



from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve

def       A_   (						__a  :					np.ndarray			,		__a  :					np.ndarray			,		__a  :					np.ndarray		):

				"""simple docstring"""
				a__					   =		np.stack((horizontal_flow, vertical_flow)			,		2		)

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

				# Find the locations outside of the original image
				a__					   =		(grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]		))
				a__					   =		0

				a__					   =		image[grid[:, :, 1], grid[:, :, 0]]

				# Set pixels at invalid locations to 0
				a__					   =		0

				return warped

def       A_   (						__a  :					np.ndarray			,		__a  :					np.ndarray			,		__a  :					SupportsIndex			,		__a  :					float | None = None			,		):

				"""simple docstring"""
				if alpha is None:
								a__					   =		0.1

				# Initialize flow
				a__					   =		np.zeros_like(__a		)
				a__					   =		np.zeros_like(__a		)

				# Prepare kernels for the calculation of the derivatives and the average velocity
				a__					   =		np.array([[-1, 1], [-1, 1]]		) * 0.2_5
				a__					   =		np.array([[-1, -1], [1, 1]]		) * 0.2_5
				a__					   =		np.array([[1, 1], [1, 1]]		) * 0.2_5
				a__					   =		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(__a		):
								a__					   =		warp(__a			,		__a			,		__a		)
								a__					   =		convolve(__a			,		__a		) + convolve(__a			,		__a		)
								a__					   =		convolve(__a			,		__a		) + convolve(__a			,		__a		)
								a__					   =		convolve(__a			,		__a		) + convolve(__a			,		-kernel_t		)

								a__					   =		convolve(__a			,		__a		)
								a__					   =		convolve(__a			,		__a		)

								# This updates the flow as proposed in the paper (Step 12)
								a__					   =		(
								    derivative_x * avg_horizontal_velocity
								    + derivative_y * avg_vertical_velocity
								    + derivative_t
								)
								a__					   =		update / (alpha**2 + derivative_x**2 + derivative_y**2)

								a__					   =		avg_horizontal_velocity - derivative_x * update
								a__					   =		avg_vertical_velocity - derivative_y * update

				return horizontal_flow, vertical_flow


if __name__ == "__main__":
	import doctest

	doctest.testmod()









'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
					from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

					from .test_feature_extraction_speechta import floats_list


lowerCamelCase_					:			List[str]     						=				get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')
@require_torch
class  _SCREAMING_SNAKE_CASE				(		unittest.TestCase						):


			'''simple docstring'''





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

									'''simple docstring'''







									UpperCamelCase__       =			tempfile.mkdtemp()

									UpperCamelCase__       =			SpeechTaTokenizer(lowercase      )
									tokenizer.save_pretrained(self.tmpdirname      )

									UpperCamelCase__       =			{
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 1_6_0_0_0,
									    """do_normalize""": False,
									    """num_mel_bins""": 8_0,
									    """hop_length""": 1_6,
									    """win_length""": 6_4,
									    """win_function""": """hann_window""",
									    """fmin""": 8_0,
									    """fmax""": 7_6_0_0,
									    """mel_floor""": 1e-10,
									    """reduction_factor""": 2,
									    """return_attention_mask""": True,
									}

									UpperCamelCase__       =			os.path.join(self.tmpdirname			,   lowercase      )
									with open(self.feature_extraction_file			,   """w"""			,   encoding="""utf-8"""      ) as fp:
															fp.write(json.dumps(lowercase      ) + """\n"""      )






			def 					A   ( self				:    Union[str, Any]			,   **lowercase				:    List[Any]      )				->     Any:

									'''simple docstring'''







									return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,   **lowercase      )






			def 					A   ( self				:    str			,   **lowercase				:    Tuple      )				->     Optional[Any]:

									'''simple docstring'''







									return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,   **lowercase      )






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

									'''simple docstring'''







									shutil.rmtree(self.tmpdirname      )






			def 					A   ( self				:    List[Any]      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_tokenizer()
									UpperCamelCase__       =			self.get_feature_extractor()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									processor.save_pretrained(self.tmpdirname      )
									UpperCamelCase__       =			SpeechTaProcessor.from_pretrained(self.tmpdirname      )

									self.assertEqual(processor.tokenizer.get_vocab()			,   tokenizer.get_vocab()      )
									self.assertIsInstance(processor.tokenizer			,   lowercase      )

									self.assertEqual(processor.feature_extractor.to_json_string()			,   feature_extractor.to_json_string()      )
									self.assertIsInstance(processor.feature_extractor			,   lowercase      )






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

									'''simple docstring'''







									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=self.get_tokenizer()			,   feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

									UpperCamelCase__       =			self.get_tokenizer(bos_token="""(BOS)"""			,   eos_token="""(EOS)"""      )
									UpperCamelCase__       =			self.get_feature_extractor(do_normalize=lowercase			,   padding_value=1.0      )

									UpperCamelCase__       =			SpeechTaProcessor.from_pretrained(
									    self.tmpdirname			,   bos_token="""(BOS)"""			,   eos_token="""(EOS)"""			,   do_normalize=lowercase			,   padding_value=1.0      )

									self.assertEqual(processor.tokenizer.get_vocab()			,   tokenizer_add_kwargs.get_vocab()      )
									self.assertIsInstance(processor.tokenizer			,   lowercase      )

									self.assertEqual(processor.feature_extractor.to_json_string()			,   feature_extractor_add_kwargs.to_json_string()      )
									self.assertIsInstance(processor.feature_extractor			,   lowercase      )






			def 					A   ( self				:    List[Any]      )				->     Dict:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			floats_list((3, 1_0_0_0)      )

									UpperCamelCase__       =			feature_extractor(audio=lowercase			,   return_tensors="""np"""      )
									UpperCamelCase__       =			processor(audio=lowercase			,   return_tensors="""np"""      )

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()			,   input_processor[key].sum()			,   delta=1e-2      )






			def 					A   ( self				:    List[str]      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			floats_list((3, 1_0_0_0)      )

									UpperCamelCase__       =			feature_extractor(audio_target=lowercase			,   return_tensors="""np"""      )
									UpperCamelCase__       =			processor(audio_target=lowercase			,   return_tensors="""np"""      )

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()			,   input_processor[key].sum()			,   delta=1e-2      )






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

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			"""This is a test string"""

									UpperCamelCase__       =			processor(text=lowercase      )
									UpperCamelCase__       =			tokenizer(lowercase      )

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]			,   encoded_processor[key]      )






			def 					A   ( self				:    List[Any]      )				->     int:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			"""This is a test string"""

									UpperCamelCase__       =			processor(text_target=lowercase      )
									UpperCamelCase__       =			tokenizer(lowercase      )

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]			,   encoded_processor[key]      )






			def 					A   ( self				:    Any      )				->     Tuple:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									UpperCamelCase__       =			processor.batch_decode(lowercase      )
									UpperCamelCase__       =			tokenizer.batch_decode(lowercase      )

									self.assertListEqual(lowercase			,   lowercase      )







			def 					A   ( self				:    List[Any]      )				->     List[str]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									self.assertListEqual(
									    processor.model_input_names			,   feature_extractor.model_input_names			,   msg="""`processor` and `feature_extractor` model input names do not match"""			,   )
