




from __future__ import annotations




class 		UpperCamelCase__	:

    '''simple docstring'''



    def __init__(    self     ,  UpperCamelCase__     )     ->      None:
           lowerCamelCase   :      List[Any]			 =					order

           # a_{0} ... a_{k}
           lowerCamelCase   :      Any			 =					[1.0] + [0.0] * order
           # b_{0} ... b_{k}
           lowerCamelCase   :      Union[str, Any]			 =					[1.0] + [0.0] * order

           # x[n-1] ... x[n-k]
           lowerCamelCase   :      int			 =					[0.0] * self.order
           # y[n-1] ... y[n-k]
           lowerCamelCase   :      Union[str, Any]			 =					[0.0] * self.order







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      None:
           if len(UpperCamelCase__     ) < self.order:
                  lowerCamelCase   :      Dict			 =					[1.0, *a_coeffs]

           if len(UpperCamelCase__     ) != self.order + 1:
                  lowerCamelCase   :      str			 =					(
                      F'''Expected a_coeffs to have {self.order + 1} elements '''
                      F'''for {self.order}-order filter, got {len(UpperCamelCase__     )}'''
                  )
                  raise ValueError(UpperCamelCase__     )

           if len(UpperCamelCase__     ) != self.order + 1:
                  lowerCamelCase   :      Optional[int]			 =					(
                      F'''Expected b_coeffs to have {self.order + 1} elements '''
                      F'''for {self.order}-order filter, got {len(UpperCamelCase__     )}'''
                  )
                  raise ValueError(UpperCamelCase__     )

           lowerCamelCase   :      List[Any]			 =					a_coeffs
           lowerCamelCase   :      str			 =					b_coeffs







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      float:
           lowerCamelCase   :      Optional[Any]			 =					0.0

           # Start at index 1 and do index 0 at the end.
           for i in range(1     ,  self.order + 1     ):
                  result += (
                      self.b_coeffs[i] * self.input_history[i - 1]
                      - self.a_coeffs[i] * self.output_history[i - 1]
                  )

           lowerCamelCase   :      Dict			 =					(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

           lowerCamelCase   :      Dict			 =					self.input_history[:-1]
           lowerCamelCase   :      int			 =					self.output_history[:-1]

           lowerCamelCase   :      List[Any]			 =					sample
           lowerCamelCase   :      Dict			 =					result

           return result











import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
				import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class 		UpperCAmelCase__			(			unittest.TestCase						,__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	load_tool('''text-to-speech''' )
					self.tool.setup()




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     List[Any]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     Union[str, Any]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     Optional[int]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     List[str]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )





