


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=1						)     ->			Any:







 """simple docstring"""


 if n_shave_prefix_segments >= 0:
  return ".".join(path.split('''.'''						)[n_shave_prefix_segments:]						)
 else:
  return ".".join(path.split('''.'''						)[:n_shave_prefix_segments]						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=0						)     ->			str:







 """simple docstring"""


 _lowercase							      =[]
 for old_item in old_list:
  _lowercase							      =old_item.replace('''in_layers.0'''     ,						'''norm1'''						)
  _lowercase							      =new_item.replace('''in_layers.2'''     ,						'''conv1'''						)

  _lowercase							      =new_item.replace('''out_layers.0'''     ,						'''norm2'''						)
  _lowercase							      =new_item.replace('''out_layers.3'''     ,						'''conv2'''						)

  _lowercase							      =new_item.replace('''emb_layers.1'''     ,						'''time_emb_proj'''						)
  _lowercase							      =new_item.replace('''skip_connection'''     ,						'''conv_shortcut'''						)

  _lowercase							      =shave_segments(__snake_case     ,						n_shave_prefix_segments=__snake_case						)

  mapping.append({'''old''': old_item, '''new''': new_item}						)

 return mapping
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=0						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =[]
 for old_item in old_list:
  _lowercase							      =old_item

  _lowercase							      =new_item.replace('''norm.weight'''     ,						'''group_norm.weight'''						)
  _lowercase							      =new_item.replace('''norm.bias'''     ,						'''group_norm.bias'''						)

  _lowercase							      =new_item.replace('''proj_out.weight'''     ,						'''proj_attn.weight'''						)
  _lowercase							      =new_item.replace('''proj_out.bias'''     ,						'''proj_attn.bias'''						)

  _lowercase							      =shave_segments(__snake_case     ,						n_shave_prefix_segments=__snake_case						)

  mapping.append({'''old''': old_item, '''new''': new_item}						)

 return mapping
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=None						)     ->			Any:







 """simple docstring"""


 assert isinstance(__snake_case     ,						__snake_case						), "Paths should be a list of dicts containing 'old' and 'new' keys."

 # Splits the attention layers into three variables.
 if attention_paths_to_split is not None:
  for path, path_map in attention_paths_to_split.items():
   _lowercase							      =old_checkpoint[path]
   _lowercase							      =old_tensor.shape[0] // 3

   _lowercase							      =(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

   _lowercase							      =old_tensor.shape[0] // config['''num_head_channels'''] // 3

   _lowercase							      =old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
   _lowercase      ,    _lowercase      ,    _lowercase							      =old_tensor.split(channels // num_heads     ,						dim=1						)

   _lowercase							      =query.reshape(__snake_case						)
   _lowercase							      =key.reshape(__snake_case						)
   _lowercase							      =value.reshape(__snake_case						)

 for path in paths:
  _lowercase							      =path['''new''']

  # These have already been assigned
  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  # Global renaming happens here
  _lowercase							      =new_path.replace('''middle_block.0'''     ,						'''mid_block.resnets.0'''						)
  _lowercase							      =new_path.replace('''middle_block.1'''     ,						'''mid_block.attentions.0'''						)
  _lowercase							      =new_path.replace('''middle_block.2'''     ,						'''mid_block.resnets.1'''						)

  if additional_replacements is not None:
   for replacement in additional_replacements:
    _lowercase							      =new_path.replace(replacement['''old''']     ,						replacement['''new''']						)

        # proj_attn.weight has to be converted from conv 1D to linear
  if "proj_attn.weight" in new_path:
   _lowercase							      =old_checkpoint[path['''old''']][:, :, 0]
  else:
   _lowercase							      =old_checkpoint[path['''old''']]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      ={}

 _lowercase							      =checkpoint['''time_embed.0.weight''']
 _lowercase							      =checkpoint['''time_embed.0.bias''']
 _lowercase							      =checkpoint['''time_embed.2.weight''']
 _lowercase							      =checkpoint['''time_embed.2.bias''']

 _lowercase							      =checkpoint['''input_blocks.0.0.weight''']
 _lowercase							      =checkpoint['''input_blocks.0.0.bias''']

 _lowercase							      =checkpoint['''out.0.weight''']
 _lowercase							      =checkpoint['''out.0.bias''']
 _lowercase							      =checkpoint['''out.2.weight''']
 _lowercase							      =checkpoint['''out.2.bias''']

 # Retrieves the keys for the input blocks only
 _lowercase							      =len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''input_blocks''' in layer}						)
 _lowercase							      ={
     layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
     for layer_id in range(__snake_case						)
 }

 # Retrieves the keys for the middle blocks only
 _lowercase							      =len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''middle_block''' in layer}						)
 _lowercase							      ={
     layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
     for layer_id in range(__snake_case						)
 }

