


'''simple docstring'''

import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

_UpperCAmelCase				: Optional[int]							    =				imread(r"""digital_image_processing/image_data/lena_small.jpg""")
_UpperCAmelCase				: Tuple							    =				cvtColor(img, COLOR_BGR2GRAY)

def 							__magic_name__(						):
      __lowerCAmelCase							       =				cn.convert_to_negative(__lowercase)
      # assert negative_img array for at least one True
      assert negative_img.any()

def 							__magic_name__(						):




      with Image.open('''digital_image_processing/image_data/lena_small.jpg''') as img:
            # Work around assertion for response
            assert str(cc.change_contrast(__lowercase,				1_1_0)).startswith(
                '''<PIL.Image.Image image mode=RGB size=100x100 at''')

def 							__magic_name__(						):
      __lowerCAmelCase							       =				canny.gen_gaussian_kernel(9,				sigma=1.4)
      # Assert ambiguous array
      assert resp.all()

def 							__magic_name__(						):
      __lowerCAmelCase							       =				imread('''digital_image_processing/image_data/lena_small.jpg''',				0)
      # assert ambiguous array for all == True
      assert canny_img.all()
      __lowerCAmelCase							       =				canny.canny(__lowercase)
      # assert canny array for at least one True
      assert canny_array.any()

def 							__magic_name__(						):
      assert gg.gaussian_filter(__lowercase,				5,				sigma=0.9).all()

def 							__magic_name__(						):
      __lowerCAmelCase							       =				array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]])
      __lowerCAmelCase							       =				conv.img_convolve(__lowercase,				__lowercase).astype(__lowercase)
      assert res.any()

def 							__magic_name__(						):
      assert med.median_filter(__lowercase,				3).any()

def 							__magic_name__(						):
      __lowerCAmelCase							       =				sob.sobel_filter(__lowercase)
      assert grad.any() and theta.any()

def 							__magic_name__(						):
      __lowerCAmelCase							       =				sp.make_sepia(__lowercase,				2_0)
      assert sepia.all()

def 							__magic_name__(						lowerCamelCase = "digital_image_processing/image_data/lena_small.jpg"):
      __lowerCAmelCase							       =				bs.Burkes(imread(__lowercase,				1),				1_2_0)
      burkes.process()
      assert burkes.output_img.any()

def 							__magic_name__(						lowerCamelCase = "digital_image_processing/image_data/lena_small.jpg",				):
      __lowerCAmelCase							       =				rs.NearestNeighbour(imread(__lowercase,				1),				4_0_0,				2_0_0)
      nn.process()
      assert nn.output.any()




def 							__magic_name__(						):
      __lowerCAmelCase							       =				'digital_image_processing/image_data/lena.jpg'

      # Reading the image and converting it to grayscale.
      __lowerCAmelCase							       =				imread(__lowercase,				0)

      # Test for get_neighbors_pixel function() return not None
      __lowerCAmelCase							       =				0
      __lowerCAmelCase							       =				0
      __lowerCAmelCase							       =				image[x_coordinate][y_coordinate]

      __lowerCAmelCase							       =				lbp.get_neighbors_pixel(
          __lowercase,				__lowercase,				__lowercase,				__lowercase)

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      __lowerCAmelCase							       =				np.zeros((image.shape[0], image.shape[1]))

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0,				image.shape[0]):
            for j in range(0,				image.shape[1]):
                  __lowerCAmelCase							       =				lbp.local_binary_value(__lowercase,				__lowercase,				__lowercase)

      assert lbp_image.any()





'''simple docstring'''

import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_UpperCAmelCase				: Tuple							    =				random.Random()

if is_torch_available():
	import torch

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=1.0,				lowerCamelCase=None,				lowerCamelCase=None):
						if rng is None:
												__lowerCAmelCase							       =				global_rng

						__lowerCAmelCase							       =				[]
						for batch_idx in range(shape[0]):
												values.append([])
												for _ in range(shape[1]):
																		values[-1].append(rng.random() * scale)

						return values


class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase=7       ,			__lowercase=4_00       ,			__lowercase=20_00       ,			__lowercase=1       ,			__lowercase=0.0       ,			__lowercase=1_60_00       ,			__lowercase=True       ,			__lowercase=True       ,			):
										__lowerCAmelCase							       =				parent
										__lowerCAmelCase							       =				batch_size
										__lowerCAmelCase							       =				min_seq_length
										__lowerCAmelCase							       =				max_seq_length
										__lowerCAmelCase							       =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										__lowerCAmelCase							       =				feature_size
										__lowerCAmelCase							       =				padding_value
										__lowerCAmelCase							       =				sampling_rate
										__lowerCAmelCase							       =				return_attention_mask
										__lowerCAmelCase							       =				do_normalize




				def 					_snake_case  (self		):
										return {
										    "feature_size": self.feature_size,
										    "padding_value": self.padding_value,
										    "sampling_rate": self.sampling_rate,
										    "return_attention_mask": self.return_attention_mask,
										    "do_normalize": self.do_normalize,
										}




