
"""simple docstring"""



from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings





@maybe_allow_in_graph
class 				a			(					nn.Module      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_=0.0     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = "geglu"     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = "layer_norm"     ,	UpperCamelCase_ = False     ,	):
          super().__init__()
          UpperCAmelCase__	:      int     							=					only_cross_attention

          UpperCAmelCase__	:      List[str]     							=					(num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
          UpperCAmelCase__	:      Union[str, Any]     							=					(num_embeds_ada_norm is not None) and norm_type == 'ada_norm'

          if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
              raise ValueError(
                  F'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
                  F''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''      )

          # Define 3 blocks. Each block has its own normalization layer.
          # 1. Self-Attn
          if self.use_ada_layer_norm:
              UpperCAmelCase__	:      Any     							=					AdaLayerNorm(UpperCamelCase_     ,	UpperCamelCase_      )
          elif self.use_ada_layer_norm_zero:
              UpperCAmelCase__	:      Optional[Any]     							=					AdaLayerNormZero(UpperCamelCase_     ,	UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      List[str]     							=					nn.LayerNorm(UpperCamelCase_     ,	elementwise_affine=UpperCamelCase_      )
          UpperCAmelCase__	:      Any     							=					Attention(
              query_dim=UpperCamelCase_     ,	heads=UpperCamelCase_     ,	dim_head=UpperCamelCase_     ,	dropout=UpperCamelCase_     ,	bias=UpperCamelCase_     ,	cross_attention_dim=cross_attention_dim if only_cross_attention else None     ,	upcast_attention=UpperCamelCase_     ,	)

          # 2. Cross-Attn
          if cross_attention_dim is not None or double_self_attention:
              # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
              # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
              # the second cross attention block.
              UpperCAmelCase__	:      Tuple     							=					(
                  AdaLayerNorm(UpperCamelCase_     ,	UpperCamelCase_      )
                  if self.use_ada_layer_norm
                  else nn.LayerNorm(UpperCamelCase_     ,	elementwise_affine=UpperCamelCase_      )
              )
              UpperCAmelCase__	:      Any     							=					Attention(
                  query_dim=UpperCamelCase_     ,	cross_attention_dim=cross_attention_dim if not double_self_attention else None     ,	heads=UpperCamelCase_     ,	dim_head=UpperCamelCase_     ,	dropout=UpperCamelCase_     ,	bias=UpperCamelCase_     ,	upcast_attention=UpperCamelCase_     ,	)  # is self-attn if encoder_hidden_states is none
          else:
              UpperCAmelCase__	:      Optional[Any]     							=					None
              UpperCAmelCase__	:      List[str]     							=					None

          # 3. Feed-forward
          UpperCAmelCase__	:      List[str]     							=					nn.LayerNorm(UpperCamelCase_     ,	elementwise_affine=UpperCamelCase_      )
          UpperCAmelCase__	:      Any     							=					FeedForward(UpperCamelCase_     ,	dropout=UpperCamelCase_     ,	activation_fn=UpperCamelCase_     ,	final_dropout=UpperCamelCase_      )

          # let chunk size default to None
          UpperCAmelCase__	:      List[Any]     							=					None
          UpperCAmelCase__	:      int     							=					0



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          # Sets chunk feed-forward
          UpperCAmelCase__	:      Optional[Any]     							=					chunk_size
          UpperCAmelCase__	:      Dict     							=					dim



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	):
          # Notice that normalization is always applied before the real computation in the following blocks.
          # 1. Self-Attention
          if self.use_ada_layer_norm:
              UpperCAmelCase__	:      int     							=					self.norma(UpperCamelCase_     ,	UpperCamelCase_      )
          elif self.use_ada_layer_norm_zero:
              UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					self.norma(
                  UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	hidden_dtype=hidden_states.dtype      )
          else:
              UpperCAmelCase__	:      Optional[Any]     							=					self.norma(UpperCamelCase_      )

          UpperCAmelCase__	:      Tuple     							=					cross_attention_kwargs if cross_attention_kwargs is not None else {}