 # Retrieves the keys for the output blocks only
 _lowercase							      =len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''output_blocks''' in layer}						)
 _lowercase							      ={
     layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
     for layer_id in range(__snake_case						)
 }

 for i in range(1     ,						__snake_case						):
  _lowercase							      =(i - 1) // (config['''num_res_blocks'''] + 1)
  _lowercase							      =(i - 1) % (config['''num_res_blocks'''] + 1)

  _lowercase							      =[key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
  _lowercase							      =[key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

  if F"input_blocks.{i}.0.op.weight" in checkpoint:
   _lowercase							      =checkpoint[
       F"input_blocks.{i}.0.op.weight"
   ]
   _lowercase							      =checkpoint[
       F"input_blocks.{i}.0.op.bias"
   ]
   continue

  _lowercase							      =renew_resnet_paths(__snake_case						)
  _lowercase							      ={'''old''': F"input_blocks.{i}.0", '''new''': F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
  _lowercase							      ={'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
  assign_to_checkpoint(
      __snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path, resnet_op]     ,						config=__snake_case						)

  if len(__snake_case						):
   _lowercase							      =renew_attention_paths(__snake_case						)
   _lowercase							      ={
       '''old''': F"input_blocks.{i}.1",
       '''new''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
   }
   _lowercase							      ={
       F"input_blocks.{i}.1.qkv.bias": {
           '''key''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
           '''query''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
           '''value''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
       },
       F"input_blocks.{i}.1.qkv.weight": {
           '''key''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
           '''query''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
           '''value''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
       },
   }
   assign_to_checkpoint(
       __snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path]     ,						attention_paths_to_split=__snake_case     ,						config=__snake_case     ,						)

 _lowercase							      =middle_blocks[0]
 _lowercase							      =middle_blocks[1]
 _lowercase							      =middle_blocks[2]

 _lowercase							      =renew_resnet_paths(__snake_case						)
 assign_to_checkpoint(__snake_case     ,						__snake_case     ,						__snake_case     ,						config=__snake_case						)

 _lowercase							      =renew_resnet_paths(__snake_case						)
 assign_to_checkpoint(__snake_case     ,						__snake_case     ,						__snake_case     ,						config=__snake_case						)

 _lowercase							      =renew_attention_paths(__snake_case						)
 _lowercase							      ={
     '''middle_block.1.qkv.bias''': {
         '''key''': '''mid_block.attentions.0.key.bias''',
         '''query''': '''mid_block.attentions.0.query.bias''',
         '''value''': '''mid_block.attentions.0.value.bias''',
     },
     '''middle_block.1.qkv.weight''': {
         '''key''': '''mid_block.attentions.0.key.weight''',
         '''query''': '''mid_block.attentions.0.query.weight''',
         '''value''': '''mid_block.attentions.0.value.weight''',
     },
 }
 assign_to_checkpoint(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						attention_paths_to_split=__snake_case     ,						config=__snake_case						)

 for i in range(__snake_case						):
  _lowercase							      =i // (config['''num_res_blocks'''] + 1)
  _lowercase							      =i % (config['''num_res_blocks'''] + 1)
  _lowercase							      =[shave_segments(__snake_case     ,						2						) for name in output_blocks[i]]
  _lowercase							      ={}

  for layer in output_block_layers:
   _lowercase      ,    _lowercase							      =layer.split('''.'''						)[0], shave_segments(__snake_case     ,						1						)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(__snake_case						)
   else:
    _lowercase							      =[layer_name]

  if len(__snake_case						) > 1:
   _lowercase							      =[key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
   _lowercase							      =[key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

   _lowercase							      =renew_resnet_paths(__snake_case						)
   _lowercase							      =renew_resnet_paths(__snake_case						)

   _lowercase							      ={'''old''': F"output_blocks.{i}.0", '''new''': F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
   assign_to_checkpoint(__snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path]     ,						config=__snake_case						)

   if ["conv.weight", "conv.bias"] in output_block_list.values():
    _lowercase							      =list(output_block_list.values()						).index(['''conv.weight''', '''conv.bias''']						)
    _lowercase							      =checkpoint[
        F"output_blocks.{i}.{index}.conv.weight"
    ]
    _lowercase							      =checkpoint[
        F"output_blocks.{i}.{index}.conv.bias"
    ]

    # Clear attentions as they have been attributed above.
    if len(__snake_case						) == 2:
     _lowercase							      =[]

   if len(__snake_case						):
    _lowercase							      =renew_attention_paths(__snake_case						)
    _lowercase							      ={
        '''old''': F"output_blocks.{i}.1",
        '''new''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
    }
    _lowercase							      ={
        F"output_blocks.{i}.1.qkv.bias": {
            '''key''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
            '''query''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
            '''value''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
        },
        F"output_blocks.{i}.1.qkv.weight": {
            '''key''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
            '''query''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
            '''value''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
        },
    }
    assign_to_checkpoint(
        __snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path]     ,						attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions						) else None     ,						config=__snake_case     ,						)
  else:
   _lowercase							      =renew_resnet_paths(__snake_case     ,						n_shave_prefix_segments=1						)
   for path in resnet_0_paths:
    _lowercase							      ='''.'''.join(['''output_blocks''', str(__snake_case						), path['''old''']]						)
    _lowercase							      ='''.'''.join(['''up_blocks''', str(__snake_case						), '''resnets''', str(__snake_case						), path['''new''']]						)

    _lowercase							      =checkpoint[old_path]

 return new_checkpoint


if __name__ == "__main__":
  UpperCAmelCase__   	=							argparse.ArgumentParser()

  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )

  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The config json file corresponding to the architecture.''',
  )

  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

  UpperCAmelCase__   	=							parser.parse_args()

  UpperCAmelCase__   	=							torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    UpperCAmelCase__   	=							json.loads(f.read())

  UpperCAmelCase__   	=							convert_ldm_checkpoint(checkpoint, config)

  if "ldm" in config:
    del config["ldm"]

  UpperCAmelCase__   	=							UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    UpperCAmelCase__   	=							DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
    UpperCAmelCase__   	=							VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

    UpperCAmelCase__   	=							LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)








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

SCREAMING_SNAKE_CASE_						   =       random.Random()



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=1.0 ,						_SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None	) ->   Tuple:


      '''simple docstring'''


      if rng is None:
            SCREAMING_SNAKE_CASE		=						global_rng

      SCREAMING_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   ):



     '''simple docstring'''

     def __init__(							self						:    List[Any]						,lowerCamelCase__						:    str						,lowerCamelCase__						:    Optional[int]=7						,lowerCamelCase__						:    Optional[Any]=400						,lowerCamelCase__						:    List[str]=2000						,lowerCamelCase__						:    List[str]=2048						,lowerCamelCase__						:    Any=128						,lowerCamelCase__						:    List[str]=1						,lowerCamelCase__						:    str=512						,lowerCamelCase__						:    Optional[Any]=30						,lowerCamelCase__						:    Tuple=44100						,)     ->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						min_seq_length
           SCREAMING_SNAKE_CASE		=						max_seq_length
           SCREAMING_SNAKE_CASE		=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           SCREAMING_SNAKE_CASE		=						spectrogram_length
           SCREAMING_SNAKE_CASE		=						feature_size
           SCREAMING_SNAKE_CASE		=						num_audio_channels
           SCREAMING_SNAKE_CASE		=						hop_length
           SCREAMING_SNAKE_CASE		=						chunk_length
           SCREAMING_SNAKE_CASE		=						sampling_rate






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Optional[int]:




           '''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 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]						,lowerCamelCase__						:    Tuple=False						,lowerCamelCase__						:    Union[str, Any]=False       )     ->		str:




           '''simple docstring'''


           def _flatten(lowerCamelCase__						:    List[Any]       ):
                 return list(itertools.chain(*lowerCamelCase__       )       )

           if equal_length:
                 SCREAMING_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
                 SCREAMING_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:
                 SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__       ) for x in speech_inputs]
           return speech_inputs

@require_torch
@require_torchaudio
class 		UpperCamelCase__ (      lowerCAmelCase_							,      unittest.TestCase   ):



     '''simple docstring'''

     __snake_case    :	List[Any]			=			TvltFeatureExtractor






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TvltFeatureExtractionTester(self       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Any:




           '''simple docstring'''


           SCREAMING_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 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict       )

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

           SCREAMING_SNAKE_CASE		=						feat_extract_first.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_second.to_dict()
           SCREAMING_SNAKE_CASE		=						dict_first.pop("""mel_filters"""       )
           SCREAMING_SNAKE_CASE		=						dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__       )       )
           self.assertEqual(lowerCamelCase__						,lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 SCREAMING_SNAKE_CASE		=						os.path.join(lowerCamelCase__						,"""feat_extract.json"""       )
                 feat_extract_first.to_json_file(lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						self.feature_extraction_class.from_json_file(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						feat_extract_first.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_second.to_dict()
           SCREAMING_SNAKE_CASE		=						dict_first.pop("""mel_filters"""       )
           SCREAMING_SNAKE_CASE		=						dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__       )       )
           self.assertEqual(lowerCamelCase__						,lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict       )

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

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						feature_extractor(np_speech_inputs[0]						,return_tensors="""np"""						,sampling_rate=44100       ).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
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""						,sampling_rate=44100       ).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
           SCREAMING_SNAKE_CASE		=						feature_extractor(
               lowerCamelCase__						,return_tensors="""np"""						,sampling_rate=44100						,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.
           SCREAMING_SNAKE_CASE		=						[floats_list((1, x)       )[0] for x in (800, 800, 800)]
           SCREAMING_SNAKE_CASE		=						np.asarray(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""						,sampling_rate=44100       ).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 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,lowerCamelCase__						:    str       )     ->		Any:




           '''simple docstring'''


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

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._load_datasamples(1       )
           SCREAMING_SNAKE_CASE		=						TvltFeatureExtractor()
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""pt"""       ).audio_values

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

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


