


"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCamelCase__				:Tuple			   =	logging.get_logger(__name__)

UpperCamelCase__				:Tuple			   =	{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
UpperCamelCase__				:Tuple			   =	[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]





def 				A_       ( snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Tuple					     =	{}
			with open(lowerCAmelCase_       ,					'''r'''		) as file:
						for line_number, line in enumerate(lowerCAmelCase_		):
									_UpperCamelCase					:int					     =	line.strip()
									if line:
												_UpperCamelCase					:List[Any]					     =	line.split()
												_UpperCamelCase					:List[str]					     =	line_number
												_UpperCamelCase					:Optional[int]					     =	words[0]
												_UpperCamelCase					:List[str]					     =	value
			return result





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  int:
			for attribute in key.split('''.'''		):
						_UpperCamelCase					:Dict					     =	getattr(lowerCAmelCase_       ,					lowerCAmelCase_		)

			_UpperCamelCase					:Union[str, Any]					     =	None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(lowerCAmelCase_		):
									_UpperCamelCase					:Any					     =	PARAM_MAPPING[full_name.split('''.'''		)[-1]]
									_UpperCamelCase					:List[Any]					     =	'''param'''

			if weight_type is not None and weight_type != "param":
						_UpperCamelCase					:Tuple					     =	getattr(lowerCAmelCase_       ,					lowerCAmelCase_		).shape
			elif weight_type is not None and weight_type == "param":
						_UpperCamelCase					:Union[str, Any]					     =	hf_pointer
						for attribute in hf_param_name.split('''.'''		):
									_UpperCamelCase					:List[Any]					     =	getattr(lowerCAmelCase_       ,					lowerCAmelCase_		)
						_UpperCamelCase					:List[str]					     =	shape_pointer.shape

						# let's reduce dimension
						_UpperCamelCase					:Optional[int]					     =	value[0]
			else:
						_UpperCamelCase					:List[Any]					     =	hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
						    f" {value.shape} for {full_name}"		)

			if weight_type == "weight":
						_UpperCamelCase					:int					     =	value
			elif weight_type == "weight_g":
						_UpperCamelCase					:Dict					     =	value
			elif weight_type == "weight_v":
						_UpperCamelCase					:Dict					     =	value
			elif weight_type == "bias":
						_UpperCamelCase					:Any					     =	value
			elif weight_type == "param":
						for attribute in hf_param_name.split('''.'''		):
									_UpperCamelCase					:Optional[Any]					     =	getattr(lowerCAmelCase_       ,					lowerCAmelCase_		)
						_UpperCamelCase					:Union[str, Any]					     =	value
			else:
						_UpperCamelCase					:Dict					     =	value

			logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."		)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  str:
			_UpperCamelCase					:Tuple					     =	None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(lowerCAmelCase_		):
									_UpperCamelCase					:str					     =	PARAM_MAPPING[full_name.split('''.'''		)[-1]]
									_UpperCamelCase					:int					     =	'''param'''

			if weight_type is not None and weight_type != "param":
						_UpperCamelCase					:Union[str, Any]					     =	'''.'''.join([key, weight_type]		)
			elif weight_type is not None and weight_type == "param":
						_UpperCamelCase					:List[str]					     =	'''.'''.join([key, hf_param_name]		)
			else:
						_UpperCamelCase					:Optional[Any]					     =	key

			_UpperCamelCase					:str					     =	value if '''lm_head''' in full_key else value[0]


UpperCamelCase__				:str			   =	{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__=None       ,					snake_case__=None		)					->  Optional[Any]:
			_UpperCamelCase					:Tuple					     =	False
			for key, mapped_key in MAPPING.items():
						_UpperCamelCase					:List[str]					     =	'''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
						if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
									_UpperCamelCase					:Optional[int]					     =	True
									if "*" in mapped_key:
												_UpperCamelCase					:str					     =	name.split(lowerCAmelCase_		)[0].split('''.'''		)[-2]
												_UpperCamelCase					:Any					     =	mapped_key.replace('''*'''       ,					lowerCAmelCase_		)
									if "weight_g" in name:
												_UpperCamelCase					:Optional[Any]					     =	'''weight_g'''
									elif "weight_v" in name:
												_UpperCamelCase					:Any					     =	'''weight_v'''
									elif "bias" in name:
												_UpperCamelCase					:Union[str, Any]					     =	'''bias'''
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												_UpperCamelCase					:Optional[Any]					     =	'''weight'''
									else:
												_UpperCamelCase					:Tuple					     =	None
									if hf_dict is not None:
												rename_dict(lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_		)
									else:
												set_recursively(lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_		)
									return is_used
			return is_used





