


'''simple docstring'''
import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				 =	logging.get_logger(__name__)

__snake_case				 =	{
    '''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
    '''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							'encodec'




		def __init__(						self       ,  UpperCamelCase_=[1.5, 3.0, 6.0, 12.0, 24.0]       ,  UpperCamelCase_=24000       ,  UpperCamelCase_=1       ,  UpperCamelCase_=False       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=128       ,  UpperCamelCase_=32       ,  UpperCamelCase_=1       ,  UpperCamelCase_=[8, 5, 4, 2]       ,  UpperCamelCase_="weight_norm"       ,  UpperCamelCase_=7       ,  UpperCamelCase_=7       ,  UpperCamelCase_=3       ,  UpperCamelCase_=2       ,  UpperCamelCase_=True       ,  UpperCamelCase_="reflect"       ,  UpperCamelCase_=2       ,  UpperCamelCase_=2       ,  UpperCamelCase_=1.0       ,  UpperCamelCase_=1024       ,  UpperCamelCase_=None       ,  UpperCamelCase_=True       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					target_bandwidths
							UpperCamelCase__  :Optional[Any]				   =					sampling_rate
							UpperCamelCase__  :Tuple				   =					audio_channels
							UpperCamelCase__  :str				   =					normalize
							UpperCamelCase__  :Tuple				   =					chunk_length_s
							UpperCamelCase__  :List[str]				   =					overlap
							UpperCamelCase__  :List[Any]				   =					hidden_size
							UpperCamelCase__  :str				   =					num_filters
							UpperCamelCase__  :Optional[Any]				   =					num_residual_layers
							UpperCamelCase__  :Any				   =					upsampling_ratios
							UpperCamelCase__  :int				   =					norm_type
							UpperCamelCase__  :Union[str, Any]				   =					kernel_size
							UpperCamelCase__  :Union[str, Any]				   =					last_kernel_size
							UpperCamelCase__  :Optional[int]				   =					residual_kernel_size
							UpperCamelCase__  :List[str]				   =					dilation_growth_rate
							UpperCamelCase__  :Union[str, Any]				   =					use_causal_conv
							UpperCamelCase__  :List[str]				   =					pad_mode
							UpperCamelCase__  :List[str]				   =					compress
							UpperCamelCase__  :Tuple				   =					num_lstm_layers
							UpperCamelCase__  :Any				   =					trim_right_ratio
							UpperCamelCase__  :int				   =					codebook_size
							UpperCamelCase__  :List[Any]				   =					codebook_dim if codebook_dim is not None else hidden_size
							UpperCamelCase__  :Tuple				   =					use_conv_shortcut

							if self.norm_type not in ["weight_norm", "time_group_norm"]:
												raise ValueError(
												    F'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''				)

							super().__init__(**UpperCamelCase_				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if self.chunk_length_s is None:
												return None
							else:
												return int(self.chunk_length_s * self.sampling_rate				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if self.chunk_length_s is None or self.overlap is None:
												return None
							else:
												return max(1       ,  int((1.0 - self.overlap) * self.chunk_length				)				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					np.prod(self.upsampling_ratios				)
							return math.ceil(self.sampling_rate / hop_length				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)



'''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   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-base'''				)
							UpperCamelCase__  :List[str]				   =					torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]				)
							# The dog is cute and lives in the garden house

							UpperCamelCase__  :Union[str, Any]				   =					torch.Size((1, 12, 768)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :Union[str, Any]				   =					torch.tensor(
							    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]				)
							#  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():
												UpperCamelCase__  :Any				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-large'''				)
							UpperCamelCase__  :List[str]				   =					torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]				)
							# The dog is cute and lives in the garden house

							UpperCamelCase__  :Any				   =					torch.Size((1, 12, 1024)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :List[Any]				   =					torch.tensor(
							    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]				)
							#  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():
												UpperCamelCase__  :Optional[int]				   =					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				)				)