          UpperCAmelCase__	:      int     							=					self.attna(
              UpperCamelCase_     ,	encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None     ,	attention_mask=UpperCamelCase_     ,	**UpperCamelCase_     ,	)
          if self.use_ada_layer_norm_zero:
              UpperCAmelCase__	:      List[str]     							=					gate_msa.unsqueeze(1      ) * attn_output
          UpperCAmelCase__	:      Optional[int]     							=					attn_output + hidden_states

          # 2. Cross-Attention
          if self.attna is not None:
              UpperCAmelCase__	:      Union[str, Any]     							=					(
                  self.norma(UpperCamelCase_     ,	UpperCamelCase_      ) if self.use_ada_layer_norm else self.norma(UpperCamelCase_      )
              )

              UpperCAmelCase__	:      int     							=					self.attna(
                  UpperCamelCase_     ,	encoder_hidden_states=UpperCamelCase_     ,	attention_mask=UpperCamelCase_     ,	**UpperCamelCase_     ,	)
              UpperCAmelCase__	:      str     							=					attn_output + hidden_states

          # 3. Feed-forward
          UpperCAmelCase__	:      Optional[int]     							=					self.norma(UpperCamelCase_      )

          if self.use_ada_layer_norm_zero:
              UpperCAmelCase__	:      str     							=					norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

          if self._chunk_size is not None:
              # "feed_forward_chunk_size" can be used to save memory
              if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                  raise ValueError(
                      F'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''      )

              UpperCAmelCase__	:      List[str]     							=					norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
              UpperCAmelCase__	:      int     							=					torch.cat(
                  [self.ff(UpperCamelCase_      ) for hid_slice in norm_hidden_states.chunk(UpperCamelCase_     ,	dim=self._chunk_dim      )]     ,	dim=self._chunk_dim     ,	)
          else:
              UpperCAmelCase__	:      Dict     							=					self.ff(UpperCamelCase_      )

          if self.use_ada_layer_norm_zero:
              UpperCAmelCase__	:      Tuple     							=					gate_mlp.unsqueeze(1      ) * ff_output

          UpperCAmelCase__	:      List[Any]     							=					ff_output + hidden_states

          return hidden_states





class 				a			(					nn.Module      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = 4     ,	UpperCamelCase_ = 0.0     ,	UpperCamelCase_ = "geglu"     ,	UpperCamelCase_ = False     ,	):
          super().__init__()
          UpperCAmelCase__	:      List[Any]     							=					int(dim * mult      )
          UpperCAmelCase__	:      int     							=					dim_out if dim_out is not None else dim

          if activation_fn == "gelu":
              UpperCAmelCase__	:      List[Any]     							=					GELU(UpperCamelCase_     ,	UpperCamelCase_      )
          if activation_fn == "gelu-approximate":
              UpperCAmelCase__	:      List[str]     							=					GELU(UpperCamelCase_     ,	UpperCamelCase_     ,	approximate='tanh'      )
          elif activation_fn == "geglu":
              UpperCAmelCase__	:      Dict     							=					GEGLU(UpperCamelCase_     ,	UpperCamelCase_      )
          elif activation_fn == "geglu-approximate":
              UpperCAmelCase__	:      List[Any]     							=					ApproximateGELU(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					nn.ModuleList([]      )
          # project in
          self.net.append(UpperCamelCase_      )
          # project dropout
          self.net.append(nn.Dropout(UpperCamelCase_      )      )
          # project out
          self.net.append(nn.Linear(UpperCamelCase_     ,	UpperCamelCase_      )      )
          # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
          if final_dropout:
              self.net.append(nn.Dropout(UpperCamelCase_      )      )



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          for module in self.net:
              UpperCAmelCase__	:      Tuple     							=					module(UpperCamelCase_      )
          return hidden_states





class 				a			(					nn.Module      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_ = "none"      ):
          super().__init__()
          UpperCAmelCase__	:      str     							=					nn.Linear(UpperCamelCase_     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					approximate



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          if gate.device.type != "mps":
              return F.gelu(UpperCamelCase_     ,	approximate=self.approximate      )
          # mps: gelu is not implemented for float16
          return F.gelu(gate.to(dtype=torch.floataa      )     ,	approximate=self.approximate      ).to(dtype=gate.dtype      )



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					self.proj(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					self.gelu(UpperCamelCase_      )
          return hidden_states





class 				a			(					nn.Module      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      Tuple     							=					nn.Linear(UpperCamelCase_     ,	dim_out * 2      )



