




"""simple docstring"""





import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
							import torch


_lowercase						   =     random.Random()

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase=1.0   ,      lowerCamelCase=None   ,      lowerCamelCase=None		):
						'''simple docstring'''


						if rng is None:
												snake_case__							:       Union[str, Any]				      =       global_rng

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

						return values

@require_torch
class    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


				def __init__( self :  Union[str, Any]   ,SCREAMING_SNAKE_CASE_ :  Union[str, Any]   ,SCREAMING_SNAKE_CASE_ :  Optional[int]=7   ,SCREAMING_SNAKE_CASE_ :  Tuple=4_0_0   ,SCREAMING_SNAKE_CASE_ :  int=2_0_0_0   ,SCREAMING_SNAKE_CASE_ :  Tuple=1   ,SCREAMING_SNAKE_CASE_ :  Optional[int]=0.0   ,SCREAMING_SNAKE_CASE_ :  Optional[int]=2_4_0_0_0   ,SCREAMING_SNAKE_CASE_ :  Any=True   ,):
										snake_case__							:       Dict				      =       parent
										snake_case__							:       Dict				      =       batch_size
										snake_case__							:       Tuple				      =       min_seq_length
										snake_case__							:       Optional[Any]				      =       max_seq_length
										snake_case__							:       Any				      =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										snake_case__							:       Union[str, Any]				      =       feature_size
										snake_case__							:       List[Any]				      =       padding_value
										snake_case__							:       Dict				      =       sampling_rate
										snake_case__							:       List[str]				      =       return_attention_mask

				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										return {
										    "feature_size": self.feature_size,
										    "padding_value": self.padding_value,
										    "sampling_rate": self.sampling_rate,
										    "return_attention_mask": self.return_attention_mask,
										}





				def    _lowerCamelCase       ( self :  Union[str, Any]   ,SCREAMING_SNAKE_CASE_ :  Any=False   ,SCREAMING_SNAKE_CASE_ :  Dict=False   ):
										def _flatten(SCREAMING_SNAKE_CASE_ :  str   ):
																return list(itertools.chain(*SCREAMING_SNAKE_CASE_   )   )

										if equal_length:
																snake_case__							:       str				      =       floats_list((self.batch_size, self.max_seq_length)   )
										else:
																# make sure that inputs increase in size
																snake_case__							:       Any				      =       [
																    _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:
																snake_case__							:       int				      =       [np.asarray(SCREAMING_SNAKE_CASE_   ) for x in audio_inputs]

										return audio_inputs







@require_torch
class    __lowercase							(      snake_case_    , unittest.TestCase						):


				'''simple docstring'''


				_lowerCAmelCase						      =EncodecFeatureExtractor

				def    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       List[Any]				      =       EnCodecFeatureExtractionTester(self   )

				def    _lowerCamelCase       ( self :  Dict   ):
										# Tests that all call wrap to encode_plus and batch_encode_plus
										snake_case__							:       List[str]				      =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
										# create three inputs of length 800, 1000, and 1200
										snake_case__							:       Optional[int]				      =       [floats_list((1, x)   )[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0   )]
										snake_case__							:       Dict				      =       [np.asarray(SCREAMING_SNAKE_CASE_   ) for audio_input in audio_inputs]

										# Test not batched input
										snake_case__							:       Optional[Any]				      =       feat_extract(audio_inputs[0]   ,return_tensors='''np'''   ).input_values
										snake_case__							:       Dict				      =       feat_extract(np_audio_inputs[0]   ,return_tensors='''np'''   ).input_values
										self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,atol=1E-3   )   )

										# Test batched
										snake_case__							:       List[str]				      =       feat_extract(SCREAMING_SNAKE_CASE_   ,padding=SCREAMING_SNAKE_CASE_   ,return_tensors='''np'''   ).input_values
										snake_case__							:       Optional[Any]				      =       feat_extract(SCREAMING_SNAKE_CASE_   ,padding=SCREAMING_SNAKE_CASE_   ,return_tensors='''np'''   ).input_values
										for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ):
																self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,atol=1E-3   )   )

