


'''simple docstring'''






import unittest

import numpy as np
import requests

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

 from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
 lowercase							=False

if is_vision_available():
 from PIL import Image

 from transformers import PixaStructImageProcessor






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=None  ,							snake_case=True  ,							snake_case=True  ,							snake_case=None  ,							)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =size if size is not None else {'height': 2_0, 'width': 2_0}
          _UpperCAmelCase       :       List[Any]							   =parent
          _UpperCAmelCase       :       Dict							   =batch_size
          _UpperCAmelCase       :       Optional[int]							   =num_channels
          _UpperCAmelCase       :       Union[str, Any]							   =image_size
          _UpperCAmelCase       :       Union[str, Any]							   =min_resolution
          _UpperCAmelCase       :       Tuple							   =max_resolution
          _UpperCAmelCase       :       Optional[Any]							   =size
          _UpperCAmelCase       :       str							   =do_normalize
          _UpperCAmelCase       :       Optional[Any]							   =do_convert_rgb
          _UpperCAmelCase       :       Dict							   =[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
          _UpperCAmelCase       :       Optional[int]							   =patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}






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




          '''simple docstring'''
          return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






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




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   ='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
          _UpperCAmelCase       :       Dict							   =Image.open(requests.get(snake_case  ,							stream=snake_case).raw).convert('RGB')
          return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11 ,reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`." ,)
@require_torch
@require_vision
class  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=PixaStructImageProcessor if is_vision_available() else None






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




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =PixaStructImageProcessingTester(self)






       @property
       def       lowerCAmelCase		(      self)						->  Dict:




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






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case  ,							'do_normalize'))
          self.assertTrue(hasattr(snake_case  ,							'do_convert_rgb'))






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




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =self.image_processor_tester.prepare_dummy_image()

          _UpperCAmelCase       :       Union[str, Any]							   =self.image_processing_class(**self.image_processor_dict)
          _UpperCAmelCase       :       str							   =2_0_4_8

          _UpperCAmelCase       :       Dict							   =image_processor(snake_case  ,							return_tensors='pt'  ,							max_patches=snake_case)
          self.assertTrue(torch.allclose(inputs.flattened_patches.mean()  ,							torch.tensor(0.06_06)  ,							atol=1E-3  ,							rtol=1E-3))






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




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

          # Test not batched input
          _UpperCAmelCase       :       Any							   =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _UpperCAmelCase       :       Optional[int]							   =image_processor(
                 image_inputs[0]  ,							return_tensors='pt'  ,							max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape  ,							(1, max_patch, expected_hidden_dim)  ,							)

             # Test batched
             _UpperCAmelCase       :       Any							   =image_processor(
                 snake_case  ,							return_tensors='pt'  ,							max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape  ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,							)






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




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

          # Test not batched input
          _UpperCAmelCase       :       Union[str, Any]							   =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          _UpperCAmelCase       :       List[Any]							   =True

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             with self.assertRaises(snake_case):
                _UpperCAmelCase       :       List[Any]							   =image_processor(
                    image_inputs[0]  ,							return_tensors='pt'  ,							max_patches=snake_case).flattened_patches

             _UpperCAmelCase       :       Any							   ='Hello'

             _UpperCAmelCase       :       Optional[int]							   =image_processor(
                 image_inputs[0]  ,							return_tensors='pt'  ,							max_patches=snake_case  ,							header_text=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape  ,							(1, max_patch, expected_hidden_dim)  ,							)

             # Test batched
             _UpperCAmelCase       :       Any							   =image_processor(
                 snake_case  ,							return_tensors='pt'  ,							max_patches=snake_case  ,							header_text=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape  ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,							)






       def       lowerCAmelCase		(      self)						->  Any:




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

          _UpperCAmelCase       :       Dict							   =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _UpperCAmelCase       :       Dict							   =image_processor(
                 image_inputs[0]  ,							return_tensors='pt'  ,							max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape  ,							(1, max_patch, expected_hidden_dim)  ,							)

             # Test batched
             _UpperCAmelCase       :       Optional[Any]							   =image_processor(
                 snake_case  ,							return_tensors='pt'  ,							max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape  ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,							)






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




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

          # Test not batched input
          _UpperCAmelCase       :       str							   =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _UpperCAmelCase       :       Optional[int]							   =image_processor(
                 image_inputs[0]  ,							return_tensors='pt'  ,							max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape  ,							(1, max_patch, expected_hidden_dim)  ,							)

             # Test batched
             _UpperCAmelCase       :       List[str]							   =image_processor(
                 snake_case  ,							return_tensors='pt'  ,							max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape  ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,							)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11 ,reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`." ,)
@require_torch
@require_vision
class  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=PixaStructImageProcessor if is_vision_available() else None






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =PixaStructImageProcessingTester(self  ,							num_channels=4)
          _UpperCAmelCase       :       Dict							   =3






       @property
       def       lowerCAmelCase		(      self)						->  Tuple:




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






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case  ,							'do_normalize'))
          self.assertTrue(hasattr(snake_case  ,							'do_convert_rgb'))






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




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _UpperCAmelCase       :       Tuple							   =prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case  ,							Image.Image)

          # Test not batched input
          _UpperCAmelCase       :       Optional[Any]							   =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * (self.image_processor_tester.num_channels - 1)
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _UpperCAmelCase       :       Optional[Any]							   =image_processor(
                 image_inputs[0]  ,							return_tensors='pt'  ,							max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape  ,							(1, max_patch, expected_hidden_dim)  ,							)

             # Test batched
             _UpperCAmelCase       :       str							   =image_processor(
                 snake_case  ,							return_tensors='pt'  ,							max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape  ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,							)





'''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()