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          if gate.device.type != "mps":
              return F.gelu(UpperCamelCase_      )
          # mps: gelu is not implemented for float16
          return F.gelu(gate.to(dtype=torch.floataa      )      ).to(dtype=gate.dtype      )



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					self.proj(UpperCamelCase_      ).chunk(2     ,	dim=-1      )
          return hidden_states * self.gelu(UpperCamelCase_      )





class 				a			(					nn.Module      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      Dict     							=					nn.Linear(UpperCamelCase_     ,	UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      List[str]     							=					self.proj(UpperCamelCase_      )
          return x * torch.sigmoid(1.702 * x      )





class 				a			(					nn.Module      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      str     							=					nn.Embedding(UpperCamelCase_     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[int]     							=					nn.SiLU()
          UpperCAmelCase__	:      List[Any]     							=					nn.Linear(UpperCamelCase_     ,	embedding_dim * 2      )
          UpperCAmelCase__	:      List[Any]     							=					nn.LayerNorm(UpperCamelCase_     ,	elementwise_affine=UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Optional[int]     							=					self.linear(self.silu(self.emb(UpperCamelCase_      )      )      )
          UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					torch.chunk(UpperCamelCase_     ,	2      )
          UpperCAmelCase__	:      Tuple     							=					self.norm(UpperCamelCase_      ) * (1 + scale) + shift
          return x





class 				a			(					nn.Module      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          super().__init__()

          UpperCAmelCase__	:      int     							=					CombinedTimestepLabelEmbeddings(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      Tuple     							=					nn.SiLU()
          UpperCAmelCase__	:      Dict     							=					nn.Linear(UpperCamelCase_     ,	6 * embedding_dim     ,	bias=UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					nn.LayerNorm(UpperCamelCase_     ,	elementwise_affine=UpperCamelCase_     ,	eps=1E-6      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_=None      ):
          UpperCAmelCase__	:      Optional[Any]     							=					self.linear(self.silu(self.emb(UpperCamelCase_     ,	UpperCamelCase_     ,	hidden_dtype=UpperCamelCase_      )      )      )
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      Any     							=					emb.chunk(6     ,	dim=1      )
          UpperCAmelCase__	:      Optional[Any]     							=					self.norm(UpperCamelCase_      ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
          return x, gate_msa, shift_mlp, scale_mlp, gate_mlp





class 				a			(					nn.Module      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = 1E-5      ):
          super().__init__()
          UpperCAmelCase__	:      Union[str, Any]     							=					num_groups
          UpperCAmelCase__	:      int     							=					eps

          if act_fn is None:
              UpperCAmelCase__	:      List[str]     							=					None
          else:
              UpperCAmelCase__	:      Union[str, Any]     							=					get_activation(UpperCamelCase_      )

          UpperCAmelCase__	:      Dict     							=					nn.Linear(UpperCamelCase_     ,	out_dim * 2      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          if self.act:
              UpperCAmelCase__	:      List[Any]     							=					self.act(UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					self.linear(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					emb[:, :, None, None]
          UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					emb.chunk(2     ,	dim=1      )

          UpperCAmelCase__	:      Optional[int]     							=					F.group_norm(UpperCamelCase_     ,	self.num_groups     ,	eps=self.eps      )
          UpperCAmelCase__	:      Any     							=					x * (1 + scale) + shift
          return x








import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

__A							:			str         =			random.Random()





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=1.0							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE=None				)							-> Union[str, Any]:




    """simple docstring"""




    if rng is None:
        _A     =							global_rng

    _A     =							[]
    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				,     snake_case_				,     snake_case_=7				,     snake_case_=400				,     snake_case_=2000				,     snake_case_=2048				,     snake_case_=128				,     snake_case_=1				,     snake_case_=512				,     snake_case_=30				,     snake_case_=4_4100				,     ):
         _A     =							parent
         _A     =							batch_size
         _A     =							min_seq_length
         _A     =							max_seq_length
         _A     =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         _A     =							spectrogram_length
         _A     =							feature_size
         _A     =							num_audio_channels
         _A     =							hop_length
         _A     =							chunk_length
         _A     =							sampling_rate




