





import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        _a(    __A	,   unittest.TestCase    ):
  lowerCamelCase__     :Optional[int]   				=		AudioLDMPipeline
  lowerCamelCase__     :List[Any]   				=		TEXT_TO_AUDIO_PARAMS
  lowerCamelCase__     :List[Any]   				=		TEXT_TO_AUDIO_BATCH_PARAMS
  lowerCamelCase__     :Dict   				=		frozenset(
      [
          'num_inference_steps',
          'num_waveforms_per_prompt',
          'generator',
          'latents',
          'output_type',
          'return_dict',
          'callback',
          'callback_steps',
      ]    )







  def        lowercase       (    self				)      ->      List[str]:


       '''simple docstring'''

       torch.manual_seed(0				)
       _snake_case       :					Union[str, Any]				       =   UNetaDConditionModel(
           block_out_channels=(3_2, 6_4) ,						layers_per_block=2 ,						sample_size=3_2 ,						in_channels=4 ,						out_channels=4 ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,						cross_attention_dim=(3_2, 6_4) ,						class_embed_type="simple_projection" ,						projection_class_embeddings_input_dim=3_2 ,						class_embeddings_concat=__snake_case ,						)
       _snake_case       :					int				       =   DDIMScheduler(
           beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule="scaled_linear" ,						clip_sample=__snake_case ,						set_alpha_to_one=__snake_case ,						)
       torch.manual_seed(0				)
       _snake_case       :					Dict				       =   AutoencoderKL(
           block_out_channels=[3_2, 6_4] ,						in_channels=1 ,						out_channels=1 ,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,						latent_channels=4 ,						)
       torch.manual_seed(0				)
       _snake_case       :					Tuple				       =   ClapTextConfig(
           bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=3_2 ,						intermediate_size=3_7 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_0_0_0 ,						projection_dim=3_2 ,						)
       _snake_case       :					Optional[int]				       =   ClapTextModelWithProjection(__snake_case				)
       _snake_case       :					Optional[Any]				       =   RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta" ,						model_max_length=7_7				)

       _snake_case       :					Dict				       =   SpeechTaHifiGanConfig(
           model_in_dim=8 ,						sampling_rate=1_6_0_0_0 ,						upsample_initial_channel=1_6 ,						upsample_rates=[2, 2] ,						upsample_kernel_sizes=[4, 4] ,						resblock_kernel_sizes=[3, 7] ,						resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] ,						normalize_before=__snake_case ,						)

       _snake_case       :					Dict				       =   SpeechTaHifiGan(__snake_case				)

       _snake_case       :					Dict				       =   {
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "vocoder": vocoder,
       }
       return components







  def        lowercase       (    self ,						__snake_case ,						__snake_case=0				)      ->      Union[str, Any]:


       '''simple docstring'''

       if str(__snake_case				).startswith("mps"				):
            _snake_case       :					List[Any]				       =   torch.manual_seed(__snake_case				)
       else:
            _snake_case       :					Tuple				       =   torch.Generator(device=__snake_case				).manual_seed(__snake_case				)
       _snake_case       :					List[str]				       =   {
           "prompt": "A hammer hitting a wooden surface",
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
       }
       return inputs







  def        lowercase       (    self				)      ->      Any:


       '''simple docstring'''

       _snake_case       :					Tuple				       =   "cpu"  # ensure determinism for the device-dependent torch.Generator

       _snake_case       :					Union[str, Any]				       =   self.get_dummy_components()
       _snake_case       :					Any				       =   AudioLDMPipeline(**__snake_case				)
       _snake_case       :					List[str]				       =   audioldm_pipe.to(__snake_case				)
       audioldm_pipe.set_progress_bar_config(disable=__snake_case				)

       _snake_case       :					List[str]				       =   self.get_dummy_inputs(__snake_case				)
       _snake_case       :					Any				       =   audioldm_pipe(**__snake_case				)
       _snake_case       :					Tuple				       =   output.audios[0]

       assert audio.ndim == 1
       assert len(__snake_case				) == 2_5_6

       _snake_case       :					Any				       =   audio[:1_0]
       _snake_case       :					Dict				       =   np.array(
           [-0.00_50, 0.00_50, -0.00_60, 0.00_33, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_33]				)

       assert np.abs(audio_slice - expected_slice				).max() < 1E-2







  def        lowercase       (    self				)      ->      Tuple:


       '''simple docstring'''