				def 					_snake_case  (self       ,			__lowercase=False       ,			__lowercase=False		):
										def _flatten(__lowercase		):
																return list(itertools.chain(*__lowercase		)		)

										if equal_length:
																__lowerCAmelCase							       =				floats_list((self.batch_size, self.max_seq_length)		)
										else:
																# make sure that inputs increase in size
																__lowerCAmelCase							       =				[
																    _flatten(floats_list((x, self.feature_size)		)		)
																    for x in range(self.min_seq_length       ,			self.max_seq_length       ,			self.seq_length_diff		)
																]

										if numpify:
																__lowerCAmelCase							       =				[np.asarray(__lowercase		) for x in speech_inputs]

										return speech_inputs


@require_torch
@require_torchaudio
class       a__				( __A							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		Union[str, Any]	=	ASTFeatureExtractor




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				ASTFeatureExtractionTester(self		)




				def 					_snake_case  (self		):
										# Tests that all call wrap to encode_plus and batch_encode_plus
										__lowerCAmelCase							       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
										# create three inputs of length 800, 1000, and 1200
										__lowerCAmelCase							       =				[floats_list((1, x)		)[0] for x in range(8_00       ,			14_00       ,			2_00		)]
										__lowerCAmelCase							       =				[np.asarray(__lowercase		) for speech_input in speech_inputs]

										# Test not batched input
										__lowerCAmelCase							       =				feat_extract(speech_inputs[0]       ,			return_tensors='''np'''		).input_values
										__lowerCAmelCase							       =				feat_extract(np_speech_inputs[0]       ,			return_tensors='''np'''		).input_values
										self.assertTrue(np.allclose(__lowercase       ,			__lowercase       ,			atol=1e-3		)		)

										# Test batched
										__lowerCAmelCase							       =				feat_extract(__lowercase       ,			padding=__lowercase       ,			return_tensors='''np'''		).input_values
										__lowerCAmelCase							       =				feat_extract(__lowercase       ,			padding=__lowercase       ,			return_tensors='''np'''		).input_values
										for enc_seq_a, enc_seq_a in zip(__lowercase       ,			__lowercase		):
																self.assertTrue(np.allclose(__lowercase       ,			__lowercase       ,			atol=1e-3		)		)

										# Test 2-D numpy arrays are batched.
										__lowerCAmelCase							       =				[floats_list((1, x)		)[0] for x in (8_00, 8_00, 8_00)]
										__lowerCAmelCase							       =				np.asarray(__lowercase		)
										__lowerCAmelCase							       =				feat_extract(__lowercase       ,			return_tensors='''np'''		).input_values
										__lowerCAmelCase							       =				feat_extract(__lowercase       ,			return_tensors='''np'''		).input_values
										for enc_seq_a, enc_seq_a in zip(__lowercase       ,			__lowercase		):
																self.assertTrue(np.allclose(__lowercase       ,			__lowercase       ,			atol=1e-3		)		)




				@require_torch
				def 					_snake_case  (self		):
										import torch

										__lowerCAmelCase							       =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
										__lowerCAmelCase							       =				np.random.rand(1_00		).astype(np.floataa		)
										__lowerCAmelCase							       =				np_speech_inputs.tolist()

										for inputs in [py_speech_inputs, np_speech_inputs]:
																__lowerCAmelCase							       =				feature_extractor.pad([{'''input_values''': inputs}]       ,			return_tensors='''np'''		)
																self.assertTrue(np_processed.input_values.dtype == np.floataa		)
																__lowerCAmelCase							       =				feature_extractor.pad([{'''input_values''': inputs}]       ,			return_tensors='''pt'''		)
																self.assertTrue(pt_processed.input_values.dtype == torch.floataa		)




				def 					_snake_case  (self       ,			__lowercase		):
										from datasets import load_dataset

										__lowerCAmelCase							       =				load_dataset('''hf-internal-testing/librispeech_asr_dummy'''       ,			'''clean'''       ,			split='''validation'''		)
										# automatic decoding with librispeech
										__lowerCAmelCase							       =				ds.sort('''id'''		).select(range(__lowercase		)		)[:num_samples]['''audio''']

										return [x["array"] for x in speech_samples]




				@require_torch
				def 					_snake_case  (self		):
										# fmt: off
										__lowerCAmelCase							       =				torch.tensor(
										    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
										     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
										     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
										     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]		)
										# fmt: on

										__lowerCAmelCase							       =				self._load_datasamples(1		)
										__lowerCAmelCase							       =				ASTFeatureExtractor()
										__lowerCAmelCase							       =				feature_extractor(__lowercase       ,			return_tensors='''pt'''		).input_values
										self.assertEquals(input_values.shape       ,			(1, 10_24, 1_28)		)
										self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,			__lowercase       ,			atol=1e-4		)		)

