



import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch


UpperCamelCase_									=   random.Random()







def       lowerCamelCase_  (    _a       :						Any   ,		_a       :						List[Any]=1.0   ,		_a       :						Tuple=None   ,		_a       :						Optional[int]=None    ):



    '''simple docstring'''




    if rng is None:
        UpperCAmelCase_     :       int  		=					global_rng

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

    return values







@require_torch
class       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     str  ,lowerCamelCase_:     List[str]=7  ,lowerCamelCase_:     int=400  ,lowerCamelCase_:     Tuple=2000  ,lowerCamelCase_:     Optional[Any]=1  ,lowerCamelCase_:     Optional[int]=0.0  ,lowerCamelCase_:     Dict=24000  ,lowerCamelCase_:     Dict=True  ,)	->      Any:
       UpperCAmelCase_     :       List[str]  		=					parent
       UpperCAmelCase_     :       List[str]  		=					batch_size
       UpperCAmelCase_     :       List[str]  		=					min_seq_length
       UpperCAmelCase_     :       Optional[int]  		=					max_seq_length
       UpperCAmelCase_     :       int  		=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       UpperCAmelCase_     :       int  		=					feature_size
       UpperCAmelCase_     :       List[str]  		=					padding_value
       UpperCAmelCase_     :       Optional[Any]  		=					sampling_rate
       UpperCAmelCase_     :       Optional[int]  		=					return_attention_mask


   def       A__    (    self:     Optional[int]							)	->      Optional[Any]:
       return {
           "feature_size": self.feature_size,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "return_attention_mask": self.return_attention_mask,
       }






   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     List[Any]=False  ,lowerCamelCase_:     Dict=False							)	->      List[str]:
       def _flatten(lowerCamelCase_:     Optional[int]							):
           return list(itertools.chain(*lowerCamelCase_							)							)

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

       return audio_inputs







@require_torch
class       _snake_case				(     __snake_case ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : List[Any]       =						EncodecFeatureExtractor


   def       A__    (    self:     Union[str, Any]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       List[str]  		=					EnCodecFeatureExtractionTester(self							)


   def       A__    (    self:     List[Any]							)	->      Dict:
       # Tests that all call wrap to encode_plus and batch_encode_plus
       UpperCAmelCase_     :       List[Any]  		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
       # create three inputs of length 800, 1000, and 1200
       UpperCAmelCase_     :       str  		=					[floats_list((1, x)							)[0] for x in range(800  ,1400  ,200							)]
       UpperCAmelCase_     :       int  		=					[np.asarray(lowerCamelCase_							) for audio_input in audio_inputs]

       # Test not batched input
       UpperCAmelCase_     :       Union[str, Any]  		=					feat_extract(audio_inputs[0]  ,return_tensors="""np"""							).input_values
       UpperCAmelCase_     :       Dict  		=					feat_extract(np_audio_inputs[0]  ,return_tensors="""np"""							).input_values
       self.assertTrue(np.allclose(lowerCamelCase_  ,lowerCamelCase_  ,atol=1e-3							)							)

       # Test batched
       UpperCAmelCase_     :       Tuple  		=					feat_extract(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""np"""							).input_values
       UpperCAmelCase_     :       Any  		=					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							)							)


   def       A__    (    self:     str							)	->      List[str]:
       UpperCAmelCase_     :       Union[str, Any]  		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
       UpperCAmelCase_     :       Union[str, Any]  		=					np.random.rand(100							).astype(np.floataa							)
       UpperCAmelCase_     :       Union[str, Any]  		=					np_audio_inputs.tolist()

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


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     Union[str, Any]							)	->      Optional[Any]:
       from datasets import load_dataset

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

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


   def       A__    (    self:     Optional[int]							)	->      Union[str, Any]:
       # fmt: off
       UpperCAmelCase_     :       Dict  		=					torch.tensor(
           [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
            3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
            2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
            4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
            7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
            4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]							)
       # fmt: on
       UpperCAmelCase_     :       Optional[Any]  		=					self._load_datasamples(1							)
       UpperCAmelCase_     :       Union[str, Any]  		=					EncodecFeatureExtractor()
       UpperCAmelCase_     :       List[Any]  		=					feature_extractor(lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(input_values.shape  ,(1, 1, 93680)							)
       self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,lowerCamelCase_  ,atol=1e-6							)							)


