


'''simple docstring'''




import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__snake_case    						=random.Random()





def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Dict=1.0	, lowerCamelCase	:     Dict=None	, lowerCamelCase	:     str=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 		UpperCAmelCase_      (  unittest.TestCase		):





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      Optional[Any]=7    ,      UpperCAmelCase__       :      str=4_0_0    ,      UpperCAmelCase__       :      List[Any]=2_0_0_0    ,      UpperCAmelCase__       :      Dict=1    ,      UpperCAmelCase__       :      Dict=0.0    ,      UpperCAmelCase__       :      List[Any]=1_6_0_0_0    ,      UpperCAmelCase__       :      Union[str, Any]=True    ,      UpperCAmelCase__       :      Tuple=True    ,      )				->  List[Any]:
        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 							__UpperCAmelCase      ( self       :      Any    )				->  List[Any]:
        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 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[int]=False    ,      UpperCAmelCase__       :      Optional[int]=False    )				->  Dict:
        def _flatten(UpperCAmelCase__       :      Tuple    ):
           return list(itertools.chain(*UpperCAmelCase__    )    )

        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(UpperCAmelCase__    ) for x in speech_inputs]

        return speech_inputs




class 		UpperCAmelCase_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Tuple		=     WavaVecaFeatureExtractor





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Optional[Any]:
        lowerCAmelCase     =			WavaVecaFeatureExtractionTester(self    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Optional[Any]    )				->  Union[str, Any]:
        self.assertTrue(np.all(np.mean(UpperCAmelCase__    ,      axis=0    ) < 1E-3    )    )
        self.assertTrue(np.all(np.abs(np.var(UpperCAmelCase__    ,      axis=0    ) - 1    ) < 1E-3    )    )





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Optional[int]:
        # 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_0_0    ,      1_4_0_0    ,      2_0_0    )]
        lowerCAmelCase     =			[np.asarray(UpperCAmelCase__    ) 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(UpperCAmelCase__    ,      UpperCAmelCase__    ,      atol=1E-3    )    )

        # Test batched
        lowerCAmelCase     =			feat_extract(UpperCAmelCase__    ,      return_tensors='np'    ).input_values
        lowerCAmelCase     =			feat_extract(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    )    )

        # Test 2-D numpy arrays are batched.
        lowerCAmelCase     =			[floats_list((1, x)    )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
        lowerCAmelCase     =			np.asarray(UpperCAmelCase__    )
        lowerCAmelCase     =			feat_extract(UpperCAmelCase__    ,      return_tensors='np'    ).input_values
        lowerCAmelCase     =			feat_extract(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 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  List[str]:
        lowerCAmelCase     =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
        lowerCAmelCase     =			[floats_list((1, x)    )[0] for x in range(8_0_0    ,      1_4_0_0    ,      2_0_0    )]

        lowerCAmelCase     =			['longest', 'max_length', 'do_not_pad']
        lowerCAmelCase     =			[None, 1_6_0_0, None]
        for max_length, padding in zip(UpperCAmelCase__    ,      UpperCAmelCase__    ):
           lowerCAmelCase     =			feat_extract(UpperCAmelCase__    ,      padding=UpperCAmelCase__    ,      max_length=UpperCAmelCase__    ,      return_tensors='np'    )
           lowerCAmelCase     =			processed.input_values

           self._check_zero_mean_unit_variance(input_values[0][:8_0_0]    )
           self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6    )
           self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]    )
           self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6    )
           self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  str:
        lowerCAmelCase     =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
        lowerCAmelCase     =			range(8_0_0    ,      1_4_0_0    ,      2_0_0    )
        lowerCAmelCase     =			[floats_list((1, x)    )[0] for x in lengths]

        lowerCAmelCase     =			['longest', 'max_length', 'do_not_pad']
        lowerCAmelCase     =			[None, 1_6_0_0, None]

