=					num_channels
           UpperCamelCase__     : Optional[Any]											=					image_size
           UpperCamelCase__     : List[Any]											=					min_resolution
           UpperCamelCase__     : int											=					max_resolution
           UpperCamelCase__     : Union[str, Any]											=					do_resize
           UpperCamelCase__     : Dict											=					size
           UpperCamelCase__     : List[str]											=					do_center_crop
           UpperCamelCase__     : Dict											=					crop_size
           UpperCamelCase__     : Any											=					do_normalize
           UpperCamelCase__     : Tuple											=					image_mean
           UpperCamelCase__     : Tuple											=					image_std
           UpperCamelCase__     : Any											=					do_reduce_labels






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_reduce_labels": self.do_reduce_labels,
           }


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : List[Any]											=					load_dataset('''hf-internal-testing/fixtures_ade20k'''			,		split='''test'''		)

      UpperCamelCase__     : Tuple											=					Image.open(dataset[0]['''file''']		)
      UpperCamelCase__     : str											=					Image.open(dataset[1]['''file''']		)

      return image, map


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : str											=					load_dataset('''hf-internal-testing/fixtures_ade20k'''			,		split='''test'''		)

      UpperCamelCase__     : List[str]											=					Image.open(ds[0]['''file''']		)
      UpperCamelCase__     : str											=					Image.open(ds[1]['''file''']		)
      UpperCamelCase__     : str											=					Image.open(ds[2]['''file''']		)
      UpperCamelCase__     : Tuple											=					Image.open(ds[3]['''file''']		)

      return [imagea, imagea], [mapa, mapa]




@require_torch
@require_vision
class 					_lowerCamelCase			(   UpperCamelCase_						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	BeitImageProcessor if is_vision_available() else None






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					BeitImageProcessingTester(self	)






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           return self.image_processor_tester.prepare_image_processor_dict()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.image_processing_class(**self.image_processor_dict	)
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,    '''do_resize'''	)	)
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,    '''size'''	)	)
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,    '''do_center_crop'''	)	)
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,    '''center_crop'''	)	)
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,    '''do_normalize'''	)	)
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,    '''image_mean'''	)	)
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,    '''image_std'''	)	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.image_processing_class.from_dict(self.image_processor_dict	)
           self.assertEqual(image_processor.size    ,    {'''height''': 2_0, '''width''': 2_0}	)
           self.assertEqual(image_processor.crop_size    ,    {'''height''': 1_8, '''width''': 1_8}	)
           self.assertEqual(image_processor.do_reduce_labels    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Optional[int]											=					self.image_processing_class.from_dict(
               self.image_processor_dict    ,    size=4_2    ,    crop_size=8_4    ,    reduce_labels=__SCREAMING_SNAKE_CASE	)
           self.assertEqual(image_processor.size    ,    {'''height''': 4_2, '''width''': 4_2}	)
           self.assertEqual(image_processor.crop_size    ,    {'''height''': 8_4, '''width''': 8_4}	)
           self.assertEqual(image_processor.do_reduce_labels    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           pass






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




           """simple docstring"""







           UpperCamelCase__     : int											=					self.image_processing_class(**self.image_processor_dict	)
           # create random PIL images
           UpperCamelCase__     : Optional[Any]											=					prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=__SCREAMING_SNAKE_CASE	)
           for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,    Image.Image	)

           # Test not batched input
           UpperCamelCase__     : Union[str, 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__     : Optional[int]											=					image_processing(__SCREAMING_SNAKE_CASE    ,    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  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """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=__SCREAMING_SNAKE_CASE    ,    numpify=__SCREAMING_SNAKE_CASE	)
           for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,    np.ndarray	)

           # Test not batched input
           UpperCamelCase__     : Dict											=					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__     : List[Any]											=					image_processing(__SCREAMING_SNAKE_CASE    ,    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  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.image_processing_class(**self.image_processor_dict	)
           # create random PyTorch tensors
           UpperCamelCase__     : str											=					prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=__SCREAMING_SNAKE_CASE    ,    torchify=__SCREAMING_SNAKE_CASE	)
           for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,    torch.Tensor	)

           # Test not batched input
           UpperCamelCase__     : Optional[int]											=					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(__SCREAMING_SNAKE_CASE    ,    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  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					self.image_processing_class(**self.image_processor_dict	)
           # create random PyTorch tensors
           UpperCamelCase__     : Optional[int]											=					prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=__SCREAMING_SNAKE_CASE    ,    torchify=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					[]
           for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,    torch.Tensor	)
                 maps.append(torch.zeros(image.shape[-2:]	).long()	)

           # Test not batched input
           UpperCamelCase__     : str											=					image_processing(image_inputs[0]    ,    maps[0]    ,    return_tensors='''pt'''	)
           self.assertEqual(
               encoding['''pixel_values'''].shape    ,    (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )    ,    )
           self.assertEqual(
               encoding['''labels'''].shape    ,    (
                   1,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )    ,    )
           self.assertEqual(encoding['''labels'''].dtype    ,    torch.long	)
           self.assertTrue(encoding['''labels'''].min().item() >= 0	)
           self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5	)