   def       A__    (    self:     List[str]							)	->      Dict:
       # fmt: off
       UpperCAmelCase_     :       Optional[int]  		=					torch.tensor(
           [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
            3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
            2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
            4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
            7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
            4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]							)
       # fmt: on
       UpperCAmelCase_     :       Tuple  		=					self._load_datasamples(1							)
       UpperCAmelCase_     :       List[Any]  		=					[np.tile(input_audio[0][None]  ,reps=(2, 1)							)]
       input_audio[0][1] *= 0.5
       UpperCAmelCase_     :       int  		=					EncodecFeatureExtractor(feature_size=2							)
       UpperCAmelCase_     :       int  		=					feature_extractor(lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(input_values.shape  ,(1, 2, 93680)							)
       self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,lowerCamelCase_  ,atol=1e-6							)							)
       self.assertTrue(torch.allclose(input_values[0, 1, :30]  ,EXPECTED_INPUT_VALUES * 0.5  ,atol=1e-6							)							)






   def       A__    (    self:     Optional[int]							)	->      List[str]:
       UpperCAmelCase_     :       Dict  		=					self._load_datasamples(2							)
       # would be easier if the stride was like
       UpperCAmelCase_     :       Any  		=					EncodecFeatureExtractor(feature_size=1  ,chunk_length_s=1  ,overlap=0.0_1							)

       # pad and trunc raise an error ?
       with self.assertRaisesRegex(
           lowerCamelCase_  ,"""^Both padding and truncation were set. Make sure you only set one.$"""  ,):
           UpperCAmelCase_     :       Optional[int]  		=					feature_extractor(
               lowerCamelCase_  ,padding="""max_length"""  ,truncation=lowerCamelCase_  ,return_tensors="""pt"""							).input_values

       # truncate to chunk
       UpperCAmelCase_     :       str  		=					feature_extractor(lowerCamelCase_  ,truncation=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(2, 1, 71520)							)  # 2 chunks

       # force truncate to max_length
       UpperCAmelCase_     :       Optional[Any]  		=					feature_extractor(
           lowerCamelCase_  ,truncation=lowerCamelCase_  ,max_length=48000  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(2, 1, 48000)							)

       # pad to chunk
       UpperCAmelCase_     :       Dict  		=					feature_extractor(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(padded_outputs.shape  ,(2, 1, 95280)							)

       # pad to chunk
       UpperCAmelCase_     :       Optional[Any]  		=					feature_extractor(lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(2, 1, 95280)							)

       # force pad to max length
       UpperCAmelCase_     :       str  		=					feature_extractor(
           lowerCamelCase_  ,padding="""max_length"""  ,max_length=100000  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(2, 1, 100000)							)

       # force no pad
       with self.assertRaisesRegex(
           lowerCamelCase_  ,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""  ,):
           UpperCAmelCase_     :       Dict  		=					feature_extractor(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values

       UpperCAmelCase_     :       Any  		=					feature_extractor(input_audio[0]  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(1, 1, 93680)							)

       # no pad if no chunk_length_s
       UpperCAmelCase_     :       str  		=					None
       with self.assertRaisesRegex(
           lowerCamelCase_  ,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""  ,):
           UpperCAmelCase_     :       Dict  		=					feature_extractor(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values

       UpperCAmelCase_     :       Any  		=					feature_extractor(input_audio[0]  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(1, 1, 93680)							)

       # no pad if no overlap
       UpperCAmelCase_     :       int  		=					2
       UpperCAmelCase_     :       Optional[Any]  		=					None
       with self.assertRaisesRegex(
           lowerCamelCase_  ,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""  ,):
           UpperCAmelCase_     :       int  		=					feature_extractor(lowerCamelCase_  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values

       UpperCAmelCase_     :       Optional[Any]  		=					feature_extractor(input_audio[0]  ,padding=lowerCamelCase_  ,return_tensors="""pt"""							).input_values
       self.assertEquals(truncated_outputs.shape  ,(1, 1, 93680)							)








"""simple docstring"""



from __future__ import annotations

from math import pi, pow




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 if side_length < 0:
  raise ValueError("""vol_cube() only accepts non-negative values"""			)
 return pow(_SCREAMING_SNAKE_CASE       ,	3			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 if height < 0 or radius < 0:
  raise ValueError("""vol_spherical_cap() only accepts non-negative values"""			)
 # Volume is 1/3 pi * height squared * (3 * radius - height)
 return 1 / 3 * pi * pow(_SCREAMING_SNAKE_CASE       ,	2			) * (3 * radius - height)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 if radius_a < 0 or radius_a < 0 or centers_distance < 0:
  raise ValueError("""vol_spheres_intersect() only accepts non-negative values"""			)
 if centers_distance == 0:
  return vol_sphere(min(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)			)

 UpperCAmelCase__ 						=				(
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )
 UpperCAmelCase__ 						=				(
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )

 return vol_spherical_cap(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			) + vol_spherical_cap(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:

 if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
  raise ValueError(
      """vol_spheres_union() only accepts non-negative values, non-zero radius"""			)

 if centers_distance == 0:
  return vol_sphere(max(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)			)

 return (
     vol_sphere(_SCREAMING_SNAKE_CASE			)
     + vol_sphere(_SCREAMING_SNAKE_CASE			)
     - vol_spheres_intersect(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)
 )