       _snake_case       :					Tuple				       =   self.get_dummy_components()
       _snake_case       :					Any				       =   AudioLDMPipeline(**__snake_case				)
       _snake_case       :					Union[str, Any]				       =   audioldm_pipe.to(__snake_case				)
       _snake_case       :					List[str]				       =   audioldm_pipe.to(__snake_case				)
       audioldm_pipe.set_progress_bar_config(disable=__snake_case				)

       _snake_case       :					Optional[int]				       =   self.get_dummy_inputs(__snake_case				)
       _snake_case       :					List[str]				       =   3 * [inputs["prompt"]]

       # forward
       _snake_case       :					Dict				       =   audioldm_pipe(**__snake_case				)
       _snake_case       :					Union[str, Any]				       =   output.audios[0]

       _snake_case       :					List[str]				       =   self.get_dummy_inputs(__snake_case				)
       _snake_case       :					Tuple				       =   3 * [inputs.pop("prompt"				)]

       _snake_case       :					int				       =   audioldm_pipe.tokenizer(
           __snake_case ,						padding="max_length" ,						max_length=audioldm_pipe.tokenizer.model_max_length ,						truncation=__snake_case ,						return_tensors="pt" ,						)
       _snake_case       :					Tuple				       =   text_inputs["input_ids"].to(__snake_case				)

       _snake_case       :					Dict				       =   audioldm_pipe.text_encoder(
           __snake_case ,						)
       _snake_case       :					Tuple				       =   prompt_embeds.text_embeds
       # additional L_2 normalization over each hidden-state
       _snake_case       :					List[Any]				       =   F.normalize(__snake_case ,						dim=-1				)

       _snake_case       :					Union[str, Any]				       =   prompt_embeds

       # forward
       _snake_case       :					int				       =   audioldm_pipe(**__snake_case				)
       _snake_case       :					Dict				       =   output.audios[0]

       assert np.abs(audio_a - audio_a				).max() < 1E-2







  def        lowercase       (    self				)      ->      List[str]:


       '''simple docstring'''

       _snake_case       :					int				       =   self.get_dummy_components()
       _snake_case       :					Optional[int]				       =   AudioLDMPipeline(**__snake_case				)
       _snake_case       :					Tuple				       =   audioldm_pipe.to(__snake_case				)
       _snake_case       :					Any				       =   audioldm_pipe.to(__snake_case				)
       audioldm_pipe.set_progress_bar_config(disable=__snake_case				)

       _snake_case       :					Tuple				       =   self.get_dummy_inputs(__snake_case				)
       _snake_case       :					Union[str, Any]				       =   3 * ["this is a negative prompt"]
       _snake_case       :					int				       =   negative_prompt
       _snake_case       :					Tuple				       =   3 * [inputs["prompt"]]

       # forward
       _snake_case       :					Tuple				       =   audioldm_pipe(**__snake_case				)
       _snake_case       :					Optional[Any]				       =   output.audios[0]

       _snake_case       :					List[str]				       =   self.get_dummy_inputs(__snake_case				)
       _snake_case       :					Dict				       =   3 * [inputs.pop("prompt"				)]

       _snake_case       :					List[Any]				       =   []
       for p in [prompt, negative_prompt]:
            _snake_case       :					Optional[Any]				       =   audioldm_pipe.tokenizer(
                __snake_case ,						padding="max_length" ,						max_length=audioldm_pipe.tokenizer.model_max_length ,						truncation=__snake_case ,						return_tensors="pt" ,						)
            _snake_case       :					List[Any]				       =   text_inputs["input_ids"].to(__snake_case				)

            _snake_case       :					List[Any]				       =   audioldm_pipe.text_encoder(
                __snake_case ,						)
            _snake_case       :					Union[str, Any]				       =   text_embeds.text_embeds
            # additional L_2 normalization over each hidden-state
            _snake_case       :					int				       =   F.normalize(__snake_case ,						dim=-1				)

            embeds.append(__snake_case				)

       _snake_case       :					int				       =   embeds

       # forward
       _snake_case       :					Any				       =   audioldm_pipe(**__snake_case				)
       _snake_case       :					Dict				       =   output.audios[0]

       assert np.abs(audio_a - audio_a				).max() < 1E-2







  def        lowercase       (    self				)      ->      Optional[int]:


       '''simple docstring'''

       _snake_case       :					Union[str, Any]				       =   "cpu"  # ensure determinism for the device-dependent torch.Generator
       _snake_case       :					Tuple				       =   self.get_dummy_components()
       _snake_case       :					str				       =   PNDMScheduler(skip_prk_steps=__snake_case				)
       _snake_case       :					Optional[int]				       =   AudioLDMPipeline(**__snake_case				)
       _snake_case       :					Tuple				       =   audioldm_pipe.to(__snake_case				)
       audioldm_pipe.set_progress_bar_config(disable=__snake_case				)

       _snake_case       :					Tuple				       =   self.get_dummy_inputs(__snake_case				)
       _snake_case       :					str				       =   "egg cracking"
       _snake_case       :					int				       =   audioldm_pipe(**__snake_case ,						negative_prompt=__snake_case				)
       _snake_case       :					Union[str, Any]				       =   output.audios[0]

       assert audio.ndim == 1
       assert len(__snake_case				) == 2_5_6

       _snake_case       :					List[str]				       =   audio[:1_0]
       _snake_case       :					Optional[int]				       =   np.array(
           [-0.00_51, 0.00_50, -0.00_60, 0.00_34, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_32]				)

       assert np.abs(audio_slice - expected_slice				).max() < 1E-2







  def        lowercase       (    self				)      ->      Dict:


       '''simple docstring'''

       _snake_case       :					List[str]				       =   "cpu"  # ensure determinism for the device-dependent torch.Generator
       _snake_case       :					Optional[Any]				       =   self.get_dummy_components()
       _snake_case       :					List[Any]				       =   PNDMScheduler(skip_prk_steps=__snake_case				)
       _snake_case       :					Union[str, Any]				       =   AudioLDMPipeline(**__snake_case				)
       _snake_case       :					int				       =   audioldm_pipe.to(__snake_case				)
       audioldm_pipe.set_progress_bar_config(disable=__snake_case				)

       _snake_case       :					List[Any]				       =   "A hammer hitting a wooden surface"

       # test num_waveforms_per_prompt=1 (default)
       _snake_case       :					Dict				       =   audioldm_pipe(__snake_case ,						num_inference_steps=2				).audios

       assert audios.shape == (1, 2_5_6)

       # test num_waveforms_per_prompt=1 (default) for batch of prompts
       _snake_case       :					int				       =   2
       _snake_case       :					Optional[int]				       =   audioldm_pipe([prompt] * batch_size ,						num_inference_steps=2				).audios

       assert audios.shape == (batch_size, 2_5_6)

       # test num_waveforms_per_prompt for single prompt
       _snake_case       :					List[Any]				       =   2
       _snake_case       :					List[str]				       =   audioldm_pipe(__snake_case ,						num_inference_steps=2 ,						num_waveforms_per_prompt=__snake_case				).audios

       assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

       # test num_waveforms_per_prompt for batch of prompts
       _snake_case       :					Optional[int]				       =   2
       _snake_case       :					str				       =   audioldm_pipe(
           [prompt] * batch_size ,						num_inference_steps=2 ,						num_waveforms_per_prompt=__snake_case				).audios

       assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)







  def        lowercase       (    self				)      ->      int:


       '''simple docstring'''

       _snake_case       :					List[str]				       =   "cpu"  # ensure determinism for the device-dependent torch.Generator
       _snake_case       :					int				       =   self.get_dummy_components()
       _snake_case       :					Union[str, Any]				       =   AudioLDMPipeline(**__snake_case				)
       _snake_case       :					Any				       =   audioldm_pipe.to(__snake_case				)
       audioldm_pipe.set_progress_bar_config(disable=__snake_case				)
       _snake_case       :					Tuple				       =   audioldm_pipe.vocoder.config.sampling_rate

       _snake_case       :					str				       =   self.get_dummy_inputs(__snake_case				)
       _snake_case       :					List[Any]				       =   audioldm_pipe(audio_length_in_s=0.0_16 ,						**__snake_case				)
       _snake_case       :					List[str]				       =   output.audios[0]

       assert audio.ndim == 1
       assert len(__snake_case				) / vocoder_sampling_rate == 0.0_16

       _snake_case       :					Tuple				       =   audioldm_pipe(audio_length_in_s=0.0_32 ,						**__snake_case				)
       _snake_case       :					Union[str, Any]				       =   output.audios[0]

       assert audio.ndim == 1
       assert len(__snake_case				) / vocoder_sampling_rate == 0.0_32







  def        lowercase       (    self				)      ->      Optional[int]:


       '''simple docstring'''

       _snake_case       :					Union[str, Any]				       =   self.get_dummy_components()
       _snake_case       :					Any				       =   AudioLDMPipeline(**__snake_case				)
       _snake_case       :					List[str]				       =   audioldm_pipe.to(__snake_case				)
       audioldm_pipe.set_progress_bar_config(disable=__snake_case				)

       _snake_case       :					int				       =   ["hey"]

       _snake_case       :					int				       =   audioldm_pipe(__snake_case ,						num_inference_steps=1				)
       _snake_case       :					List[Any]				       =   output.audios.shape
       assert audio_shape == (1, 2_5_6)

       _snake_case       :					Optional[Any]				       =   audioldm_pipe.vocoder.config
       config.model_in_dim *= 2
       _snake_case       :					str				       =   SpeechTaHifiGan(__snake_case				).to(__snake_case				)
       _snake_case       :					Any				       =   audioldm_pipe(__snake_case ,						num_inference_steps=1				)
       _snake_case       :					Tuple				       =   output.audios.shape
       # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
       assert audio_shape == (1, 2_5_6)







  def        lowercase       (    self				)      ->      Any:


       '''simple docstring'''

       self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__snake_case				)







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


       '''simple docstring'''

       self._test_inference_batch_single_identical(test_mean_pixel_difference=__snake_case				)







  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available() ,						reason="XFormers attention is only available with CUDA and `xformers` installed" ,						)
  def        lowercase       (    self				)      ->      Dict:


       '''simple docstring'''

       self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__snake_case				)






@slow
class        _a(    unittest.TestCase    ):







  def        lowercase       (    self				)      ->      Optional[Any]:


       '''simple docstring'''

       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  def        lowercase       (    self ,						__snake_case ,						__snake_case="cpu" ,						__snake_case=torch.floataa ,						__snake_case=0				)      ->      Any:


       '''simple docstring'''

       _snake_case       :					int				       =   torch.Generator(device=__snake_case				).manual_seed(__snake_case				)
       _snake_case       :					Dict				       =   np.random.RandomState(__snake_case				).standard_normal((1, 8, 1_2_8, 1_6)				)
       _snake_case       :					Any				       =   torch.from_numpy(__snake_case				).to(device=__snake_case ,						dtype=__snake_case				)
       _snake_case       :					List[Any]				       =   {
           "prompt": "A hammer hitting a wooden surface",
           "latents": latents,
           "generator": generator,
           "num_inference_steps": 3,
           "guidance_scale": 2.5,
       }
       return inputs







  def        lowercase       (    self				)      ->      Dict:


       '''simple docstring'''

       _snake_case       :					List[Any]				       =   AudioLDMPipeline.from_pretrained("cvssp/audioldm"				)
       _snake_case       :					str				       =   audioldm_pipe.to(__snake_case				)
       audioldm_pipe.set_progress_bar_config(disable=__snake_case				)

       _snake_case       :					Tuple				       =   self.get_inputs(__snake_case				)
       _snake_case       :					Optional[Any]				       =   2_5
       _snake_case       :					str				       =   audioldm_pipe(**__snake_case				).audios[0]

       assert audio.ndim == 1
       assert len(__snake_case				) == 8_1_9_2_0

       _snake_case       :					Any				       =   audio[7_7_2_3_0:7_7_2_4_0]
       _snake_case       :					str				       =   np.array(
           [-0.48_84, -0.46_07, 0.00_23, 0.50_07, 0.58_96, 0.51_51, 0.38_13, -0.02_08, -0.36_87, -0.43_15]				)
       _snake_case       :					List[str]				       =   np.abs(expected_slice - audio_slice				).max()
       assert max_diff < 1E-2







  def        lowercase       (    self				)      ->      Union[str, Any]:


       '''simple docstring'''

       _snake_case       :					Tuple				       =   AudioLDMPipeline.from_pretrained("cvssp/audioldm"				)
       _snake_case       :					Any				       =   LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config				)
       _snake_case       :					int				       =   audioldm_pipe.to(__snake_case				)
       audioldm_pipe.set_progress_bar_config(disable=__snake_case				)

