

import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import SpeechaTextFeatureExtractor

__UpperCAmelCase								=			random.Random()





def      snake_case_				(__A					:						Dict ,       __A					:						str=1.0 ,       __A					:						Optional[Any]=None ,       __A					:						Tuple=None					)      ->     Tuple:
   if rng is None:
      __lowerCAmelCase  :      List[Any]    =						global_rng

   __lowerCAmelCase  :      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
@require_torchaudio
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Dict=7	,							lowerCAmelCase			:   Dict=4_00	,							lowerCAmelCase			:   Optional[int]=20_00	,							lowerCAmelCase			:   Dict=24	,							lowerCAmelCase			:   Any=24	,							lowerCAmelCase			:   Tuple=0.0	,							lowerCAmelCase			:   Any=1_60_00	,							lowerCAmelCase			:   Union[str, Any]=True	,							lowerCAmelCase			:   Optional[Any]=True	,							)							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						parent
     __lowerCAmelCase  :      str    =						batch_size
     __lowerCAmelCase  :      Dict    =						min_seq_length
     __lowerCAmelCase  :      Tuple    =						max_seq_length
     __lowerCAmelCase  :      Union[str, Any]    =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     __lowerCAmelCase  :      Optional[Any]    =						feature_size
     __lowerCAmelCase  :      Optional[int]    =						num_mel_bins
     __lowerCAmelCase  :      Optional[int]    =						padding_value
     __lowerCAmelCase  :      Any    =						sampling_rate
     __lowerCAmelCase  :      Optional[Any]    =						return_attention_mask
     __lowerCAmelCase  :      Any    =						do_normalize







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[Any]:






     """simple docstring"""







     return {
         "feature_size": self.feature_size,
         "num_mel_bins": self.num_mel_bins,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   Dict=False	,							lowerCAmelCase			:   List[Any]=False   )							->     str:






     """simple docstring"""







     def _flatten(lowerCAmelCase			:   Union[str, Any]   ):
        return list(itertools.chain(*lowerCAmelCase   )   )

     if equal_length:
        __lowerCAmelCase  :      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
        __lowerCAmelCase  :      str    =						[
            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  :      Union[str, Any]    =						[np.asarray(lowerCAmelCase   ) for x in speech_inputs]
     return speech_inputs






@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=SpeechaTextFeatureExtractor if is_speech_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						SpeechaTextFeatureExtractionTester(self   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Union[str, Any]   )							->     str:






     """simple docstring"""







     self.assertTrue(np.all(np.mean(lowerCAmelCase	,							axis=0   ) < 1e-3   )   )
     self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase	,							axis=0   ) - 1   ) < 1e-3   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     # create three inputs of length 800, 1000, and 1200
     __lowerCAmelCase  :      int    =						[floats_list((1, x)   )[0] for x in range(8_00	,							14_00	,							2_00   )]
     __lowerCAmelCase  :      Tuple    =						[np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

     # Test feature size
     __lowerCAmelCase  :      int    =						feature_extractor(lowerCAmelCase	,							padding=lowerCAmelCase	,							return_tensors="""np"""   ).input_features
     self.assertTrue(input_features.ndim == 3   )
     self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size   )

     # Test not batched input
     __lowerCAmelCase  :      Union[str, Any]    =						feature_extractor(speech_inputs[0]	,							return_tensors="""np"""   ).input_features
     __lowerCAmelCase  :      int    =						feature_extractor(np_speech_inputs[0]	,							return_tensors="""np"""   ).input_features
     self.assertTrue(np.allclose(lowerCAmelCase	,							lowerCAmelCase	,							atol=1e-3   )   )

     # Test batched
     __lowerCAmelCase  :      Tuple    =						feature_extractor(lowerCAmelCase	,							return_tensors="""np"""   ).input_features
     __lowerCAmelCase  :      str    =						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.
     __lowerCAmelCase  :      Tuple    =						[floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
     __lowerCAmelCase  :      Any    =						np.asarray(lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						feature_extractor(lowerCAmelCase	,							return_tensors="""np"""   ).input_features
     __lowerCAmelCase  :      Tuple    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     __lowerCAmelCase  :      str    =						[floats_list((1, x)   )[0] for x in range(8_00	,							14_00	,							2_00   )]

     __lowerCAmelCase  :      str    =						["""longest""", """max_length""", """do_not_pad"""]
     __lowerCAmelCase  :      List[Any]    =						[None, 16, None]
     for max_length, padding in zip(lowerCAmelCase	,							lowerCAmelCase   ):
        __lowerCAmelCase  :      Optional[int]    =						feature_extractor(
            lowerCAmelCase	,							padding=lowerCAmelCase	,							max_length=lowerCAmelCase	,							return_attention_mask=lowerCAmelCase   )
        __lowerCAmelCase  :      int    =						inputs.input_features
        __lowerCAmelCase  :      Union[str, Any]    =						inputs.attention_mask
        __lowerCAmelCase  :      Dict    =						[np.sum(lowerCAmelCase   ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     __lowerCAmelCase  :      Optional[int]    =						[floats_list((1, x)   )[0] for x in range(8_00	,							14_00	,							2_00   )]

