"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
				import torch

				from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class     UpperCamelCase__(			unittest.TestCase ):





						@slow
						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							XLMRobertaModel.from_pretrained('xlm-roberta-base'     )
													A__	     =							torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]     )
													# The dog is cute and lives in the garden house

													A__	     =							torch.Size((1, 12, 7_68)     )  # batch_size, sequence_length, embedding_vector_dim
													A__	     =							torch.tensor(
													    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]     )
													#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
													#  xlmr.eval()
													#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
													with torch.no_grad():
																				A__	     =							model(__UpperCAmelCase     )['last_hidden_state'].detach()
													self.assertEqual(output.shape		,__UpperCAmelCase     )
													# compare the actual values for a slice of last dim
													self.assertTrue(torch.allclose(output[:, :, -1]		,__UpperCAmelCase		,atol=1e-3     )     )





						@slow
						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							XLMRobertaModel.from_pretrained('xlm-roberta-large'     )
													A__	     =							torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]     )
													# The dog is cute and lives in the garden house

													A__	     =							torch.Size((1, 12, 10_24)     )  # batch_size, sequence_length, embedding_vector_dim
													A__	     =							torch.tensor(
													    [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]     )
													#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
													#  xlmr.eval()
													#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
													with torch.no_grad():
																				A__	     =							model(__UpperCAmelCase     )['last_hidden_state'].detach()
													self.assertEqual(output.shape		,__UpperCAmelCase     )
													# compare the actual values for a slice of last dim
													self.assertTrue(torch.allclose(output[:, :, -1]		,__UpperCAmelCase		,atol=1e-3     )     )






"""simple docstring"""




from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np
class     UpperCamelCase__(			__A ):





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				float:
													return 0.0






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
							A__	     =							max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
							return lowest, highest






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							512
							A__	     =							[1] + [0] * (size - 1)
							A__	     =							[filter_type.process(UpperCamelCase__				) for item in inputs]

							A__	     =							[0] * (samplerate - size)  # zero-padding
							outputs += filler
							A__	     =							np.abs(np.fft.fft(UpperCamelCase__				)				)
							A__	     =							20 * np.logaa(UpperCamelCase__				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24       ,    samplerate / 2 - 1				)
							plt.xlabel('Frequency (Hz)'				)
							plt.xscale('log'				)

							# Display within reasonable bounds
							A__	     =							get_bounds(UpperCamelCase__       ,    UpperCamelCase__				)
							plt.ylim(max([-80, bounds[0]]				)       ,    min([80, bounds[1]]				)				)
							plt.ylabel('Gain (dB)'				)

							plt.plot(UpperCamelCase__				)
							plt.show()






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							512
							A__	     =							[1] + [0] * (size - 1)
							A__	     =							[filter_type.process(UpperCamelCase__				) for item in inputs]

							A__	     =							[0] * (samplerate - size)  # zero-padding
							outputs += filler
							A__	     =							np.angle(np.fft.fft(UpperCamelCase__				)				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24       ,    samplerate / 2 - 1				)
							plt.xlabel('Frequency (Hz)'				)
							plt.xscale('log'				)

							plt.ylim(-2 * pi       ,    2 * pi				)
							plt.ylabel('Phase shift (Radians)'				)
							plt.plot(np.unwrap(UpperCamelCase__       ,    -2 * pi				)				)
							plt.show()





