


import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin



@skip_mps
class    UpperCAmelCase_	(							_a,     _a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     Tuple							=					IFPipeline
       __UpperCamelCase      :     Dict							=					TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
       __UpperCamelCase      :     int							=					TEXT_TO_IMAGE_BATCH_PARAMS
       __UpperCamelCase      :     Optional[int]							=					PipelineTesterMixin.required_optional_params - {"latents"}






       def  _lowercase   (     self						):




              """simple docstring"""




              return self._get_dummy_components()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=0						):




              """simple docstring"""




              if str(__SCREAMING_SNAKE_CASE						).startswith('''mps'''						):
                     UpperCamelCase    :	List[Any]   	=		torch.manual_seed(__SCREAMING_SNAKE_CASE						)
              else:
                     UpperCamelCase    :	Any   	=		torch.Generator(device=__SCREAMING_SNAKE_CASE						).manual_seed(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		{
                  '''prompt''': '''A painting of a squirrel eating a burger''',
                  '''generator''': generator,
                  '''num_inference_steps''': 2,
                  '''output_type''': '''numpy''',
              }

              return inputs






       def  _lowercase   (     self						):




              """simple docstring"""




              self._test_save_load_optional_components()






       @unittest.skipIf(torch_device != '''cuda''' ,					reason='''float16 requires CUDA'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              super().test_save_load_floataa(expected_max_diff=1e-1						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self._test_attention_slicing_forward_pass(expected_max_diff=1e-2						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self._test_save_load_local()






       def  _lowercase   (     self						):




              """simple docstring"""




              self._test_inference_batch_single_identical(
                  expected_max_diff=1e-2 ,					)






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




              """simple docstring"""




              self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)




@slow
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




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






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0''' ,					variant='''fp16''' ,					torch_dtype=torch.floataa						)

              UpperCamelCase    :	Optional[int]   	=		IFSuperResolutionPipeline.from_pretrained(
                  '''DeepFloyd/IF-II-L-v1.0''' ,					variant='''fp16''' ,					torch_dtype=torch.floataa ,					text_encoder=__SCREAMING_SNAKE_CASE ,					tokenizer=__SCREAMING_SNAKE_CASE						)

              # pre compute text embeddings and remove T5 to save memory

              pipe_a.text_encoder.to('''cuda'''						)

              UpperCamelCase	, UpperCamelCase    :	Tuple   	=		pipe_a.encode_prompt('''anime turtle''' ,					device='''cuda'''						)

              del pipe_a.tokenizer
              del pipe_a.text_encoder
              gc.collect()

              UpperCamelCase    :	List[str]   	=		None
              UpperCamelCase    :	int   	=		None

              pipe_a.enable_model_cpu_offload()
              pipe_a.enable_model_cpu_offload()

              pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
              pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

              self._test_if(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              pipe_a.remove_all_hooks()
              pipe_a.remove_all_hooks()

              # img2img

              UpperCamelCase    :	Tuple   	=		IFImgaImgPipeline(**pipe_a.components						)
              UpperCamelCase    :	Optional[int]   	=		IFImgaImgSuperResolutionPipeline(**pipe_a.components						)

              pipe_a.enable_model_cpu_offload()
              pipe_a.enable_model_cpu_offload()

              pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
              pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

              self._test_if_imgaimg(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              pipe_a.remove_all_hooks()
              pipe_a.remove_all_hooks()

              # inpainting

              UpperCamelCase    :	List[str]   	=		IFInpaintingPipeline(**pipe_a.components						)
              UpperCamelCase    :	Dict   	=		IFInpaintingSuperResolutionPipeline(**pipe_a.components						)

              pipe_a.enable_model_cpu_offload()
              pipe_a.enable_model_cpu_offload()

              pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
              pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

              self._test_if_inpainting(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              _start_torch_memory_measurement()

              UpperCamelCase    :	List[Any]   	=		torch.Generator(device='''cpu'''						).manual_seed(0						)
              UpperCamelCase    :	List[Any]   	=		pipe_a(
                  prompt_embeds=__SCREAMING_SNAKE_CASE ,					negative_prompt_embeds=__SCREAMING_SNAKE_CASE ,					num_inference_steps=2 ,					generator=__SCREAMING_SNAKE_CASE ,					output_type='''np''' ,					)

