
"""simple docstring"""





from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Union[str, Any]     	=       np.stack((horizontal_flow, vertical_flow),						2    )

   # Create a grid of all pixel coordinates and subtract the flow to get the
   # target pixels coordinates
   UpperCAmelCase_   :   Union[str, Any]     	=       np.stack(
       np.meshgrid(np.arange(0,						image.shape[1]    ),						np.arange(0,						image.shape[0]    )    ),						2    )
   UpperCAmelCase_   :   Optional[int]     	=       np.round(grid - flow    ).astype(np.intaa    )

   # Find the locations outside of the original image
   UpperCAmelCase_   :   Optional[int]     	=       (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]    ))
   UpperCAmelCase_   :   List[Any]     	=       0

   UpperCAmelCase_   :   Union[str, Any]     	=       image[grid[:, :, 1], grid[:, :, 0]]

   # Set pixels at invalid locations to 0
   UpperCAmelCase_   :   Union[str, Any]     	=       0

   return warped







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase = None,						):
   if alpha is None:
      UpperCAmelCase_   :   int     	=       0.1

   # Initialize flow
   UpperCAmelCase_   :   Union[str, Any]     	=       np.zeros_like(__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       np.zeros_like(__lowerCamelCase    )

   # Prepare kernels for the calculation of the derivatives and the average velocity
   UpperCAmelCase_   :   Optional[int]     	=       np.array([[-1, 1], [-1, 1]]    ) * 0.25
   UpperCAmelCase_   :   List[str]     	=       np.array([[-1, -1], [1, 1]]    ) * 0.25
   UpperCAmelCase_   :   Dict     	=       np.array([[1, 1], [1, 1]]    ) * 0.25
   UpperCAmelCase_   :   List[Any]     	=       np.array(
       [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]    )

   # Iteratively refine the flow
   for _ in range(__lowerCamelCase    ):
      UpperCAmelCase_   :   Tuple     	=       warp(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
      UpperCAmelCase_   :   Tuple     	=       convolve(__lowerCamelCase,						__lowerCamelCase    ) + convolve(__lowerCamelCase,						__lowerCamelCase    )
      UpperCAmelCase_   :   Any     	=       convolve(__lowerCamelCase,						__lowerCamelCase    ) + convolve(__lowerCamelCase,						__lowerCamelCase    )
      UpperCAmelCase_   :   Optional[Any]     	=       convolve(__lowerCamelCase,						__lowerCamelCase    ) + convolve(__lowerCamelCase,						-kernel_t    )

      UpperCAmelCase_   :   List[Any]     	=       convolve(__lowerCamelCase,						__lowerCamelCase    )
      UpperCAmelCase_   :   Union[str, Any]     	=       convolve(__lowerCamelCase,						__lowerCamelCase    )

      # This updates the flow as proposed in the paper (Step 12)
      UpperCAmelCase_   :   Optional[int]     	=       (
          derivative_x * avg_horizontal_velocity
          + derivative_y * avg_vertical_velocity
          + derivative_t
      )
      UpperCAmelCase_   :   List[Any]     	=       update / (alpha**2 + derivative_x**2 + derivative_y**2)

      UpperCAmelCase_   :   Optional[Any]     	=       avg_horizontal_velocity - derivative_x * update
      UpperCAmelCase_   :   List[Any]     	=       avg_vertical_velocity - derivative_y * update

   return horizontal_flow, vertical_flow


if __name__ == "__main__":
    import doctest

    doctest.testmod()






"""simple docstring"""







import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE					,       unittest.TestCase			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   SpeechaTextaTokenizer
							SCREAMING_SNAKE_CASE              =   False






							def        lowerCAmelCase  (    self			:  Dict					):
													super().setUp()

													UpperCAmelCase__												=     '''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''					)
													UpperCAmelCase__												=     ['''he re</w> 123''', '''here a 1456''']
													UpperCAmelCase__												=     dict(zip(lowerCamelCase_		,			range(len(lowerCamelCase_					)					)					)					)

													UpperCAmelCase__												=     {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

													UpperCAmelCase__												=     tempfile.mkdtemp()
													UpperCAmelCase__												=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['''vocab_file''']					)
													UpperCAmelCase__												=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['''merges_file''']					)

													with open(self.vocab_file		,			'''w'''		,			encoding='''utf-8'''					) as fp:
																			fp.write(json.dumps(lowerCamelCase_					) + '''\n'''					)

													with open(self.merges_file		,			'''w'''					) as fp:
																			fp.write('''\n'''.join(lowerCamelCase_					)					)






							def        lowerCAmelCase  (    self			:  Union[str, Any]					):
													UpperCAmelCase__												=     list(self.get_tokenizer().get_vocab().keys()					)

													self.assertEqual(vocab_keys[0]		,			'''<s>'''					)
													self.assertEqual(vocab_keys[1]		,			'''<pad>'''					)
													self.assertEqual(vocab_keys[-1]		,			'''vocab'''					)
													self.assertEqual(len(lowerCamelCase_					)		,			14					)






							def        lowerCAmelCase  (    self			:  Optional[Any]					):
													self.assertEqual(self.get_tokenizer().vocab_size		,			14					)






							def        lowerCAmelCase  (    self			:  Any					):
													UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

													# make sure @@ is correctly concatenated
													UpperCAmelCase__												=     [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
													UpperCAmelCase__												=     tokenizer.decode(lowerCamelCase_					)

													self.assertTrue(output_string == '''herecouple words ofthe'''					)






							def        lowerCAmelCase  (    self			:  int					):
													UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			tokenizer.save_pretrained(lowerCamelCase_					)
																			os.remove(os.path.join(lowerCamelCase_		,			'''merges.txt'''					)					)

																			# load tokenizer without merges file should not throw an error
																			UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_					)

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			# save tokenizer and load again
																			tokenizer.save_pretrained(lowerCamelCase_					)
																			UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_					)

													self.assertIsNotNone(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  int					):
													if not self.test_slow_tokenizer:
																			return

													UpperCAmelCase__												=     inspect.signature(self.tokenizer_class.__init__					)
													UpperCAmelCase__												=     self.get_tokenizer()

													for parameter_name, parameter in signature.parameters.items():
																			if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																									self.assertIn(lowerCamelCase_		,			tokenizer.init_kwargs					)