        for max_length, padding in zip(UpperCAmelCase__    ,      UpperCAmelCase__    ):
           lowerCAmelCase     =			feat_extract(UpperCAmelCase__    ,      max_length=UpperCAmelCase__    ,      padding=UpperCAmelCase__    )
           lowerCAmelCase     =			processed.input_values

           self._check_zero_mean_unit_variance(input_values[0][:8_0_0]    )
           self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]    )
           self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Tuple:
        lowerCAmelCase     =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
        lowerCAmelCase     =			[floats_list((1, x)    )[0] for x in range(8_0_0    ,      1_4_0_0    ,      2_0_0    )]
        lowerCAmelCase     =			feat_extract(
            UpperCAmelCase__    ,      truncation=UpperCAmelCase__    ,      max_length=1_0_0_0    ,      padding='max_length'    ,      return_tensors='np'    )
        lowerCAmelCase     =			processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_0_0]    )
        self._check_zero_mean_unit_variance(input_values[1]    )
        self._check_zero_mean_unit_variance(input_values[2]    )





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Tuple:
        lowerCAmelCase     =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
        lowerCAmelCase     =			[floats_list((1, x)    )[0] for x in range(8_0_0    ,      1_4_0_0    ,      2_0_0    )]
        lowerCAmelCase     =			feat_extract(
            UpperCAmelCase__    ,      truncation=UpperCAmelCase__    ,      max_length=1_0_0_0    ,      padding='longest'    ,      return_tensors='np'    )
        lowerCAmelCase     =			processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_0_0]    )
        self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]    )
        self._check_zero_mean_unit_variance(input_values[2]    )

        # make sure that if max_length < longest -> then pad to max_length
        self.assertTrue(input_values.shape == (3, 1_0_0_0)    )

        lowerCAmelCase     =			[floats_list((1, x)    )[0] for x in range(8_0_0    ,      1_4_0_0    ,      2_0_0    )]
        lowerCAmelCase     =			feat_extract(
            UpperCAmelCase__    ,      truncation=UpperCAmelCase__    ,      max_length=2_0_0_0    ,      padding='longest'    ,      return_tensors='np'    )
        lowerCAmelCase     =			processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_0_0]    )
        self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]    )
        self._check_zero_mean_unit_variance(input_values[2]    )

        # make sure that if max_length > longest -> then pad to longest
        self.assertTrue(input_values.shape == (3, 1_2_0_0)    )





     @require_torch
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[Any]:
        import torch

        lowerCAmelCase     =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
        lowerCAmelCase     =			np.random.rand(1_0_0    ).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    )





     @slow
     @require_torch
     def 							__UpperCAmelCase      ( self       :      Any    )				->  Optional[int]:
        # this test makes sure that models that are using
        # group norm don't have their feature extractor return the
        # attention_mask
        for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
           lowerCAmelCase     =			WavaVecaConfig.from_pretrained(UpperCAmelCase__    )
           lowerCAmelCase     =			WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase__    )

           # only "layer" feature extraction norm should make use of
           # attention_mask
           self.assertEqual(feat_extract.return_attention_mask    ,      config.feat_extract_norm == 'layer'    )








import math


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						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(SCREAMING_SNAKE_CASE__       ) + 1       )  ,		6       ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 10001       ):

						try:
												snake_case_   =	int(SCREAMING_SNAKE_CASE__       )
						except (TypeError, ValueError):
												raise TypeError('''Parameter nth must be int or castable to int.'''       ) from None
						if nth <= 0:
												raise ValueError('''Parameter nth must be greater than or equal to one.'''       )
						snake_case_   =	[]
						snake_case_   =	2
						while len(SCREAMING_SNAKE_CASE__       ) < nth:
												if is_prime(SCREAMING_SNAKE_CASE__       ):
																		primes.append(SCREAMING_SNAKE_CASE__       )
																		num += 1
												else:
																		num += 1
						return primes[len(SCREAMING_SNAKE_CASE__       ) - 1]


if __name__ == "__main__":
					print(f"""{solution() = }""")