





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


__A    :   Dict          =       get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")

@require_torch
class      A_	(unittest.TestCase      ):
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    tempfile.mkdtemp()

				UpperCAmelCase       		=    SpeechTaTokenizer(_A		)
				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							, _A		)
				with open(self.feature_extraction_file							, '''w'''							, encoding='''utf-8'''		) as fp:
							fp.write(json.dumps(_A		) + '''\n'''		)
	def 					_lowercase    (   self							, **_A		):






				'''simple docstring'''







				return SpeechTaTokenizer.from_pretrained(self.tmpdirname							, **_A		)
	def 					_lowercase    (   self							, **_A		):






				'''simple docstring'''







				return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname							, **_A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				shutil.rmtree(self.tmpdirname		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.get_tokenizer()
				UpperCAmelCase       		=    self.get_feature_extractor()

				UpperCAmelCase       		=    SpeechTaProcessor(tokenizer=_A							, feature_extractor=_A		)

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

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

				self.assertEqual(processor.feature_extractor.to_json_string()							, feature_extractor.to_json_string()		)
				self.assertIsInstance(processor.feature_extractor							, _A		)
	def 					_lowercase    (   self		):






				'''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=_A							, padding_value=1.0		)

				UpperCAmelCase       		=    SpeechTaProcessor.from_pretrained(
				    self.tmpdirname							, bos_token='''(BOS)'''							, eos_token='''(EOS)'''							, do_normalize=_A							, padding_value=1.0		)

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

				self.assertEqual(processor.feature_extractor.to_json_string()							, feature_extractor_add_kwargs.to_json_string()		)
				self.assertIsInstance(processor.feature_extractor							, _A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.get_feature_extractor()
				UpperCAmelCase       		=    self.get_tokenizer()

				UpperCAmelCase       		=    SpeechTaProcessor(tokenizer=_A							, feature_extractor=_A		)

				UpperCAmelCase       		=    floats_list((3, 1_0_0_0)		)

				UpperCAmelCase       		=    feature_extractor(audio=_A							, return_tensors='''np'''		)
				UpperCAmelCase       		=    processor(audio=_A							, 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 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.get_feature_extractor()
				UpperCAmelCase       		=    self.get_tokenizer()

				UpperCAmelCase       		=    SpeechTaProcessor(tokenizer=_A							, feature_extractor=_A		)

				UpperCAmelCase       		=    floats_list((3, 1_0_0_0)		)

				UpperCAmelCase       		=    feature_extractor(audio_target=_A							, return_tensors='''np'''		)
				UpperCAmelCase       		=    processor(audio_target=_A							, 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 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.get_feature_extractor()
				UpperCAmelCase       		=    self.get_tokenizer()

				UpperCAmelCase       		=    SpeechTaProcessor(tokenizer=_A							, feature_extractor=_A		)

				UpperCAmelCase       		=    '''This is a test string'''

				UpperCAmelCase       		=    processor(text=_A		)
				UpperCAmelCase       		=    tokenizer(_A		)

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






				'''simple docstring'''







				UpperCAmelCase       		=    self.get_feature_extractor()
				UpperCAmelCase       		=    self.get_tokenizer()

				UpperCAmelCase       		=    SpeechTaProcessor(tokenizer=_A							, feature_extractor=_A		)

				UpperCAmelCase       		=    '''This is a test string'''

				UpperCAmelCase       		=    processor(text_target=_A		)
				UpperCAmelCase       		=    tokenizer(_A		)

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






				'''simple docstring'''







				UpperCAmelCase       		=    self.get_feature_extractor()
				UpperCAmelCase       		=    self.get_tokenizer()

				UpperCAmelCase       		=    SpeechTaProcessor(tokenizer=_A							, feature_extractor=_A		)

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

				UpperCAmelCase       		=    processor.batch_decode(_A		)
				UpperCAmelCase       		=    tokenizer.batch_decode(_A		)

				self.assertListEqual(_A							, _A		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.get_feature_extractor()
				UpperCAmelCase       		=    self.get_tokenizer()

				UpperCAmelCase       		=    SpeechTaProcessor(tokenizer=_A							, feature_extractor=_A		)

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










from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    np.ndarray:







			'''simple docstring'''

			UpperCAmelCase       		=    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       		=    np.stack(
			    np.meshgrid(np.arange(0      ,      image.shape[1]			)      ,      np.arange(0      ,      image.shape[0]			)			)      ,      2			)
			UpperCAmelCase       		=    np.round(grid - flow			).astype(np.intaa			)

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

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

			# Set pixels at invalid locations to 0
			UpperCAmelCase       		=    0

			return warped





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__ = None      ,      )  ->    tuple[np.ndarray, np.ndarray]:







			'''simple docstring'''

			if alpha is None:
						UpperCAmelCase       		=    0.1

			# Initialize flow
			UpperCAmelCase       		=    np.zeros_like(UpperCamelCase__			)
			UpperCAmelCase       		=    np.zeros_like(UpperCamelCase__			)

			# Prepare kernels for the calculation of the derivatives and the average velocity
			UpperCAmelCase       		=    np.array([[-1, 1], [-1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array([[-1, -1], [1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array([[1, 1], [1, 1]]			) * 0.25
			UpperCAmelCase       		=    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(UpperCamelCase__			):
						UpperCAmelCase       		=    warp(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      -kernel_t			)

						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)

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

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

			return horizontal_flow, vertical_flow


if __name__ == "__main__":
				import doctest

				doctest.testmod()



