


"""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


A     :   Optional[int]			    =			random.Random()

if is_torch_available():
	import torch


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=1.0    , _UpperCamelCase=None    , _UpperCamelCase=None      ):

			'''simple docstring'''




			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   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a=7    ,   __a=4_00    ,   __a=20_00    ,   __a=1    ,   __a=0.0    ,   __a=1_60_00    ,   __a=True    ,   __a=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    ,   __a=False    ,   __a=False	):
										def _flatten(__a	):
													return list(itertools.chain(*__a	)	)

										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(__a	) for x in speech_inputs]

										return speech_inputs
@require_torch
@require_torchaudio
class   _UpperCamelCase  (   lowerCAmelCase__       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    List[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(__a	) 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(__a    ,   __a    ,   atol=1e-3	)	)

										# Test batched
										__lowerCAmelCase				    =			feat_extract(__a    ,   padding=__a    ,   return_tensors="np"	).input_values
										__lowerCAmelCase				    =			feat_extract(__a    ,   padding=__a    ,   return_tensors="np"	).input_values
										for enc_seq_a, enc_seq_a in zip(__a    ,   __a	):
													self.assertTrue(np.allclose(__a    ,   __a    ,   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(__a	)
										__lowerCAmelCase				    =			feat_extract(__a    ,   return_tensors="np"	).input_values
										__lowerCAmelCase				    =			feat_extract(__a    ,   return_tensors="np"	).input_values
										for enc_seq_a, enc_seq_a in zip(__a    ,   __a	):
													self.assertTrue(np.allclose(__a    ,   __a    ,   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    ,   __a	):
										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(__a	)	)[: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(__a    ,   return_tensors="pt"	).input_values
										self.assertEquals(input_values.shape    ,   (1, 10_24, 1_28)	)
										self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,   __a    ,   atol=1e-4	)	)








"""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

A     :   Union[str, Any]			    =			imread(R"digital_image_processing/image_data/lena_small.jpg")
A     :   Optional[Any]			    =			cvtColor(img, COLOR_BGR2GRAY)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			cn.convert_to_negative(_UpperCamelCase      )
			# assert negative_img array for at least one True
			assert negative_img.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			with Image.open("digital_image_processing/image_data/lena_small.jpg"      ) as img:
						# Work around assertion for response
						assert str(cc.change_contrast(_UpperCamelCase    , 110      )      ).startswith(
						    "<PIL.Image.Image image mode=RGB size=100x100 at"      )


def _lowerCamelCase	(		):

			'''simple docstring'''




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


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			imread("digital_image_processing/image_data/lena_small.jpg"    , 0      )
			# assert ambiguous array for all == True
			assert canny_img.all()
			__lowerCAmelCase				    =			canny.canny(_UpperCamelCase      )
			# assert canny array for at least one True
			assert canny_array.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert gg.gaussian_filter(_UpperCamelCase    , 5    , sigma=0.9      ).all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]      )
			__lowerCAmelCase				    =			conv.img_convolve(_UpperCamelCase    , _UpperCamelCase      ).astype(_UpperCamelCase      )
			assert res.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert med.median_filter(_UpperCamelCase    , 3      ).any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase    ,	__lowerCAmelCase				    =			sob.sobel_filter(_UpperCamelCase      )
			assert grad.any() and theta.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			sp.make_sepia(_UpperCamelCase    , 20      )
			assert sepia.all()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			bs.Burkes(imread(_UpperCamelCase    , 1      )    , 120      )
			burkes.process()
			assert burkes.output_img.any()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    , ):

			'''simple docstring'''




			__lowerCAmelCase				    =			rs.NearestNeighbour(imread(_UpperCamelCase    , 1      )    , 400    , 200      )
			nn.process()
			assert nn.output.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"digital_image_processing/image_data/lena.jpg"

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

			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(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert lbp_image.any()




