





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
       import torch

if is_datasets_available():
       from datasets import load_dataset

a__:      List[Any]									=		random.Random()


def 	UpperCamelCase__(       UpperCamelCase__			:       List[str] ,     UpperCamelCase__			:       List[str]=1.0 ,     UpperCamelCase__			:       Dict=None ,     UpperCamelCase__			:       Optional[Any]=None  )->List[str]:
       if rng is None:
              A__											=						global_rng

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

       return values




class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=7,__lowerCamelCase=400,__lowerCamelCase=2000,__lowerCamelCase=2048,__lowerCamelCase=128,__lowerCamelCase=1,__lowerCamelCase=512,__lowerCamelCase=30,__lowerCamelCase=4_4100,):
        A__											=						parent
        A__											=						batch_size
        A__											=						min_seq_length
        A__											=						max_seq_length
        A__											=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        A__											=						spectrogram_length
        A__											=						feature_size
        A__											=						num_audio_channels
        A__											=						hop_length
        A__											=						chunk_length
        A__											=						sampling_rate



 def 				UpperCamelCase			(							self ):
        return {
            "spectrogram_length": self.spectrogram_length,
            "feature_size": self.feature_size,
            "num_audio_channels": self.num_audio_channels,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "sampling_rate": self.sampling_rate,
        }




 def 				UpperCamelCase			(							self,__lowerCamelCase=False,__lowerCamelCase=False ):
        def _flatten(__lowerCamelCase ):
               return list(itertools.chain(*__lowerCamelCase ) )

        if equal_length:
               A__											=						[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
        else:
               # make sure that inputs increase in size
               A__											=						[
                   floats_list((x, self.feature_size) )
                   for x in range(self.min_seq_length,self.max_seq_length,self.seq_length_diff )
               ]
        if numpify:
               A__											=						[np.asarray(__lowerCamelCase ) for x in speech_inputs]
        return speech_inputs




@require_torch
@require_torchaudio
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	TvltFeatureExtractor



 def 				UpperCamelCase			(							self ):
        A__											=						TvltFeatureExtractionTester(self )



 def 				UpperCamelCase			(							self ):
        A__											=						self.feature_extraction_class(**self.feat_extract_dict )
        self.assertTrue(hasattr(__lowerCamelCase,'''spectrogram_length''' ) )
        self.assertTrue(hasattr(__lowerCamelCase,'''feature_size''' ) )
        self.assertTrue(hasattr(__lowerCamelCase,'''num_audio_channels''' ) )
        self.assertTrue(hasattr(__lowerCamelCase,'''hop_length''' ) )
        self.assertTrue(hasattr(__lowerCamelCase,'''chunk_length''' ) )
        self.assertTrue(hasattr(__lowerCamelCase,'''sampling_rate''' ) )



 def 				UpperCamelCase			(							self ):
        A__											=						self.feature_extraction_class(**self.feat_extract_dict )

        with tempfile.TemporaryDirectory() as tmpdirname:
               A__											=						feat_extract_first.save_pretrained(__lowerCamelCase )[0]
               check_json_file_has_correct_format(__lowerCamelCase )
               A__											=						self.feature_extraction_class.from_pretrained(__lowerCamelCase )

        A__											=						feat_extract_first.to_dict()
        A__											=						feat_extract_second.to_dict()
        A__											=						dict_first.pop('''mel_filters''' )
        A__											=						dict_second.pop('''mel_filters''' )
        self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase ) )
        self.assertEqual(__lowerCamelCase,__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.feature_extraction_class(**self.feat_extract_dict )

        with tempfile.TemporaryDirectory() as tmpdirname:
               A__											=						os.path.join(__lowerCamelCase,'''feat_extract.json''' )
               feat_extract_first.to_json_file(__lowerCamelCase )
               A__											=						self.feature_extraction_class.from_json_file(__lowerCamelCase )

        A__											=						feat_extract_first.to_dict()
        A__											=						feat_extract_second.to_dict()
        A__											=						dict_first.pop('''mel_filters''' )
        A__											=						dict_second.pop('''mel_filters''' )
        self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase ) )
        self.assertEqual(__lowerCamelCase,__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        # Initialize feature_extractor
        A__											=						self.feature_extraction_class(**self.feat_extract_dict )

        # create three inputs of length 800, 1000, and 1200
        A__											=						[floats_list((1, x) )[0] for x in range(800,1400,200 )]
        A__											=						[np.asarray(__lowerCamelCase ) for speech_input in speech_inputs]

        # Test not batched input
        A__											=						feature_extractor(np_speech_inputs[0],return_tensors='''np''',sampling_rate=4_4100 ).audio_values

        self.assertTrue(encoded_audios.ndim == 4 )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

        # Test batched
        A__											=						feature_extractor(__lowerCamelCase,return_tensors='''np''',sampling_rate=4_4100 ).audio_values

        self.assertTrue(encoded_audios.ndim == 4 )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

        # Test audio masking
        A__											=						feature_extractor(
            __lowerCamelCase,return_tensors='''np''',sampling_rate=4_4100,mask_audio=__lowerCamelCase ).audio_values

        self.assertTrue(encoded_audios.ndim == 4 )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

        # Test 2-D numpy arrays are batched.
        A__											=						[floats_list((1, x) )[0] for x in (800, 800, 800)]
        A__											=						np.asarray(__lowerCamelCase )
        A__											=						feature_extractor(__lowerCamelCase,return_tensors='''np''',sampling_rate=4_4100 ).audio_values
        self.assertTrue(encoded_audios.ndim == 4 )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )



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

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




 def 				UpperCamelCase			(							self ):
        A__											=						self._load_datasamples(1 )
        A__											=						TvltFeatureExtractor()
        A__											=						feature_extractor(__lowerCamelCase,return_tensors='''pt''' ).audio_values

        self.assertEquals(audio_values.shape,(1, 1, 192, 128) )

        A__											=						torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2],__lowerCamelCase,atol=1E-4 ) )












from __future__ import annotations

import math


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->bool:

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5 ,     int(math.sqrt(UpperCamelCase__  ) + 1  ) ,     6  ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->list[int]:
       A__											=						str(UpperCamelCase__  )
       A__											=						[n]
       for i in range(1 ,     len(UpperCamelCase__  )  ):
              list_nums.append(int(str_num[i:]  )  )
              list_nums.append(int(str_num[:-i]  )  )
       return list_nums


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->bool:
       if len(str(UpperCamelCase__  )  ) > 3:
              if not is_prime(int(str(UpperCamelCase__  )[-3:]  )  ) or not is_prime(int(str(UpperCamelCase__  )[:3]  )  ):
                     return False
       return True


def 	UpperCamelCase__(       UpperCamelCase__			:       int = 11  )->list[int]:
       A__											=						[]
       A__											=						13
       while len(UpperCamelCase__  ) != count:
              if validate(UpperCamelCase__  ):
                     A__											=						list_truncated_nums(UpperCamelCase__  )
                     if all(is_prime(UpperCamelCase__  ) for i in list_nums  ):
                            list_truncated_primes.append(UpperCamelCase__  )
              num += 2
       return list_truncated_primes


def 	UpperCamelCase__(       )->int:
       return sum(compute_truncated_primes(11  )  )


if __name__ == "__main__":
       print(F"{sum(compute_truncated_primes(11)) = }")