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Union[str, Any]:
			_UpperCamelCase					:Dict					     =	[]
			_UpperCamelCase					:Dict					     =	fairseq_model.state_dict()

			_UpperCamelCase					:Union[str, Any]					     =	hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCamelCase					:str					     =	False
						if "conv_layers" in name:
									load_conv_layer(
									    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					hf_model.config.feat_extract_norm == '''group'''       ,					)
									_UpperCamelCase					:List[str]					     =	True
						else:
									_UpperCamelCase					:Tuple					     =	load_wavaveca_layer(lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_		)
						if not is_used:
									unused_weights.append(lowerCAmelCase_		)

			logger.warning(f"Unused weights: {unused_weights}"		)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Tuple					     =	full_name.split('''conv_layers.'''		)[-1]
			_UpperCamelCase					:Any					     =	name.split('''.'''		)
			_UpperCamelCase					:Optional[Any]					     =	int(items[0]		)
			_UpperCamelCase					:List[str]					     =	int(items[1]		)

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    f"{full_name} has size {value.shape}, but"
												    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."		)
									_UpperCamelCase					:List[Any]					     =	value
									logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    f"{full_name} has size {value.shape}, but"
												    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."		)
									_UpperCamelCase					:Tuple					     =	value
									logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    f"{full_name} has size {value.shape}, but"
												    f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."		)
									_UpperCamelCase					:Dict					     =	value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    f"{full_name} has size {value.shape}, but"
												    f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."		)
									_UpperCamelCase					:int					     =	value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
			else:
						unused_weights.append(lowerCAmelCase_		)





@torch.no_grad()
def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__=None       ,					snake_case__=None       ,					snake_case__=True       ,					snake_case__=False		)					->  Dict:
			if config_path is not None:
						_UpperCamelCase					:Tuple					     =	WavaVecaConfig.from_pretrained(lowerCAmelCase_		)
			else:
						_UpperCamelCase					:List[Any]					     =	WavaVecaConfig()

			if is_seq_class:
						_UpperCamelCase					:List[Any]					     =	read_txt_into_dict(lowerCAmelCase_		)
						_UpperCamelCase					:Optional[Any]					     =	idalabel
						_UpperCamelCase					:List[Any]					     =	WavaVecaForSequenceClassification(lowerCAmelCase_		)
						_UpperCamelCase					:str					     =	WavaVecaFeatureExtractor(
						    feature_size=1       ,					sampling_rate=1_60_00       ,					padding_value=0       ,					do_normalize=lowerCAmelCase_       ,					return_attention_mask=lowerCAmelCase_       ,					)
						feature_extractor.save_pretrained(lowerCAmelCase_		)

			elif is_finetuned:
						if dict_path:
									_UpperCamelCase					:Dict					     =	Dictionary.load(lowerCAmelCase_		)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									_UpperCamelCase					:int					     =	target_dict.pad_index
									_UpperCamelCase					:str					     =	target_dict.bos_index
									_UpperCamelCase					:int					     =	target_dict.eos_index
									_UpperCamelCase					:Tuple					     =	len(target_dict.symbols		)
									_UpperCamelCase					:Any					     =	os.path.join(lowerCAmelCase_       ,					'''vocab.json'''		)
									if not os.path.isdir(lowerCAmelCase_		):
												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase_		)		)
												return
									os.makedirs(lowerCAmelCase_       ,					exist_ok=lowerCAmelCase_		)
									_UpperCamelCase					:int					     =	target_dict.indices

									# fairseq has the <pad> and <s> switched
									_UpperCamelCase					:str					     =	0
									_UpperCamelCase					:List[str]					     =	1
									with open(lowerCAmelCase_       ,					'''w'''       ,					encoding='''utf-8'''		) as vocab_handle:
												json.dump(lowerCAmelCase_       ,					lowerCAmelCase_		)
									_UpperCamelCase					:Tuple					     =	WavaVecaCTCTokenizer(
									    lowerCAmelCase_       ,					unk_token=target_dict.unk_word       ,					pad_token=target_dict.pad_word       ,					bos_token=target_dict.bos_word       ,					eos_token=target_dict.eos_word       ,					word_delimiter_token='''|'''       ,					do_lower_case=lowerCAmelCase_       ,					)
									_UpperCamelCase					:Optional[Any]					     =	True if config.feat_extract_norm == '''layer''' else False
									_UpperCamelCase					:Optional[Any]					     =	WavaVecaFeatureExtractor(
									    feature_size=1       ,					sampling_rate=1_60_00       ,					padding_value=0       ,					do_normalize=lowerCAmelCase_       ,					return_attention_mask=lowerCAmelCase_       ,					)
									_UpperCamelCase					:int					     =	WavaVecaProcessor(feature_extractor=lowerCAmelCase_       ,					tokenizer=lowerCAmelCase_		)
									processor.save_pretrained(lowerCAmelCase_		)

						_UpperCamelCase					:Optional[Any]					     =	WavaVecaForCTC(lowerCAmelCase_		)
			else:
						_UpperCamelCase					:Tuple					     =	WavaVecaForPreTraining(lowerCAmelCase_		)

			if is_finetuned or is_seq_class:
						_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:str					     =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]       ,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		)}		)
			else:
						_UpperCamelCase					:List[Any]					     =	argparse.Namespace(task='''audio_pretraining'''		)
						_UpperCamelCase					:Dict					     =	fairseq.tasks.setup_task(lowerCAmelCase_		)

						_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:str					     =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,					task=lowerCAmelCase_		)

			_UpperCamelCase					:Union[str, Any]					     =	model[0].eval()

			recursively_load_weights(lowerCAmelCase_       ,					lowerCAmelCase_       ,					not is_finetuned		)

			hf_wavavec.save_pretrained(lowerCAmelCase_		)


if __name__ == "__main__":
					UpperCamelCase__				:List[Any]			   =	argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					parser.add_argument(
					    """--is_seq_class""",
					    action="""store_true""",
					    help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
					)
					UpperCamelCase__				:int			   =	parser.parse_args()

					UpperCamelCase__				:str			   =	not args.not_finetuned and not args.is_seq_class
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.dict_path,
					    is_finetuned,
					    args.is_seq_class,
					)






"""simple docstring"""






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

snake_case   					=				random.Random()




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=1.0  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None	)						->				List[str]:
  if rng is None:
    _snake_case   	=      global_rng

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

  return values



class     UpperCAmelCase    (	unittest.TestCase  ):



   def __init__(       self    :	Tuple							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Optional[Any]=7							,       __lowerCamelCase    :	List[Any]=4_0_0							,       __lowerCamelCase    :	Any=2_0_0_0							,       __lowerCamelCase    :	Any=2_0_4_8							,       __lowerCamelCase    :	Any=1_2_8							,       __lowerCamelCase    :	Any=1							,       __lowerCamelCase    :	Optional[int]=5_1_2							,       __lowerCamelCase    :	Tuple=3_0							,       __lowerCamelCase    :	List[Any]=4_4_1_0_0							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      min_seq_length
     _snake_case   	=      max_seq_length
     _snake_case   	=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     _snake_case   	=      spectrogram_length
     _snake_case   	=      feature_size
     _snake_case   	=      num_audio_channels
     _snake_case   	=      hop_length
     _snake_case   	=      chunk_length
     _snake_case   	=      sampling_rate



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     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    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Any=False							,       __lowerCamelCase    :	int=False				):



     """simple docstring"""
     def _flatten(__lowerCamelCase    :	List[str]				):
       return list(itertools.chain(*__lowerCamelCase				)				)

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



@require_torch
@require_torchaudio
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Tuple           =  TvltFeatureExtractor



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      TvltFeatureExtractionTester(self				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''spectrogram_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''feature_size'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''num_audio_channels'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''hop_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''chunk_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''sampling_rate'''				)				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

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

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



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

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

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



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     # Initialize feature_extractor
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

     # create three inputs of length 800, 1000, and 1200
     _snake_case   	=      [floats_list((1, x)				)[0] for x in range(8_0_0							,       1_4_0_0							,       2_0_0				)]
     _snake_case   	=      [np.asarray(__lowerCamelCase				) for speech_input in speech_inputs]

     # Test not batched input
     _snake_case   	=      feature_extractor(np_speech_inputs[0]							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).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
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).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
     _snake_case   	=      feature_extractor(
         __lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0							,       mask_audio=__lowerCamelCase				).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.
     _snake_case   	=      [floats_list((1, x)				)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
     _snake_case   	=      np.asarray(__lowerCamelCase				)
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).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    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      load_dataset('''hf-internal-testing/librispeech_asr_dummy'''							,       '''clean'''							,       split='''validation'''				)
     # automatic decoding with librispeech
     _snake_case   	=      ds.sort('''id'''				).select(range(__lowerCamelCase				)				)[:num_samples]['''audio''']

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





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self._load_datasamples(1				)
     _snake_case   	=      TvltFeatureExtractor()
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''pt'''				).audio_values

     self.assertEquals(audio_values.shape							,       (1, 1, 1_9_2, 1_2_8)				)

     _snake_case   	=      torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]				)
     self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]							,       __lowerCamelCase							,       atol=1E-4				)				)