              UpperCamelCase    :	Any   	=		output.images[0]

              assert image.shape == (64, 64, 3)

              UpperCamelCase    :	Dict   	=		torch.cuda.max_memory_allocated()
              assert mem_bytes < 13 * 10**9

              UpperCamelCase    :	str   	=		load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'''						)
              assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              # pipeline 2

              _start_torch_memory_measurement()

              UpperCamelCase    :	Optional[Any]   	=		torch.Generator(device='''cpu'''						).manual_seed(0						)

              UpperCamelCase    :	Any   	=		floats_tensor((1, 3, 64, 64) ,					rng=random.Random(0						)						).to(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	str   	=		pipe_a(
                  prompt_embeds=__SCREAMING_SNAKE_CASE ,					negative_prompt_embeds=__SCREAMING_SNAKE_CASE ,					image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					num_inference_steps=2 ,					output_type='''np''' ,					)

              UpperCamelCase    :	Any   	=		output.images[0]

              assert image.shape == (256, 256, 3)

              UpperCamelCase    :	Union[str, Any]   	=		torch.cuda.max_memory_allocated()
              assert mem_bytes < 4 * 10**9

              UpperCamelCase    :	Union[str, Any]   	=		load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''						)
              assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              _start_torch_memory_measurement()

              UpperCamelCase    :	int   	=		floats_tensor((1, 3, 64, 64) ,					rng=random.Random(0						)						).to(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	int   	=		torch.Generator(device='''cpu'''						).manual_seed(0						)

              UpperCamelCase    :	List[str]   	=		pipe_a(
                  prompt_embeds=__SCREAMING_SNAKE_CASE ,					negative_prompt_embeds=__SCREAMING_SNAKE_CASE ,					image=__SCREAMING_SNAKE_CASE ,					num_inference_steps=2 ,					generator=__SCREAMING_SNAKE_CASE ,					output_type='''np''' ,					)

              UpperCamelCase    :	Optional[int]   	=		output.images[0]

              assert image.shape == (64, 64, 3)

              UpperCamelCase    :	Union[str, Any]   	=		torch.cuda.max_memory_allocated()
              assert mem_bytes < 10 * 10**9

              UpperCamelCase    :	Any   	=		load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''						)
              assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              # pipeline 2

              _start_torch_memory_measurement()

              UpperCamelCase    :	Tuple   	=		torch.Generator(device='''cpu'''						).manual_seed(0						)

              UpperCamelCase    :	Dict   	=		floats_tensor((1, 3, 256, 256) ,					rng=random.Random(0						)						).to(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		floats_tensor((1, 3, 64, 64) ,					rng=random.Random(0						)						).to(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[Any]   	=		pipe_a(
                  prompt_embeds=__SCREAMING_SNAKE_CASE ,					negative_prompt_embeds=__SCREAMING_SNAKE_CASE ,					image=__SCREAMING_SNAKE_CASE ,					original_image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					num_inference_steps=2 ,					output_type='''np''' ,					)

              UpperCamelCase    :	Tuple   	=		output.images[0]

              assert image.shape == (256, 256, 3)

              UpperCamelCase    :	str   	=		torch.cuda.max_memory_allocated()
              assert mem_bytes < 4 * 10**9

              UpperCamelCase    :	List[str]   	=		load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'''						)
              assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              _start_torch_memory_measurement()

              UpperCamelCase    :	Optional[int]   	=		floats_tensor((1, 3, 64, 64) ,					rng=random.Random(0						)						).to(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		floats_tensor((1, 3, 64, 64) ,					rng=random.Random(1						)						).to(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	int   	=		torch.Generator(device='''cpu'''						).manual_seed(0						)
              UpperCamelCase    :	Dict   	=		pipe_a(
                  prompt_embeds=__SCREAMING_SNAKE_CASE ,					negative_prompt_embeds=__SCREAMING_SNAKE_CASE ,					image=__SCREAMING_SNAKE_CASE ,					mask_image=__SCREAMING_SNAKE_CASE ,					num_inference_steps=2 ,					generator=__SCREAMING_SNAKE_CASE ,					output_type='''np''' ,					)

              UpperCamelCase    :	Tuple   	=		output.images[0]

              assert image.shape == (64, 64, 3)

              UpperCamelCase    :	int   	=		torch.cuda.max_memory_allocated()
              assert mem_bytes < 10 * 10**9

              UpperCamelCase    :	Optional[int]   	=		load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'''						)
              assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              # pipeline 2

              _start_torch_memory_measurement()

              UpperCamelCase    :	Any   	=		torch.Generator(device='''cpu'''						).manual_seed(0						)

              UpperCamelCase    :	Union[str, Any]   	=		floats_tensor((1, 3, 64, 64) ,					rng=random.Random(0						)						).to(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		floats_tensor((1, 3, 256, 256) ,					rng=random.Random(0						)						).to(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Tuple   	=		floats_tensor((1, 3, 256, 256) ,					rng=random.Random(1						)						).to(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Tuple   	=		pipe_a(
                  prompt_embeds=__SCREAMING_SNAKE_CASE ,					negative_prompt_embeds=__SCREAMING_SNAKE_CASE ,					image=__SCREAMING_SNAKE_CASE ,					mask_image=__SCREAMING_SNAKE_CASE ,					original_image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					num_inference_steps=2 ,					output_type='''np''' ,					)

              UpperCamelCase    :	Optional[Any]   	=		output.images[0]

              assert image.shape == (256, 256, 3)

              UpperCamelCase    :	Tuple   	=		torch.cuda.max_memory_allocated()
              assert mem_bytes < 4 * 10**9

              UpperCamelCase    :	List[Any]   	=		load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'''						)
              assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)




def      a	(							):






       """simple docstring"""





       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__UpperCAmelCase : Dict 			=				logging.get_logger(__name__)



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Any							=					["input_features"]






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=80 ,					__SCREAMING_SNAKE_CASE=16_000 ,					__SCREAMING_SNAKE_CASE=160 ,					__SCREAMING_SNAKE_CASE=30 ,					__SCREAMING_SNAKE_CASE=400 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=False ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(
                  feature_size=__SCREAMING_SNAKE_CASE ,					sampling_rate=__SCREAMING_SNAKE_CASE ,					padding_value=__SCREAMING_SNAKE_CASE ,					return_attention_mask=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	List[str]   	=		n_fft
              UpperCamelCase    :	Dict   	=		hop_length
              UpperCamelCase    :	Dict   	=		chunk_length
              UpperCamelCase    :	List[str]   	=		chunk_length * sampling_rate
              UpperCamelCase    :	Dict   	=		self.n_samples // hop_length
              UpperCamelCase    :	str   	=		sampling_rate
              UpperCamelCase    :	Union[str, Any]   	=		mel_filter_bank(
                  num_frequency_bins=1 + n_fft // 2 ,					num_mel_filters=__SCREAMING_SNAKE_CASE ,					min_frequency=0.0 ,					max_frequency=8_000.0 ,					sampling_rate=__SCREAMING_SNAKE_CASE ,					norm='''slaney''' ,					mel_scale='''slaney''' ,					)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		spectrogram(
                  __SCREAMING_SNAKE_CASE ,					window_function(self.n_fft ,					'''hann'''						) ,					frame_length=self.n_fft ,					hop_length=self.hop_length ,					power=2.0 ,					mel_filters=self.mel_filters ,					log_mel='''log10''' ,					)
              UpperCamelCase    :	int   	=		log_spec[:, :-1]
              UpperCamelCase    :	int   	=		np.maximum(__SCREAMING_SNAKE_CASE ,					log_spec.max() - 8.0						)
              UpperCamelCase    :	Any   	=		(log_spec + 4.0) / 4.0
              return log_spec






       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def  _lowercase   (     __SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 0.0						):