     def        lowerCAmelCase__		(    self							):
         return {
             "spectrogram_length": self.spectrogram_length,
             "feature_size": self.feature_size,
             "num_audio_channels": self.num_audio_channels,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "sampling_rate": self.sampling_rate,
         }




     def        lowerCAmelCase__		(    self				,     snake_case_=False				,     snake_case_=False							):
         def _flatten(snake_case_							):
             return list(itertools.chain(*snake_case_							)							)

         if equal_length:
             _A     =							[floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
         else:
             # make sure that inputs increase in size
             _A     =							[
                 floats_list((x, self.feature_size)							)
                 for x in range(self.min_seq_length				,     self.max_seq_length				,     self.seq_length_diff							)
             ]
         if numpify:
             _A     =							[np.asarray(snake_case_							) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class      lowerCamelCase(						__snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			TvltFeatureExtractor




     def        lowerCAmelCase__		(    self							):
         _A     =							TvltFeatureExtractionTester(self							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)
         self.assertTrue(hasattr(snake_case_				,     'spectrogram_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'feature_size'							)							)
         self.assertTrue(hasattr(snake_case_				,     'num_audio_channels'							)							)
         self.assertTrue(hasattr(snake_case_				,     'hop_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'chunk_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'sampling_rate'							)							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
             _A     =							feat_extract_first.save_pretrained(snake_case_							)[0]
             check_json_file_has_correct_format(snake_case_							)
             _A     =							self.feature_extraction_class.from_pretrained(snake_case_							)

         _A     =							feat_extract_first.to_dict()
         _A     =							feat_extract_second.to_dict()
         _A     =							dict_first.pop('mel_filters'							)
         _A     =							dict_second.pop('mel_filters'							)
         self.assertTrue(np.allclose(snake_case_				,     snake_case_							)							)
         self.assertEqual(snake_case_				,     snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
             _A     =							os.path.join(snake_case_				,     'feat_extract.json'							)
             feat_extract_first.to_json_file(snake_case_							)
             _A     =							self.feature_extraction_class.from_json_file(snake_case_							)

         _A     =							feat_extract_first.to_dict()
         _A     =							feat_extract_second.to_dict()
         _A     =							dict_first.pop('mel_filters'							)
         _A     =							dict_second.pop('mel_filters'							)
         self.assertTrue(np.allclose(snake_case_				,     snake_case_							)							)
         self.assertEqual(snake_case_				,     snake_case_							)




     def        lowerCAmelCase__		(    self							):
         # Initialize feature_extractor
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

         # create three inputs of length 800, 1000, and 1200
         _A     =							[floats_list((1, x)							)[0] for x in range(800				,     1400				,     200							)]
         _A     =							[np.asarray(snake_case_							) for speech_input in speech_inputs]

         # Test not batched input
         _A     =							feature_extractor(np_speech_inputs[0]				,     return_tensors='np'				,     sampling_rate=4_4100							).audio_values

         self.assertTrue(encoded_audios.ndim == 4							)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

         # Test batched
         _A     =							feature_extractor(snake_case_				,     return_tensors='np'				,     sampling_rate=4_4100							).audio_values

         self.assertTrue(encoded_audios.ndim == 4							)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

         # Test audio masking
         _A     =							feature_extractor(
             snake_case_				,     return_tensors='np'				,     sampling_rate=4_4100				,     mask_audio=snake_case_							).audio_values

         self.assertTrue(encoded_audios.ndim == 4							)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

         # Test 2-D numpy arrays are batched.
         _A     =							[floats_list((1, x)							)[0] for x in (800, 800, 800)]
         _A     =							np.asarray(snake_case_							)
         _A     =							feature_extractor(snake_case_				,     return_tensors='np'				,     sampling_rate=4_4100							).audio_values
         self.assertTrue(encoded_audios.ndim == 4							)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)




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

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




     def        lowerCAmelCase__		(    self							):
         _A     =							self._load_datasamples(1							)
         _A     =							TvltFeatureExtractor()
         _A     =							feature_extractor(snake_case_				,     return_tensors='pt'							).audio_values

         self.assertEquals(audio_values.shape				,     (1, 1, 192, 128)							)

         _A     =							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]							)
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]				,     snake_case_				,     atol=1E-4							)							)





