


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


a_       :Optional[int]				  =				random.Random()
def 	lowercase_							(A			:			Optional[Any]		, A			:			str=1.0		, A			:			int=None		, A			:			str=None				):
       if rng is None:
              snake_case__							:     str            =     global_rng

       snake_case__							:     Optional[Any]            =     []
       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  snake_case__						(	unittest.TestCase					):






     """simple docstring"""


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






     def        lowercase_       (	self  :  int )       ->Dict:
            return {
                "feature_size": self.feature_size,
                "padding_value": self.padding_value,
                "sampling_rate": self.sampling_rate,
                "return_attention_mask": self.return_attention_mask,
            }






     def        lowercase_       (	self  :  Union[str, Any],			_snake_case  :  int=False,			_snake_case  :  List[Any]=False )       ->int:





            def _flatten(_snake_case  :  int ):
                   return list(itertools.chain(*_UpperCamelCase ) )

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

            return audio_inputs




@require_torch
class  snake_case__						(	__lowerCamelCase    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     EncodecFeatureExtractor






     def        lowercase_       (	self  :  Optional[Any] )       ->List[str]:
            snake_case__							:     Union[str, Any]            =     EnCodecFeatureExtractionTester(self )






     def        lowercase_       (	self  :  Dict )       ->List[Any]:
            snake_case__							:     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__							:     Optional[int]            =     [np.asarray(_UpperCamelCase ) 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__							:     Optional[Any]            =     feat_extract(np_audio_inputs[0],			return_tensors='np' ).input_values
            self.assertTrue(np.allclose(_UpperCamelCase,			_UpperCamelCase,			atol=1e-3 ) )

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






     def        lowercase_       (	self  :  Any )       ->Optional[int]:
            snake_case__							:     Tuple            =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
            snake_case__							:     Tuple            =     np.random.rand(1_0_0 ).astype(np.floataa )
            snake_case__							:     List[str]            =     np_audio_inputs.tolist()

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






     def        lowercase_       (	self  :  Tuple,			_snake_case  :  int )       ->List[str]:
            from datasets import load_dataset

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

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






     def        lowercase_       (	self  :  List[str] )       ->str:
            snake_case__							:     Tuple            =     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__							:     Any            =     self._load_datasamples(1 )
            snake_case__							:     List[str]            =     EncodecFeatureExtractor()
            snake_case__							:     List[str]            =     feature_extractor(_UpperCamelCase,			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],			_UpperCamelCase,			atol=1e-6 ) )






     def        lowercase_       (	self  :  Optional[int] )       ->List[Any]:
            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__							:     str            =     self._load_datasamples(1 )
            snake_case__							:     int            =     [np.tile(input_audio[0][None],			reps=(2, 1) )]
            input_audio[0][1] *= 0.5
            snake_case__							:     int            =     EncodecFeatureExtractor(feature_size=2 )
            snake_case__							:     Tuple            =     feature_extractor(_UpperCamelCase,			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],			_UpperCamelCase,			atol=1e-6 ) )
            self.assertTrue(torch.allclose(input_values[0, 1, :3_0],			EXPECTED_INPUT_VALUES * 0.5,			atol=1e-6 ) )






     def        lowercase_       (	self  :  Optional[Any] )       ->List[str]:
            snake_case__							:     str            =     self._load_datasamples(2 )
            # would be easier if the stride was like
            snake_case__							:     Union[str, Any]            =     EncodecFeatureExtractor(feature_size=1,			chunk_length_s=1,			overlap=0.0_1 )

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

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

            # pad to chunk
            snake_case__							:     Tuple            =     feature_extractor(_UpperCamelCase,			return_tensors='pt' ).input_values
            self.assertEquals(truncated_outputs.shape,			(2, 1, 9_5_2_8_0) )

            # force pad to max length
            snake_case__							:     List[Any]            =     feature_extractor(
                _UpperCamelCase,			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(
                _UpperCamelCase,			'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',			):
                   snake_case__							:     Optional[int]            =     feature_extractor(_UpperCamelCase,			padding=_UpperCamelCase,			return_tensors='pt' ).input_values

            snake_case__							:     Optional[int]            =     feature_extractor(input_audio[0],			padding=_UpperCamelCase,			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__							:     List[Any]            =     None
            with self.assertRaisesRegex(
                _UpperCamelCase,			'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',			):
                   snake_case__							:     Optional[int]            =     feature_extractor(_UpperCamelCase,			padding=_UpperCamelCase,			return_tensors='pt' ).input_values

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

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

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











import math
def 	lowercase_							(A			:			int				):

       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(A				) + 1				)		, 6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True
def 	lowercase_							(A			:			int = 1_0_0_0		, A			:			int = 1_0_0_0				):
       snake_case__							:     str            =     [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1		, A				):
              for b in range(2		, A				):
                     if is_prime(A				):
                            snake_case__							:     Optional[Any]            =     0
                            snake_case__							:     Optional[int]            =     0
                            while is_prime((n**2) + (a * n) + b				):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   snake_case__							:     Union[str, Any]            =     [count, a, b]
       snake_case__							:     List[str]            =     longest[1] * longest[2]
       return ans


if __name__ == "__main__":
 print(solution(1_000, 1_000))