              """simple docstring"""




              if attention_mask is not None:
                     UpperCamelCase    :	List[Any]   	=		np.array(__SCREAMING_SNAKE_CASE ,					np.intaa						)
                     UpperCamelCase    :	Optional[Any]   	=		[]

                     for vector, length in zip(__SCREAMING_SNAKE_CASE ,					attention_mask.sum(-1						)						):
                            UpperCamelCase    :	Optional[Any]   	=		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
                            if length < normed_slice.shape[0]:
                                   UpperCamelCase    :	Optional[int]   	=		padding_value

                            normed_input_values.append(__SCREAMING_SNAKE_CASE						)
              else:
                     UpperCamelCase    :	Union[str, Any]   	=		[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

              return normed_input_values






       def __call__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = "max_length" ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              if sampling_rate is not None:
                     if sampling_rate != self.sampling_rate:
                            raise ValueError(
                                f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                                f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                                f""" was sampled with {self.sampling_rate} and not {sampling_rate}."""						)
              else:
                     logger.warning(
                         '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                         '''Failing to do so can result in silent errors that might be hard to debug.'''						)

              UpperCamelCase    :	Tuple   	=		isinstance(__SCREAMING_SNAKE_CASE ,					np.ndarray						) and len(raw_speech.shape						) > 1
              if is_batched_numpy and len(raw_speech.shape						) > 2:
                     raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""						)
              UpperCamelCase    :	Union[str, Any]   	=		is_batched_numpy or (
                  isinstance(__SCREAMING_SNAKE_CASE ,					(list, tuple)						) and (isinstance(raw_speech[0] ,					(np.ndarray, tuple, list)						))
              )

              if is_batched:
                     UpperCamelCase    :	List[Any]   	=		[np.asarray([speech] ,					dtype=np.floataa						).T for speech in raw_speech]
              elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE ,					np.ndarray						):
                     UpperCamelCase    :	int   	=		np.asarray(__SCREAMING_SNAKE_CASE ,					dtype=np.floataa						)
              elif isinstance(__SCREAMING_SNAKE_CASE ,					np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
                     UpperCamelCase    :	Union[str, Any]   	=		raw_speech.astype(np.floataa						)

              # always return batch
              if not is_batched:
                     UpperCamelCase    :	Optional[int]   	=		[np.asarray([raw_speech]						).T]

              UpperCamelCase    :	Optional[int]   	=		BatchFeature({'''input_features''': raw_speech}						)

              # convert into correct format for padding

              UpperCamelCase    :	Optional[Any]   	=		self.pad(
                  __SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE ,					max_length=max_length if max_length else self.n_samples ,					truncation=__SCREAMING_SNAKE_CASE ,					pad_to_multiple_of=__SCREAMING_SNAKE_CASE ,					return_attention_mask=return_attention_mask or do_normalize ,					)

              # zero-mean and unit-variance normalization
              if do_normalize:
                     UpperCamelCase    :	Optional[Any]   	=		self.zero_mean_unit_var_norm(
                         padded_inputs['''input_features'''] ,					attention_mask=padded_inputs['''attention_mask'''] ,					padding_value=self.padding_value ,					)
                     UpperCamelCase    :	List[str]   	=		np.stack(padded_inputs['''input_features'''] ,					axis=0						)

              # make sure list is in array format
              UpperCamelCase    :	Dict   	=		padded_inputs.get('''input_features'''						).transpose(2 ,					0 ,					1						)

              UpperCamelCase    :	Tuple   	=		[self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE						) for waveform in input_features[0]]

              if isinstance(input_features[0] ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Optional[int]   	=		[np.asarray(__SCREAMING_SNAKE_CASE ,					dtype=np.floataa						) for feature in input_features]
              else:
                     UpperCamelCase    :	Dict   	=		input_features

              if return_attention_mask:
                     # rescale from sample (48000) to feature (3000)
                     UpperCamelCase    :	Union[str, Any]   	=		padded_inputs['''attention_mask'''][:, :: self.hop_length]

              if return_tensors is not None:
                     UpperCamelCase    :	Dict   	=		padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE						)

              return padded_inputs






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		copy.deepcopy(self.__dict__						)
              UpperCamelCase    :	List[str]   	=		self.__class__.__name__
              if "mel_filters" in output:
                     del output["mel_filters"]
              return output