				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										snake_case__							:       Union[str, Any]				      =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
										snake_case__							:       int				      =       np.random.rand(1_0_0   ).astype(np.floataa   )
										snake_case__							:       Dict				      =       np_audio_inputs.tolist()

										for inputs in [py_audio_inputs, np_audio_inputs]:
																snake_case__							:       Union[str, Any]				      =       feature_extractor.pad([{'''input_values''': inputs}]   ,return_tensors='''np'''   )
																self.assertTrue(np_processed.input_values.dtype == np.floataa   )
																snake_case__							:       Tuple				      =       feature_extractor.pad([{'''input_values''': inputs}]   ,return_tensors='''pt'''   )
																self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )

				def    _lowerCamelCase       ( self :  Tuple   ,SCREAMING_SNAKE_CASE_ :  List[Any]   ):
										from datasets import load_dataset

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

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

				def    _lowerCamelCase       ( self :  List[str]   ):
										# fmt: off
										snake_case__							:       Dict				      =       torch.tensor(
										    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
										     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
										     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
										     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
										     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
										     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]   )
										# fmt: on
										snake_case__							:       List[str]				      =       self._load_datasamples(1   )
										snake_case__							:       str				      =       EncodecFeatureExtractor()
										snake_case__							:       List[str]				      =       feature_extractor(SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values
										self.assertEquals(input_values.shape   ,(1, 1, 9_3_6_8_0)   )
										self.assertTrue(torch.allclose(input_values[0, 0, :3_0]   ,SCREAMING_SNAKE_CASE_   ,atol=1E-6   )   )

				def    _lowerCamelCase       ( self :  str   ):
										# fmt: off
										snake_case__							:       Any				      =       torch.tensor(
										    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
										     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
										     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
										     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
										     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
										     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]   )
										# fmt: on
										snake_case__							:       Union[str, Any]				      =       self._load_datasamples(1   )
										snake_case__							:       List[Any]				      =       [np.tile(input_audio[0][None]   ,reps=(2, 1)   )]
										input_audio[0][1] *= 0.5
										snake_case__							:       Dict				      =       EncodecFeatureExtractor(feature_size=2   )
										snake_case__							:       int				      =       feature_extractor(SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values
										self.assertEquals(input_values.shape   ,(1, 2, 9_3_6_8_0)   )
										self.assertTrue(torch.allclose(input_values[0, 0, :3_0]   ,SCREAMING_SNAKE_CASE_   ,atol=1E-6   )   )
										self.assertTrue(torch.allclose(input_values[0, 1, :3_0]   ,EXPECTED_INPUT_VALUES * 0.5   ,atol=1E-6   )   )





				def    _lowerCamelCase       ( self :  str   ):
										snake_case__							:       List[str]				      =       self._load_datasamples(2   )
										# would be easier if the stride was like
										snake_case__							:       Any				      =       EncodecFeatureExtractor(feature_size=1   ,chunk_length_s=1   ,overlap=0.01   )

										# pad and trunc raise an error ?
										with self.assertRaisesRegex(
										    SCREAMING_SNAKE_CASE_   ,'''^Both padding and truncation were set. Make sure you only set one.$'''   ,):
																snake_case__							:       str				      =       feature_extractor(
																    SCREAMING_SNAKE_CASE_   ,padding='''max_length'''   ,truncation=SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values

										# truncate to chunk
										snake_case__							:       int				      =       feature_extractor(SCREAMING_SNAKE_CASE_   ,truncation=SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values
										self.assertEquals(truncated_outputs.shape   ,(2, 1, 7_1_5_2_0)   )  # 2 chunks

										# force truncate to max_length
										snake_case__							:       List[Any]				      =       feature_extractor(
										    SCREAMING_SNAKE_CASE_   ,truncation=SCREAMING_SNAKE_CASE_   ,max_length=4_8_0_0_0   ,return_tensors='''pt'''   ).input_values
										self.assertEquals(truncated_outputs.shape   ,(2, 1, 4_8_0_0_0)   )

