
'''simple docstring'''






import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCAmelCase_       :   Dict   =       random.Random()

if is_torch_available():
 import torch







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Any=1.0				,	_lowerCAmelCase   :    List[str]=None				,	_lowerCAmelCase   :    str=None							):



      """simple docstring"""




      if rng is None:
            _lowerCamelCase				:   List[str]								=   global_rng

      _lowerCamelCase				:   str								=   []
      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      :       Dict,__A      :       List[str],__A      :       Union[str, Any]=7,__A      :       Union[str, Any]=4_0_0,__A      :       List[Any]=2_0_0_0,__A      :       Any=1,__A      :       Dict=0.0,__A      :       Optional[int]=1_6_0_0_0,__A      :       int=True,__A      :       List[str]=True,):
          _lowerCamelCase				:   Union[str, Any]								=   parent
          _lowerCamelCase				:   Any								=   batch_size
          _lowerCamelCase				:   List[str]								=   min_seq_length
          _lowerCamelCase				:   Any								=   max_seq_length
          _lowerCamelCase				:   Tuple								=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          _lowerCamelCase				:   List[str]								=   feature_size
          _lowerCamelCase				:   Any								=   padding_value
          _lowerCamelCase				:   List[str]								=   sampling_rate
          _lowerCamelCase				:   int								=   return_attention_mask
          _lowerCamelCase				:   int								=   do_normalize



    def   lowerCamelCase_  (	self      :       Dict     ):
          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   lowerCamelCase_  (	self      :       List[Any],__A      :       Any=False,__A      :       Optional[int]=False     ):
          def _flatten(__A      :       Optional[int]     ):
                return list(itertools.chain(*lowerCAmelCase__     )     )

          if equal_length:
                _lowerCamelCase				:   str								=   floats_list((self.batch_size, self.max_seq_length)     )
          else:
                # make sure that inputs increase in size
                _lowerCamelCase				:   Optional[int]								=   [
                    _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				:   Optional[Any]								=   [np.asarray(lowerCAmelCase__     ) for x in speech_inputs]

          return speech_inputs




@require_torch
@require_torchaudio
class  UpperCAmelCase__						(       _snake_case						,	unittest.TestCase ):
    lowerCAmelCase_				    =		ASTFeatureExtractor



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   Dict								=   ASTFeatureExtractionTester(self     )



    def   lowerCamelCase_  (	self      :       Any     ):
          # Tests that all call wrap to encode_plus and batch_encode_plus
          _lowerCamelCase				:   List[Any]								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
          # create three inputs of length 800, 1000, and 1200
          _lowerCamelCase				:   Optional[int]								=   [floats_list((1, x)     )[0] for x in range(8_0_0,1_4_0_0,2_0_0     )]
          _lowerCamelCase				:   Optional[Any]								=   [np.asarray(lowerCAmelCase__     ) for speech_input in speech_inputs]

          # Test not batched input
          _lowerCamelCase				:   Dict								=   feat_extract(speech_inputs[0],return_tensors="np"     ).input_values
          _lowerCamelCase				:   int								=   feat_extract(np_speech_inputs[0],return_tensors="np"     ).input_values
          self.assertTrue(np.allclose(lowerCAmelCase__,lowerCAmelCase__,atol=1e-3     )     )

          # Test batched
          _lowerCamelCase				:   List[Any]								=   feat_extract(lowerCAmelCase__,padding=lowerCAmelCase__,return_tensors="np"     ).input_values
          _lowerCamelCase				:   int								=   feat_extract(lowerCAmelCase__,padding=lowerCAmelCase__,return_tensors="np"     ).input_values
          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				:   Dict								=   [floats_list((1, x)     )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
          _lowerCamelCase				:   Optional[Any]								=   np.asarray(lowerCAmelCase__     )
          _lowerCamelCase				:   Optional[int]								=   feat_extract(lowerCAmelCase__,return_tensors="np"     ).input_values
          _lowerCamelCase				:   Union[str, Any]								=   feat_extract(lowerCAmelCase__,return_tensors="np"     ).input_values
          for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__,lowerCAmelCase__     ):
                self.assertTrue(np.allclose(lowerCAmelCase__,lowerCAmelCase__,atol=1e-3     )     )



    @require_torch
    def   lowerCamelCase_  (	self      :       int     ):
          import torch

          _lowerCamelCase				:   str								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
          _lowerCamelCase				:   Dict								=   np.random.rand(1_0_0     ).astype(np.floataa     )
          _lowerCamelCase				:   Tuple								=   np_speech_inputs.tolist()

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



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       List[Any]     ):
          from datasets import load_dataset

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

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



    @require_torch
    def   lowerCamelCase_  (	self      :       Any     ):
          # fmt: off
          _lowerCamelCase				:   Union[str, Any]								=   torch.tensor(
              [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
               -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
               -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
               -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]     )
          # fmt: on

          _lowerCamelCase				:   Tuple								=   self._load_datasamples(1     )
          _lowerCamelCase				:   Union[str, Any]								=   ASTFeatureExtractor()
          _lowerCamelCase				:   Tuple								=   feature_extractor(lowerCAmelCase__,return_tensors="pt"     ).input_values
          self.assertEquals(input_values.shape,(1, 1_0_2_4, 1_2_8)     )
          self.assertTrue(torch.allclose(input_values[0, 0, :3_0],lowerCAmelCase__,atol=1e-4     )     )

'''simple docstring'''






def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'float' object cannot be interpreted as an integer"							)
      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'str' object cannot be interpreted as an integer"							)

      if num == 0:
            return "0b0"

      _lowerCamelCase				:   Tuple								=   False

      if num < 0:
            _lowerCamelCase				:   List[Any]								=   True
            _lowerCamelCase				:   int								=   -num

      _lowerCamelCase				:   list[int]								=   []
      while num > 0:
            binary.insert(0				,	num % 2							)
            num >>= 1

      if negative:
            return "-0b" + "".join(str(_lowerCAmelCase							) for e in binary							)

      return "0b" + "".join(str(_lowerCAmelCase							) for e in binary							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()