       _snake_case       :					Any				       =   self.get_inputs(__snake_case				)
       _snake_case       :					Optional[Any]				       =   audioldm_pipe(**__snake_case				).audios[0]

       assert audio.ndim == 1
       assert len(__snake_case				) == 8_1_9_2_0

       _snake_case       :					str				       =   audio[2_7_7_8_0:2_7_7_9_0]
       _snake_case       :					Any				       =   np.array([-0.21_31, -0.08_73, -0.01_24, -0.01_89, 0.05_69, 0.13_73, 0.18_83, 0.28_86, 0.32_97, 0.22_12]				)
       _snake_case       :					Tuple				       =   np.abs(expected_slice - audio_slice				).max()
       assert max_diff < 3E-2






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCAmelCase :Tuple     =    logging.get_logger(__name__)






@dataclass
class        _a:
		lowerCamelCase__     :str   				=		field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()    )}    )
		lowerCamelCase__     :str   				=		field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}    )
		lowerCamelCase__     :int   				=		field(
		    default=128	,   metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }	,   )
		lowerCamelCase__     :bool   				=		field(
		    default=__A	,   metadata={'help': 'Overwrite the cached training and evaluation sets'}    )







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.task_name.lower()






class        _a(    __A    ):
		lowerCamelCase__     :Optional[Any]   				=		'train'
		lowerCamelCase__     :List[str]   				=		'dev'
		lowerCamelCase__     :Any   				=		'test'






class        _a(    __A    ):
		lowerCamelCase__     :GlueDataTrainingArguments
		lowerCamelCase__     :str
		lowerCamelCase__     :List[InputFeatures]







		def __init__(    self ,						__snake_case ,						__snake_case ,						__snake_case = None ,						__snake_case = Split.train ,						__snake_case = None ,						)      ->      Any:


							'''simple docstring'''

							warnings.warn(
							    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
							    "library. You can have a look at this example script for pointers: "
							    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py" ,						__snake_case ,						)
							_snake_case       :					Optional[Any]				       =   args
							_snake_case       :					Optional[int]				       =   glue_processors[args.task_name]()
							_snake_case       :					int				       =   glue_output_modes[args.task_name]
							if isinstance(__snake_case ,						__snake_case				):
												try:
																	_snake_case       :					Tuple				       =   Split[mode]
												except KeyError:
																	raise KeyError("mode is not a valid split name"				)
        # Load data features from cache or dataset file
							_snake_case       :					List[str]				       =   os.path.join(
							    cache_dir if cache_dir is not None else args.data_dir ,						f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}""" ,						)
							_snake_case       :					List[Any]				       =   self.processor.get_labels()
							if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
							    "RobertaTokenizer",
							    "RobertaTokenizerFast",
							    "XLMRobertaTokenizer",
							    "BartTokenizer",
							    "BartTokenizerFast",
							):
												# HACK(label indices are swapped in RoBERTa pretrained model)
												_snake_case       , _snake_case       :					int				       =   label_list[2], label_list[1]
							_snake_case       :					str				       =   label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							_snake_case       :					Optional[Any]				       =   cached_features_file + ".lock"
							with FileLock(__snake_case				):
												if os.path.exists(__snake_case				) and not args.overwrite_cache:
																	_snake_case       :					List[str]				       =   time.time()
																	_snake_case       :					str				       =   torch.load(__snake_case				)
																	logger.info(
																	    f"""Loading features from cached file {cached_features_file} [took %.3f s]""" ,						time.time() - start				)
												else:
																	logger.info(f"""Creating features from dataset file at {args.data_dir}"""				)

																	if mode == Split.dev:
																						_snake_case       :					List[Any]				       =   self.processor.get_dev_examples(args.data_dir				)
																	elif mode == Split.test:
																						_snake_case       :					List[Any]				       =   self.processor.get_test_examples(args.data_dir				)
																	else:
																						_snake_case       :					str				       =   self.processor.get_train_examples(args.data_dir				)
																	if limit_length is not None:
																						_snake_case       :					Tuple				       =   examples[:limit_length]
																	_snake_case       :					List[str]				       =   glue_convert_examples_to_features(
																	    __snake_case ,						__snake_case ,						max_length=args.max_seq_length ,						label_list=__snake_case ,						output_mode=self.output_mode ,						)
																	_snake_case       :					Optional[Any]				       =   time.time()
																	torch.save(self.features ,						__snake_case				)
																	# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																	logger.info(
																	    f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""				)







		def __len__(    self				)      ->      List[str]:


							'''simple docstring'''

							return len(self.features				)







		def __getitem__(    self ,						__snake_case				)      ->      InputFeatures:


							'''simple docstring'''

							return self.features[i]







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							return self.label_list