										# pad to chunk
										snake_case__							:       Optional[Any]				      =       feature_extractor(SCREAMING_SNAKE_CASE_   ,padding=SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values
										self.assertEquals(padded_outputs.shape   ,(2, 1, 9_5_2_8_0)   )

										# pad to chunk
										snake_case__							:       Optional[Any]				      =       feature_extractor(SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values
										self.assertEquals(truncated_outputs.shape   ,(2, 1, 9_5_2_8_0)   )

										# force pad to max length
										snake_case__							:       Dict				      =       feature_extractor(
										    SCREAMING_SNAKE_CASE_   ,padding='''max_length'''   ,max_length=1_0_0_0_0_0   ,return_tensors='''pt'''   ).input_values
										self.assertEquals(truncated_outputs.shape   ,(2, 1, 1_0_0_0_0_0)   )

										# force no pad
										with self.assertRaisesRegex(
										    SCREAMING_SNAKE_CASE_   ,'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''   ,):
																snake_case__							:       Dict				      =       feature_extractor(SCREAMING_SNAKE_CASE_   ,padding=SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values

										snake_case__							:       List[str]				      =       feature_extractor(input_audio[0]   ,padding=SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values
										self.assertEquals(truncated_outputs.shape   ,(1, 1, 9_3_6_8_0)   )

										# no pad if no chunk_length_s
										snake_case__							:       Tuple				      =       None
										with self.assertRaisesRegex(
										    SCREAMING_SNAKE_CASE_   ,'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''   ,):
																snake_case__							:       int				      =       feature_extractor(SCREAMING_SNAKE_CASE_   ,padding=SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values

										snake_case__							:       Any				      =       feature_extractor(input_audio[0]   ,padding=SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values
										self.assertEquals(truncated_outputs.shape   ,(1, 1, 9_3_6_8_0)   )

										# no pad if no overlap
										snake_case__							:       int				      =       2
										snake_case__							:       Any				      =       None
										with self.assertRaisesRegex(
										    SCREAMING_SNAKE_CASE_   ,'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''   ,):
																snake_case__							:       Dict				      =       feature_extractor(SCREAMING_SNAKE_CASE_   ,padding=SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values

										snake_case__							:       Tuple				      =       feature_extractor(input_audio[0]   ,padding=SCREAMING_SNAKE_CASE_   ,return_tensors='''pt'''   ).input_values
										self.assertEquals(truncated_outputs.shape   ,(1, 1, 9_3_6_8_0)   )








"""simple docstring"""





import math
import os

import cva
import numpy as np

_lowercase						   =     2_55.0

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Tuple				      =       np.mean((original - contrast) ** 2		)
						if mse == 0:
												return 100

						return 20 * math.logaa(PIXEL_MAX / math.sqrt(lowerCamelCase		)		)

def 		lowerCAmelCase				(			):
						'''simple docstring'''


						snake_case__							:       Any				      =       os.path.dirname(os.path.realpath(lowerCamelCase		)		)
						# Loading images (original image and compressed image)
						snake_case__							:       Any				      =       cva.imread(os.path.join(lowerCamelCase   ,      '''image_data/original_image.png'''		)		)
						snake_case__							:       Any				      =       cva.imread(os.path.join(lowerCamelCase   ,      '''image_data/compressed_image.png'''		)   ,      1		)

						snake_case__							:       Optional[Any]				      =       cva.imread(os.path.join(lowerCamelCase   ,      '''image_data/PSNR-example-base.png'''		)		)
						snake_case__							:       Tuple				      =       cva.imread(
						    os.path.join(lowerCamelCase   ,      '''image_data/PSNR-example-comp-10.jpg'''		)   ,      1		)

						# Value expected: 29.73dB
						print('''-- First Test --'''		)
						print(f'''PSNR value is {peak_signal_to_noise_ratio(lowerCamelCase   ,      lowerCamelCase		)} dB'''		)

						# # Value expected: 31.53dB (Wikipedia Example)
						print('''\n-- Second Test --'''		)
						print(f'''PSNR value is {peak_signal_to_noise_ratio(lowerCamelCase   ,      lowerCamelCase		)} dB'''		)


if __name__ == "__main__":
							main()