           # Test batched
           UpperCamelCase__     : Dict											=					image_processing(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	)
           self.assertEqual(
               encoding['''pixel_values'''].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'''],
               )    ,    )
           self.assertEqual(
               encoding['''labels'''].shape    ,    (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )    ,    )
           self.assertEqual(encoding['''labels'''].dtype    ,    torch.long	)
           self.assertTrue(encoding['''labels'''].min().item() >= 0	)
           self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5	)

           # Test not batched input (PIL images)
           UpperCamelCase__					,UpperCamelCase__     : Any											=					prepare_semantic_single_inputs()

           UpperCamelCase__     : Union[str, Any]											=					image_processing(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	)
           self.assertEqual(
               encoding['''pixel_values'''].shape    ,    (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )    ,    )
           self.assertEqual(
               encoding['''labels'''].shape    ,    (
                   1,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )    ,    )
           self.assertEqual(encoding['''labels'''].dtype    ,    torch.long	)
           self.assertTrue(encoding['''labels'''].min().item() >= 0	)
           self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5	)

           # Test batched input (PIL images)
           UpperCamelCase__					,UpperCamelCase__     : Union[str, Any]											=					prepare_semantic_batch_inputs()

           UpperCamelCase__     : Any											=					image_processing(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	)
           self.assertEqual(
               encoding['''pixel_values'''].shape    ,    (
                   2,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )    ,    )
           self.assertEqual(
               encoding['''labels'''].shape    ,    (
                   2,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )    ,    )
           self.assertEqual(encoding['''labels'''].dtype    ,    torch.long	)
           self.assertTrue(encoding['''labels'''].min().item() >= 0	)
           self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.image_processing_class(**self.image_processor_dict	)

           # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
           UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					prepare_semantic_single_inputs()
           UpperCamelCase__     : Dict											=					image_processing(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	)
           self.assertTrue(encoding['''labels'''].min().item() >= 0	)
           self.assertTrue(encoding['''labels'''].max().item() <= 1_5_0	)

           UpperCamelCase__     : str											=					True
           UpperCamelCase__     : Union[str, Any]											=					image_processing(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''	)
           self.assertTrue(encoding['''labels'''].min().item() >= 0	)
           self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5	)




from typing import Any


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      _validation(
          UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		)
      # Creates data structures and fill initial step
      UpperCamelCase__     : dict											=					{}
      UpperCamelCase__     : dict											=					{}
      for state in states_space:
            UpperCamelCase__     : Optional[int]											=					observations_space[0]
            UpperCamelCase__     : Any											=					(
                initial_probabilities[state] * emission_probabilities[state][observation]
            )
            UpperCamelCase__     : Union[str, Any]											=					None

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

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

                  UpperCamelCase__     : Optional[Any]											=					arg_max

    # The final observation
      UpperCamelCase__     : List[str]											=					observations_space[len(UpperCamelCase__		) - 1]

      # argmax for given final observation
      UpperCamelCase__     : Dict											=					''''''
      UpperCamelCase__     : Tuple											=					-1
      for k_state in states_space:
            UpperCamelCase__     : Any											=					probabilities[(k_state, final_observation)]
            if probability > max_probability:
                  UpperCamelCase__     : List[str]											=					probability
                  UpperCamelCase__     : Tuple											=					k_state
      UpperCamelCase__     : Any											=					arg_max

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

      return result


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      _validate_not_empty(
          UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		)
      _validate_lists(UpperCamelCase__			,		UpperCamelCase__		)
      _validate_dicts(
          UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)


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


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      _validate_list(UpperCamelCase__			,		'''observations_space'''		)
      _validate_list(UpperCamelCase__			,		'''states_space'''		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      if not isinstance(_object			,		UpperCamelCase__		):
            UpperCamelCase__     : List[Any]											=					f'''{var_name} must be a list'''
            raise ValueError(UpperCamelCase__		)
      else:
            for x in _object:
                  if not isinstance(UpperCamelCase__			,		UpperCamelCase__		):
                        UpperCamelCase__     : List[Any]											=					f'''{var_name} must be a list of strings'''
                        raise ValueError(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      _validate_dict(UpperCamelCase__			,		'''initial_probabilities'''			,		UpperCamelCase__		)
      _validate_nested_dict(UpperCamelCase__			,		'''transition_probabilities'''		)
      _validate_nested_dict(UpperCamelCase__			,		'''emission_probabilities'''		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      _validate_dict(_object			,		UpperCamelCase__			,		UpperCamelCase__		)
      for x in _object.values():
            _validate_dict(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__ = False		):
      if not isinstance(_object			,		UpperCamelCase__		):
            UpperCamelCase__     : List[str]											=					f'''{var_name} must be a dict'''
            raise ValueError(UpperCamelCase__		)
      if not all(isinstance(UpperCamelCase__			,		UpperCamelCase__		) for x in _object		):
            UpperCamelCase__     : Dict											=					f'''{var_name} all keys must be strings'''
            raise ValueError(UpperCamelCase__		)
      if not all(isinstance(UpperCamelCase__			,		UpperCamelCase__		) for x in _object.values()		):
            UpperCamelCase__     : Optional[Any]											=					'''nested dictionary ''' if nested else ''''''
            UpperCamelCase__     : Optional[Any]											=					f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
            raise ValueError(UpperCamelCase__		)


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

    testmod()