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 if width < 0 or height < 0 or length < 0:
  raise ValueError("""vol_cuboid() only accepts non-negative values"""			)
 return float(width * height * length			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 if height < 0 or area_of_base < 0:
  raise ValueError("""vol_cone() only accepts non-negative values"""			)
 return area_of_base * height / 3.0




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 if height < 0 or radius < 0:
  raise ValueError("""vol_right_circ_cone() only accepts non-negative values"""			)
 return pi * pow(_SCREAMING_SNAKE_CASE       ,	2			) * height / 3.0




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 if height < 0 or area_of_base < 0:
  raise ValueError("""vol_prism() only accepts non-negative values"""			)
 return float(area_of_base * height			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 if height < 0 or area_of_base < 0:
  raise ValueError("""vol_pyramid() only accepts non-negative values"""			)
 return area_of_base * height / 3.0




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 if radius < 0:
  raise ValueError("""vol_sphere() only accepts non-negative values"""			)
 # Volume is 4/3 * pi * radius cubed
 return 4 / 3 * pi * pow(_SCREAMING_SNAKE_CASE       ,	3			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 if radius < 0:
  raise ValueError("""vol_hemisphere() only accepts non-negative values"""			)
 # Volume is radius cubed * pi * 2/3
 return pow(_SCREAMING_SNAKE_CASE       ,	3			) * pi * 2 / 3




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 if height < 0 or radius < 0:
  raise ValueError("""vol_circular_cylinder() only accepts non-negative values"""			)
 # Volume is radius squared * height * pi
 return pow(_SCREAMING_SNAKE_CASE       ,	2			) * height * pi




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 # Volume - (outer_radius squared - inner_radius squared) * pi * height
 if inner_radius < 0 or outer_radius < 0 or height < 0:
  raise ValueError(
      """vol_hollow_circular_cylinder() only accepts non-negative values"""			)
 if outer_radius <= inner_radius:
  raise ValueError("""outer_radius must be greater than inner_radius"""			)
 return pi * (pow(_SCREAMING_SNAKE_CASE       ,	2			) - pow(_SCREAMING_SNAKE_CASE       ,	2			)) * height




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 # Volume is 1/3 * pi * height *
 #           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError("""vol_conical_frustum() only accepts non-negative values"""			)
 return (
     1
     / 3
     * pi
     * height
     * (pow(_SCREAMING_SNAKE_CASE       ,	2			) + pow(_SCREAMING_SNAKE_CASE       ,	2			) + radius_a * radius_a)
 )




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 if torus_radius < 0 or tube_radius < 0:
  raise ValueError("""vol_torus() only accepts non-negative values"""			)
 return 2 * pow(_SCREAMING_SNAKE_CASE       ,	2			) * torus_radius * pow(_SCREAMING_SNAKE_CASE       ,	2			)




def       snake_case__		(   )    ->str:
 print("""Volumes:"""			)
 print(F'''Cube: {vol_cube(2			) = }'''			)  # = 8
 print(F'''Cuboid: {vol_cuboid(2       ,	2       ,	2			) = }'''			)  # = 8
 print(F'''Cone: {vol_cone(2       ,	2			) = }'''			)  # ~= 1.33
 print(F'''Right Circular Cone: {vol_right_circ_cone(2       ,	2			) = }'''			)  # ~= 8.38
 print(F'''Prism: {vol_prism(2       ,	2			) = }'''			)  # = 4
 print(F'''Pyramid: {vol_pyramid(2       ,	2			) = }'''			)  # ~= 1.33
 print(F'''Sphere: {vol_sphere(2			) = }'''			)  # ~= 33.5
 print(F'''Hemisphere: {vol_hemisphere(2			) = }'''			)  # ~= 16.75
 print(F'''Circular Cylinder: {vol_circular_cylinder(2       ,	2			) = }'''			)  # ~= 25.1
 print(F'''Torus: {vol_torus(2       ,	2			) = }'''			)  # ~= 157.9
 print(F'''Conical Frustum: {vol_conical_frustum(2       ,	2       ,	4			) = }'''			)  # ~= 58.6
 print(F'''Spherical cap: {vol_spherical_cap(1       ,	2			) = }'''			)  # ~= 5.24
 print(F'''Spheres intersetion: {vol_spheres_intersect(2       ,	2       ,	1			) = }'''			)  # ~= 21.21
 print(F'''Spheres union: {vol_spheres_union(2       ,	2       ,	1			) = }'''			)  # ~= 45.81
 print(
     F'''Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1       ,	2       ,	3			) = }'''			)  # ~= 28.3


if __name__ == "__main__":
 main()



