





'''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


lowercase__		:     int			=							random.Random()

if is_torch_available():
   import torch







def    a__							(							lowercase   :	Optional[int],   lowercase   :	Optional[int]=1.0,   lowercase   :	Optional[int]=None,   lowercase   :	List[Any]=None )						->						str:





     """simple docstring"""

     if rng is None:
          _UpperCamelCase							       =			global_rng

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

     return values
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       def __init__(				self  :						List[Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Optional[Any]=7			,					lowerCAmelCase__  :						Optional[Any]=400			,					lowerCAmelCase__  :						Optional[Any]=2000			,					lowerCAmelCase__  :						int=1			,					lowerCAmelCase__  :						Any=0.0			,					lowerCAmelCase__  :						List[str]=16000			,					lowerCAmelCase__  :						Any=True			,					lowerCAmelCase__  :						List[Any]=True			,					)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			batch_size
            _UpperCamelCase							       =			min_seq_length
            _UpperCamelCase							       =			max_seq_length
            _UpperCamelCase							       =			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            _UpperCamelCase							       =			feature_size
            _UpperCamelCase							       =			padding_value
            _UpperCamelCase							       =			sampling_rate
            _UpperCamelCase							       =			return_attention_mask
            _UpperCamelCase							       =			do_normalize





       def snake_case__		(				self  :						List[Any]	)       ->	str:

            '''simple docstring'''




            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 snake_case__		(				self  :						Any			,					lowerCAmelCase__  :						Optional[Any]=False			,					lowerCAmelCase__  :						str=False	)       ->	Dict:

            '''simple docstring'''




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

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

            return speech_inputs

@require_torch
@require_torchaudio
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       ASTFeatureExtractor





       def snake_case__		(				self  :						Union[str, Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			ASTFeatureExtractionTester(self	)





       def snake_case__		(				self  :						Union[str, Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
            # create three inputs of length 800, 1000, and 1200
            _UpperCamelCase							       =			[floats_list((1, x)	)[0] for x in range(800			,					1400			,					200	)]
            _UpperCamelCase							       =			[np.asarray(lowerCAmelCase__	) for speech_input in speech_inputs]

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

            # Test batched
            _UpperCamelCase							       =			feat_extract(lowerCAmelCase__			,					padding=lowerCAmelCase__			,					return_tensors='''np'''	).input_values
            _UpperCamelCase							       =			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.
            _UpperCamelCase							       =			[floats_list((1, x)	)[0] for x in (800, 800, 800)]
            _UpperCamelCase							       =			np.asarray(lowerCAmelCase__	)
            _UpperCamelCase							       =			feat_extract(lowerCAmelCase__			,					return_tensors='''np'''	).input_values
            _UpperCamelCase							       =			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 snake_case__		(				self  :						str	)       ->	Tuple:

            '''simple docstring'''




            import torch

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

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





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						List[Any]	)       ->	Optional[int]:

            '''simple docstring'''




            from datasets import load_dataset

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

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







       @require_torch
       def snake_case__		(				self  :						Optional[Any]	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			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

            _UpperCamelCase							       =			self._load_datasamples(1	)
            _UpperCamelCase							       =			ASTFeatureExtractor()
            _UpperCamelCase							       =			feature_extractor(lowerCAmelCase__			,					return_tensors='''pt'''	).input_values
            self.assertEquals(input_values.shape			,					(1, 1024, 128)	)
            self.assertTrue(torch.allclose(input_values[0, 0, :30]			,					lowerCAmelCase__			,					atol=1e-4	)	)










'''simple docstring'''

def    a__							(							lowercase   :	Dict,   lowercase   :	Optional[Any] )						->						int:





     """simple docstring"""

     _UpperCamelCase							       =			0
     _UpperCamelCase							       =			len(lowercase ) - 1

     while left <= right:
          # avoid divided by 0 during interpolation
          if sorted_collection[left] == sorted_collection[right]:
               if sorted_collection[left] == item:
                    return left
               else:
                    return None

          _UpperCamelCase							       =			left + ((item - sorted_collection[left]) * (right - left)) // (
              sorted_collection[right] - sorted_collection[left]
          )

          # out of range check
          if point < 0 or point >= len(lowercase ):
               return None

          _UpperCamelCase							       =			sorted_collection[point]
          if current_item == item:
               return point
          else:
               if point < left:
                    _UpperCamelCase							       =			left
                    _UpperCamelCase							       =			point
               elif point > right:
                    _UpperCamelCase							       =			right
                    _UpperCamelCase							       =			point
               else:
                    if item < current_item:
                         _UpperCamelCase							       =			point - 1
                    else:
                         _UpperCamelCase							       =			point + 1
     return None







def    a__							(							lowercase   :	int,   lowercase   :	Optional[int],   lowercase   :	List[Any],   lowercase   :	List[Any] )						->						Dict:





     """simple docstring"""

     if sorted_collection[left] == sorted_collection[right]:
          if sorted_collection[left] == item:
               return left
          else:
               return None

     _UpperCamelCase							       =			left + ((item - sorted_collection[left]) * (right - left)) // (
         sorted_collection[right] - sorted_collection[left]
     )

     # out of range check
     if point < 0 or point >= len(lowercase ):
          return None

     if sorted_collection[point] == item:
          return point
     elif point < left:
          return interpolation_search_by_recursion(lowercase,   lowercase,   lowercase,   lowercase )
     elif point > right:
          return interpolation_search_by_recursion(lowercase,   lowercase,   lowercase,   lowercase )
     else:
          if sorted_collection[point] > item:
               return interpolation_search_by_recursion(
                   lowercase,   lowercase,   lowercase,   point - 1 )
          else:
               return interpolation_search_by_recursion(
                   lowercase,   lowercase,   point + 1,   lowercase )







def    a__							(							lowercase   :	Any )						->						List[Any]:





     """simple docstring"""

     if collection != sorted(lowercase ):
          raise ValueError('''Collection must be ascending sorted''' )
     return True


if __name__ == "__main__":
   import sys
   lowercase__		:     int			=							0
   if debug == 1:
      lowercase__		:     Optional[Any]			=							[10, 30, 40, 45, 50, 66, 77, 93]
      try:
         __assert_sorted(collection)
      except ValueError:
         sys.exit('Sequence must be ascending sorted to apply interpolation search')
      lowercase__		:     List[str]			=							67

   lowercase__		:     Optional[int]			=							interpolation_search(collection, target)
   if result is not None:
      print(F"""{target} found at positions: {result}""")
   else:
      print('Not found')



