


import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
    from transformers import WhisperFeatureExtractor

if is_torch_available():
    import torch

UpperCamelCase					:       Dict			    =     random.Random()



def UpperCamelCase_				(				__a			,				__a=1.0			,				__a=None			,				__a=None      )	->   Union[str, Any]:
      if rng is None:
            a__	:  Optional[Any]     =   global_rng

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

      return values



@require_torch
@require_torchaudio
class A__     ( unittest.TestCase  ):



  """simple docstring"""



  def __init__( self			:  List[str]				,				lowerCamelCase__			:  List[str]				,				lowerCamelCase__			:  str=7				,				lowerCamelCase__			:  Any=400				,				lowerCamelCase__			:  str=2_000				,				lowerCamelCase__			:  str=10				,				lowerCamelCase__			:  List[Any]=160				,				lowerCamelCase__			:  str=8				,				lowerCamelCase__			:  str=0.0				,				lowerCamelCase__			:  List[Any]=4_000				,				lowerCamelCase__			:  str=False				,				lowerCamelCase__			:  int=True				,				):
        a__	:  Optional[Any]     =   parent
        a__	:  Dict     =   batch_size
        a__	:  List[Any]     =   min_seq_length
        a__	:  Union[str, Any]     =   max_seq_length
        a__	:  int     =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        a__	:  List[str]     =   padding_value
        a__	:  Dict     =   sampling_rate
        a__	:  Optional[Any]     =   return_attention_mask
        a__	:  int     =   do_normalize
        a__	:  str     =   feature_size
        a__	:  Any     =   chunk_length
        a__	:  Optional[Any]     =   hop_length

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        return {
            "feature_size": self.feature_size,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "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			:  Dict				,				lowerCamelCase__			:  List[Any]=False				,				lowerCamelCase__			:  str=False       ):
        def _flatten(lowerCamelCase__			:  int       ):
              return list(itertools.chain(*lowerCamelCase__       )       )

        if equal_length:
              a__	:  Optional[Any]     =   [floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
        else:
              # make sure that inputs increase in size
              a__	:  List[Any]     =   [
                  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__	:  Union[str, Any]     =   [np.asarray(lowerCamelCase__       ) for x in speech_inputs]
        return speech_inputs




@require_torch
@require_torchaudio
class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		WhisperFeatureExtractor if is_speech_available() else None

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Optional[Any]     =   WhisperFeatureExtractionTester(self       )

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  int     =   self.feature_extraction_class(**self.feat_extract_dict       )

        with tempfile.TemporaryDirectory() as tmpdirname:
              a__	:  Union[str, Any]     =   feat_extract_first.save_pretrained(lowerCamelCase__       )[0]
              check_json_file_has_correct_format(lowerCamelCase__       )
              a__	:  str     =   self.feature_extraction_class.from_pretrained(lowerCamelCase__       )

        a__	:  Any     =   feat_extract_first.to_dict()
        a__	:  int     =   feat_extract_second.to_dict()
        a__	:  int     =   feat_extract_first.mel_filters
        a__	:  Union[str, Any]     =   feat_extract_second.mel_filters
        self.assertTrue(np.allclose(lowerCamelCase__				,				lowerCamelCase__       )       )
        self.assertEqual(lowerCamelCase__				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Optional[Any]     =   self.feature_extraction_class(**self.feat_extract_dict       )

        with tempfile.TemporaryDirectory() as tmpdirname:
              a__	:  int     =   os.path.join(lowerCamelCase__				,				"feat_extract.json"       )
              feat_extract_first.to_json_file(lowerCamelCase__       )
              a__	:  Tuple     =   self.feature_extraction_class.from_json_file(lowerCamelCase__       )

        a__	:  List[str]     =   feat_extract_first.to_dict()
        a__	:  List[str]     =   feat_extract_second.to_dict()
        a__	:  List[Any]     =   feat_extract_first.mel_filters
        a__	:  Tuple     =   feat_extract_second.mel_filters
        self.assertTrue(np.allclose(lowerCamelCase__				,				lowerCamelCase__       )       )
        self.assertEqual(lowerCamelCase__				,				lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[str]       ):
        # Tests that all call wrap to encode_plus and batch_encode_plus
        a__	:  Tuple     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
        # create three inputs of length 800, 1000, and 1200
        a__	:  Tuple     =   [floats_list((1, x)       )[0] for x in range(800				,				1_400				,				200       )]
        a__	:  List[str]     =   [np.asarray(lowerCamelCase__       ) for speech_input in speech_inputs]

        # Test feature size
        a__	:  Optional[Any]     =   feature_extractor(lowerCamelCase__				,				padding="max_length"				,				return_tensors="np"       ).input_features
        self.assertTrue(input_features.ndim == 3       )
        self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames       )
        self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size       )

        # Test not batched input
        a__	:  int     =   feature_extractor(speech_inputs[0]				,				return_tensors="np"       ).input_features
        a__	:  int     =   feature_extractor(np_speech_inputs[0]				,				return_tensors="np"       ).input_features
        self.assertTrue(np.allclose(lowerCamelCase__				,				lowerCamelCase__				,				atol=1E-3       )       )

