

'''simple docstring'''




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

_lowerCAmelCase         =						random.Random()




def 						UpperCamelCase	(			a		,						a=1.0		,						a=None		,						a=None			)				->     Optional[int]:






     '''simple docstring'''
     if rng is None:
          __magic_name__						       =    global_rng

     __magic_name__						       =    []
     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     ):



 def __init__(   self		:   List[Any]			,				a__		:   List[str]			,				a__		:   Dict=7			,				a__		:   int=400			,				a__		:   Optional[Any]=2000			,				a__		:   Optional[int]=24			,				a__		:   Dict=24			,				a__		:   Dict=0.0			,				a__		:   Any=1_6000			,				a__		:   List[Any]=True			,				a__		:   int=True			,				):
      __magic_name__						       =    parent
      __magic_name__						       =    batch_size
      __magic_name__						       =    min_seq_length
      __magic_name__						       =    max_seq_length
      __magic_name__						       =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      __magic_name__						       =    feature_size
      __magic_name__						       =    num_mel_bins
      __magic_name__						       =    padding_value
      __magic_name__						       =    sampling_rate
      __magic_name__						       =    return_attention_mask
      __magic_name__						       =    do_normalize



 def 			snake_case__  (   self		:   Tuple      ):
      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 			snake_case__  (   self		:   Dict			,				a__		:   str=False			,				a__		:   Any=False      ):
      def _flatten(a__		:   Tuple      ):
           return list(itertools.chain(*a__      )      )

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






@require_torch
@require_torchaudio
class   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Any      	=					SpeechaTextFeatureExtractor if is_speech_available() else None



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    SpeechaTextFeatureExtractionTester(self      )



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   str      ):
      self.assertTrue(np.all(np.mean(a__			,				axis=0      ) < 1E-3      )      )
      self.assertTrue(np.all(np.abs(np.var(a__			,				axis=0      ) - 1      ) < 1E-3      )      )



 def 			snake_case__  (   self		:   str      ):
      # Tests that all call wrap to encode_plus and batch_encode_plus
      __magic_name__						       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
      # create three inputs of length 800, 1000, and 1200
      __magic_name__						       =    [floats_list((1, x)      )[0] for x in range(800			,				1400			,				200      )]
      __magic_name__						       =    [np.asarray(a__      ) for speech_input in speech_inputs]

      # Test feature size
      __magic_name__						       =    feature_extractor(a__			,				padding=a__			,				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
      __magic_name__						       =    feature_extractor(speech_inputs[0]			,				return_tensors='''np'''      ).input_features
      __magic_name__						       =    feature_extractor(np_speech_inputs[0]			,				return_tensors='''np'''      ).input_features
      self.assertTrue(np.allclose(a__			,				a__			,				atol=1E-3      )      )

      # Test batched
      __magic_name__						       =    feature_extractor(a__			,				return_tensors='''np'''      ).input_features
      __magic_name__						       =    feature_extractor(a__			,				return_tensors='''np'''      ).input_features
      for enc_seq_a, enc_seq_a in zip(a__			,				a__      ):
           self.assertTrue(np.allclose(a__			,				a__			,				atol=1E-3      )      )

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



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
      __magic_name__						       =    [floats_list((1, x)      )[0] for x in range(800			,				1400			,				200      )]

      __magic_name__						       =    ['''longest''', '''max_length''', '''do_not_pad''']
      __magic_name__						       =    [None, 16, None]
      for max_length, padding in zip(a__			,				a__      ):
           __magic_name__						       =    feature_extractor(
               a__			,				padding=a__			,				max_length=a__			,				return_attention_mask=a__      )
           __magic_name__						       =    inputs.input_features
           __magic_name__						       =    inputs.attention_mask
           __magic_name__						       =    [np.sum(a__      ) 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 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
      __magic_name__						       =    [floats_list((1, x)      )[0] for x in range(800			,				1400			,				200      )]