     __lowerCAmelCase  :      Tuple    =						["""longest""", """max_length""", """do_not_pad"""]
     __lowerCAmelCase  :      Dict    =						[None, 16, None]
     for max_length, padding in zip(lowerCAmelCase	,							lowerCAmelCase   ):
        __lowerCAmelCase  :      List[str]    =						feature_extractor(
            lowerCAmelCase	,							max_length=lowerCAmelCase	,							padding=lowerCAmelCase	,							return_tensors="""np"""	,							return_attention_mask=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						inputs.input_features
        __lowerCAmelCase  :      Dict    =						inputs.attention_mask
        __lowerCAmelCase  :      List[Any]    =						[np.sum(lowerCAmelCase   ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
        self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6   )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
        self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6   )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     __lowerCAmelCase  :      str    =						[floats_list((1, x)   )[0] for x in range(8_00	,							14_00	,							2_00   )]
     __lowerCAmelCase  :      Optional[int]    =						feature_extractor(
         lowerCAmelCase	,							padding="""max_length"""	,							max_length=4	,							truncation=lowerCAmelCase	,							return_tensors="""np"""	,							return_attention_mask=lowerCAmelCase	,							)
     __lowerCAmelCase  :      Tuple    =						inputs.input_features
     __lowerCAmelCase  :      List[str]    =						inputs.attention_mask
     __lowerCAmelCase  :      Dict    =						np.sum(attention_mask == 1	,							axis=1   )

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
     self._check_zero_mean_unit_variance(input_features[1]   )
     self._check_zero_mean_unit_variance(input_features[2]   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     __lowerCAmelCase  :      Dict    =						[floats_list((1, x)   )[0] for x in range(8_00	,							14_00	,							2_00   )]
     __lowerCAmelCase  :      Union[str, Any]    =						feature_extractor(
         lowerCAmelCase	,							padding="""longest"""	,							max_length=4	,							truncation=lowerCAmelCase	,							return_tensors="""np"""	,							return_attention_mask=lowerCAmelCase	,							)
     __lowerCAmelCase  :      Dict    =						inputs.input_features
     __lowerCAmelCase  :      str    =						inputs.attention_mask
     __lowerCAmelCase  :      str    =						np.sum(attention_mask == 1	,							axis=1   )

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
     self._check_zero_mean_unit_variance(input_features[2]   )

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape	,							(3, 4, 24)   )

     __lowerCAmelCase  :      Any    =						[floats_list((1, x)   )[0] for x in range(8_00	,							14_00	,							2_00   )]
     __lowerCAmelCase  :      List[Any]    =						feature_extractor(
         lowerCAmelCase	,							padding="""longest"""	,							max_length=16	,							truncation=lowerCAmelCase	,							return_tensors="""np"""	,							return_attention_mask=lowerCAmelCase	,							)
     __lowerCAmelCase  :      Optional[Any]    =						inputs.input_features
     __lowerCAmelCase  :      Dict    =						inputs.attention_mask
     __lowerCAmelCase  :      Dict    =						np.sum(attention_mask == 1	,							axis=1   )

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
     self._check_zero_mean_unit_variance(input_features[2]   )

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape	,							(3, 6, 24)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     int:






     """simple docstring"""







     import torch

     __lowerCAmelCase  :      Any    =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     __lowerCAmelCase  :      List[Any]    =						np.random.rand(1_00	,							32   ).astype(np.floataa   )
     __lowerCAmelCase  :      Optional[Any]    =						np_speech_inputs.tolist()

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   List[str]   )							->     Dict:






     """simple docstring"""







     from datasets import load_dataset

     __lowerCAmelCase  :      str    =						load_dataset("""hf-internal-testing/librispeech_asr_dummy"""	,							"""clean"""	,							split="""validation"""   )
     # automatic decoding with librispeech
     __lowerCAmelCase  :      Tuple    =						ds.sort("""id"""   ).select(range(lowerCAmelCase   )   )[:num_samples]["""audio"""]

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						np.array([
         -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
         -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
         -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
     ]   )
     # fmt: on

     __lowerCAmelCase  :      Optional[Any]    =						self._load_datasamples(1   )
     __lowerCAmelCase  :      Optional[int]    =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     __lowerCAmelCase  :      Dict    =						feature_extractor(lowerCAmelCase	,							return_tensors="""pt"""   ).input_features
     self.assertEquals(input_features.shape	,							(1, 5_84, 24)   )
     self.assertTrue(np.allclose(input_features[0, 0, :30]	,							lowerCAmelCase	,							atol=1e-4   )   )





from __future__ import annotations





def      snake_case_				(__A					:						list[float] ,       __A					:						list[float]					)      ->     float:
   __lowerCAmelCase  :      Union[str, Any]    =						sorted(numsa + numsa					)
   __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						divmod(len(__A					) ,       2					)
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of first array: """).split()]
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')