        # Test batched
        a__	:  int     =   feature_extractor(lowerCamelCase__				,				return_tensors="np"       ).input_features
        a__	:  Union[str, Any]     =   feature_extractor(lowerCamelCase__				,				return_tensors="np"       ).input_features
        for enc_seq_a, enc_seq_a in zip(lowerCamelCase__				,				lowerCamelCase__       ):
              self.assertTrue(np.allclose(lowerCamelCase__				,				lowerCamelCase__				,				atol=1E-3       )       )

        # Test 2-D numpy arrays are batched.
        a__	:  Optional[Any]     =   [floats_list((1, x)       )[0] for x in (800, 800, 800)]
        a__	:  int     =   np.asarray(lowerCamelCase__       )
        a__	:  Dict     =   feature_extractor(lowerCamelCase__				,				return_tensors="np"       ).input_features
        a__	:  Optional[int]     =   feature_extractor(lowerCamelCase__				,				return_tensors="np"       ).input_features
        for enc_seq_a, enc_seq_a in zip(lowerCamelCase__				,				lowerCamelCase__       ):
              self.assertTrue(np.allclose(lowerCamelCase__				,				lowerCamelCase__				,				atol=1E-3       )       )

        # Test truncation required
        a__	:  int     =   [floats_list((1, x)       )[0] for x in range(200				,				(feature_extractor.n_samples + 500)				,				200       )]
        a__	:  List[str]     =   [np.asarray(lowerCamelCase__       ) for speech_input in speech_inputs]

        a__	:  Dict     =   [x[: feature_extractor.n_samples] for x in speech_inputs]
        a__	:  List[Any]     =   [np.asarray(lowerCamelCase__       ) for speech_input in speech_inputs_truncated]

        a__	:  List[str]     =   feature_extractor(lowerCamelCase__				,				return_tensors="np"       ).input_features
        a__	:  List[Any]     =   feature_extractor(lowerCamelCase__				,				return_tensors="np"       ).input_features
        for enc_seq_a, enc_seq_a in zip(lowerCamelCase__				,				lowerCamelCase__       ):
              self.assertTrue(np.allclose(lowerCamelCase__				,				lowerCamelCase__				,				atol=1E-3       )       )

  def    _UpperCamelCase( self			:  Dict       ):
        import torch

        a__	:  Tuple     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
        a__	:  List[Any]     =   np.random.rand(100				,				32       ).astype(np.floataa       )
        a__	:  List[Any]     =   np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
              a__	:  Dict     =   feature_extractor.pad([{"input_features": inputs}]				,				return_tensors="np"       )
              self.assertTrue(np_processed.input_features.dtype == np.floataa       )
              a__	:  Tuple     =   feature_extractor.pad([{"input_features": inputs}]				,				return_tensors="pt"       )
              self.assertTrue(pt_processed.input_features.dtype == torch.floataa       )

  def    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  List[Any]       ):
        a__	:  List[str]     =   load_dataset("hf-internal-testing/librispeech_asr_dummy"				,				"clean"				,				split="validation"       )
        # automatic decoding with librispeech
        a__	:  Dict     =   ds.sort("id"       ).select(range(lowerCamelCase__       )       )[:num_samples]["audio"]

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

  def    _UpperCamelCase( self			:  Tuple       ):
        # fmt: off
        a__	:  int     =   torch.tensor(
            [
                0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
                0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
                0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
                -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
            ]       )
        # fmt: on

        a__	:  str     =   self._load_datasamples(1       )
        a__	:  List[str]     =   WhisperFeatureExtractor()
        a__	:  Optional[int]     =   feature_extractor(lowerCamelCase__				,				return_tensors="pt"       ).input_features
        self.assertEqual(input_features.shape				,				(1, 80, 3_000)       )
        self.assertTrue(torch.allclose(input_features[0, 0, :30]				,				lowerCamelCase__				,				atol=1E-4       )       )

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  List[Any]     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
        a__	:  Dict     =   self._load_datasamples(1       )[0]
        a__	:  int     =   ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
        a__	:  Tuple     =   feat_extract.zero_mean_unit_var_norm([audio]				,				attention_mask=lowerCamelCase__       )[0]

        self.assertTrue(np.all(np.mean(lowerCamelCase__       ) < 1E-3       )       )
        self.assertTrue(np.all(np.abs(np.var(lowerCamelCase__       ) - 1       ) < 1E-3       )       )


"""simple docstring"""



import math
from collections.abc import Iterator
from itertools import takewhile

def  _A(		lowerCAmelCase							):

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

def  _A(		):
							A__       :     List[str]								=			2
							while True:
														if is_prime(lowerCAmelCase							):
																					yield num
														num += 1

def  _A(		lowerCAmelCase = 200_0000							):
							return sum(takewhile(lambda lowerCAmelCase							: x < n			,  prime_generator()							)							)


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