      __magic_name__						       =    ['''longest''', '''max_length''', '''do_not_pad''']
      __magic_name__						       =    [None, 16, None]
      for max_length, padding in zip(a__			,				a__      ):
           __magic_name__						       =    feature_extractor(
               a__			,				max_length=a__			,				padding=a__			,				return_tensors='''np'''			,				return_attention_mask=a__      )
           __magic_name__						       =    inputs.input_features
           __magic_name__						       =    inputs.attention_mask
           __magic_name__						       =    [np.sum(a__      ) 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 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
      __magic_name__						       =    [floats_list((1, x)      )[0] for x in range(800			,				1400			,				200      )]
      __magic_name__						       =    feature_extractor(
          a__			,				padding='''max_length'''			,				max_length=4			,				truncation=a__			,				return_tensors='''np'''			,				return_attention_mask=a__			,				)
      __magic_name__						       =    inputs.input_features
      __magic_name__						       =    inputs.attention_mask
      __magic_name__						       =    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 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
      __magic_name__						       =    [floats_list((1, x)      )[0] for x in range(800			,				1400			,				200      )]
      __magic_name__						       =    feature_extractor(
          a__			,				padding='''longest'''			,				max_length=4			,				truncation=a__			,				return_tensors='''np'''			,				return_attention_mask=a__			,				)
      __magic_name__						       =    inputs.input_features
      __magic_name__						       =    inputs.attention_mask
      __magic_name__						       =    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)      )

      __magic_name__						       =    [floats_list((1, x)      )[0] for x in range(800			,				1400			,				200      )]
      __magic_name__						       =    feature_extractor(
          a__			,				padding='''longest'''			,				max_length=16			,				truncation=a__			,				return_tensors='''np'''			,				return_attention_mask=a__			,				)
      __magic_name__						       =    inputs.input_features
      __magic_name__						       =    inputs.attention_mask
      __magic_name__						       =    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 			snake_case__  (   self		:   Tuple      ):
      import torch

      __magic_name__						       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
      __magic_name__						       =    np.random.rand(100			,				32      ).astype(np.floataa      )
      __magic_name__						       =    np_speech_inputs.tolist()

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



 def 			snake_case__  (   self		:   Tuple			,				a__		:   List[str]      ):
      from datasets import load_dataset

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

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



 def 			snake_case__  (   self		:   int      ):
      # fmt: off
      __magic_name__						       =    np.array([
          -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
          -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
          -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
      ]      )
      # fmt: on

      __magic_name__						       =    self._load_datasamples(1      )
      __magic_name__						       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
      __magic_name__						       =    feature_extractor(a__			,				return_tensors='''pt'''      ).input_features
      self.assertEquals(input_features.shape			,				(1, 584, 24)      )
      self.assertTrue(np.allclose(input_features[0, 0, :30]			,				a__			,				atol=1E-4      )      )









'''simple docstring'''




from random import randint, random




def 						UpperCamelCase	(			a		,						a		,						a		,						a = False		,						a = False		,						a = 5		,						)				->     list:






     '''simple docstring'''
     __magic_name__						       =    [[-1] * number_of_cells]  # Create a highway without any car
     __magic_name__						       =    0
     __magic_name__						       =    max(a		,						0			)
     while i < number_of_cells:
          __magic_name__						       =    (
              randint(0		,						a			) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1		,						max_speed * 2			) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    0
     __magic_name__						       =    highway_now[car_index + 1 :]
     for cell in range(len(a			)			):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(a		,						-1			)




def 						UpperCamelCase	(			a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(a			)
     # Beforce calculations, the highway is empty
     __magic_name__						       =    [-1] * number_of_cells

     for car_index in range(a			):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               __magic_name__						       =    min(highway_now[car_index] + 1		,						a			)
               # Number of empty cell before the next car
               __magic_name__						       =    get_distance(a		,						a			) - 1
               # We can't have the car causing an accident
               __magic_name__						       =    min(next_highway[car_index]		,						a			)
               if random() < probability:
                    # Randomly, a driver will slow down
                    __magic_name__						       =    max(next_highway[car_index] - 1		,						0			)
     return next_highway




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     list:






     '''simple docstring'''
     __magic_name__						       =    len(highway[0]			)

     for i in range(a			):
          __magic_name__						       =    update(highway[i]		,						a		,						a			)
          __magic_name__						       =    [-1] * number_of_cells

          for car_index in range(a			):
               __magic_name__						       =    next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    __magic_name__						       =    (car_index + speed) % number_of_cells
                    # Commit the change of position
                    __magic_name__						       =    speed
          highway.append(a			)

     return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()






