
"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


a		:       Any     						=       logging.get_logger(__name__)  # pylint: disable=invalid-name

a		:       Optional[Any]     						=       '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		Dict							,		UpperCamelCase     :		str=8     )     ->						Optional[int]:
    a__		=			height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    a__		=			width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		str=512							,		UpperCamelCase     :		str=512     )     ->						Optional[int]:
    a__		=			pil_image.resize((w, h)							,		resample=Image.BICUBIC							,		reducing_gap=1     )
    a__		=			np.array(pil_image.convert('RGB'     )     )
    a__		=			arr.astype(np.floataa     ) / 127.5 - 1
    a__		=			np.transpose(UpperCamelCase							,		[2, 0, 1]     )
    a__		=			torch.from_numpy(UpperCamelCase     ).unsqueeze(0     )
    return image







class     lowercase(_lowercase       ):



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				)		->     Dict:







         """simple docstring"""



         super().__init__()

         self.register_modules(
             unet=__SCREAMING_SNAKE_CASE     ,				scheduler=__SCREAMING_SNAKE_CASE     ,				movq=__SCREAMING_SNAKE_CASE     ,				)
         a__		=			2 ** (len(self.movq.config.block_out_channels   ) - 1)



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         a__		=			min(int(num_inference_steps * strength   )     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			max(num_inference_steps - init_timestep     ,				0   )
         a__		=			self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=None   )		->     List[Any]:







         """simple docstring"""



         if not isinstance(__SCREAMING_SNAKE_CASE     ,				(torch.Tensor, PIL.Image.Image, list)   ):
             raise ValueError(
                 f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__SCREAMING_SNAKE_CASE   )}'   )

         a__		=			image.to(device=__SCREAMING_SNAKE_CASE     ,				dtype=__SCREAMING_SNAKE_CASE   )

         a__		=			batch_size * num_images_per_prompt

         if image.shape[1] == 4:
             a__		=			image

         else:
             if isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ) and len(__SCREAMING_SNAKE_CASE   ) != batch_size:
                 raise ValueError(
                     f'You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE   )}, but requested an effective batch'
                     f' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

             elif isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ):
                 a__		=			[
                     self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(__SCREAMING_SNAKE_CASE   )
                 ]
                 a__		=			torch.cat(__SCREAMING_SNAKE_CASE     ,				dim=0   )
             else:
                 a__		=			self.movq.encode(__SCREAMING_SNAKE_CASE   ).latent_dist.sample(__SCREAMING_SNAKE_CASE   )

             a__		=			self.movq.config.scaling_factor * init_latents

         a__		=			torch.cat([init_latents]     ,				dim=0   )

         a__		=			init_latents.shape
         a__		=			randn_tensor(__SCREAMING_SNAKE_CASE     ,				generator=__SCREAMING_SNAKE_CASE     ,				device=__SCREAMING_SNAKE_CASE     ,				dtype=__SCREAMING_SNAKE_CASE   )

         # get latents
         a__		=			self.scheduler.add_noise(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			init_latents

         return latents



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=0   )		->     int:







         """simple docstring"""



         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError('Please install accelerate via `pip install accelerate`'   )

         a__		=			torch.device(f'cuda:{gpu_id}'   )

         a__		=			[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                 cpu_offload(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=0   )		->     Optional[int]:







         """simple docstring"""



         if is_accelerate_available() and is_accelerate_version('>='     ,				'0.17.0.dev0'   ):
             from accelerate import cpu_offload_with_hook
         else:
             raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'   )

         a__		=			torch.device(f'cuda:{gpu_id}'   )

         if self.device.type != "cpu":
             self.to('cpu'     ,				silence_dtype_warnings=__SCREAMING_SNAKE_CASE   )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         a__		=			None
         for cpu_offloaded_model in [self.unet, self.movq]:
             a__	,     a__		=			cpu_offload_with_hook(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				prev_module_hook=__SCREAMING_SNAKE_CASE   )

         # We'll offload the last model manually.
         a__		=			hook



     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         if not hasattr(self.unet     ,				'_hf_hook'   ):
             return self.device
         for module in self.unet.modules():
             if (
                 hasattr(__SCREAMING_SNAKE_CASE     ,				'_hf_hook'   )
                 and hasattr(module._hf_hook     ,				'execution_device'   )
                 and module._hf_hook.execution_device is not None
             ):
                 return torch.device(module._hf_hook.execution_device   )
         return self.device




     @torch.no_grad()
     @replace_example_docstring(__SCREAMING_SNAKE_CASE   )
     def __call__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = 5_1_2     ,				__SCREAMING_SNAKE_CASE = 5_1_2     ,				__SCREAMING_SNAKE_CASE = 1_0_0     ,				__SCREAMING_SNAKE_CASE = 4.0     ,				__SCREAMING_SNAKE_CASE = 0.3     ,				__SCREAMING_SNAKE_CASE = 1     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = "pil"     ,				__SCREAMING_SNAKE_CASE = True     ,				)		->     List[str]:







         """simple docstring"""



         a__		=			self._execution_device

         a__		=			guidance_scale > 1.0

         if isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ):
             a__		=			torch.cat(__SCREAMING_SNAKE_CASE     ,				dim=0   )
         a__		=			image_embeds.shape[0]
         if isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ):
             a__		=			torch.cat(__SCREAMING_SNAKE_CASE     ,				dim=0   )

         if do_classifier_free_guidance:
             a__		=			image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE     ,				dim=0   )
             a__		=			negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE     ,				dim=0   )

         a__		=			torch.cat([negative_image_embeds, image_embeds]     ,				dim=0   ).to(dtype=self.unet.dtype     ,				device=__SCREAMING_SNAKE_CASE   )

         if not isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ):
             a__		=			[image]
         if not all(isinstance(__SCREAMING_SNAKE_CASE     ,				(PIL.Image.Image, torch.Tensor)   ) for i in image   ):
             raise ValueError(
                 f'Input is in incorrect format: {[type(__SCREAMING_SNAKE_CASE   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'   )

         a__		=			torch.cat([prepare_image(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ) for i in image]     ,				dim=0   )
         a__		=			image.to(dtype=image_embeds.dtype     ,				device=__SCREAMING_SNAKE_CASE   )

         a__		=			self.movq.encode(__SCREAMING_SNAKE_CASE   )['latents']
         a__		=			latents.repeat_interleave(__SCREAMING_SNAKE_CASE     ,				dim=0   )
         self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,				device=__SCREAMING_SNAKE_CASE   )
         a__	,     a__		=			self.get_timesteps(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			timesteps[:1].repeat(batch_size * num_images_per_prompt   )
         a__	,     a__		=			downscale_height_and_width(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				self.movq_scale_factor   )
         a__		=			self.prepare_latents(
             __SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				image_embeds.dtype     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE   )   ):
             # expand the latents if we are doing classifier free guidance
             a__		=			torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

             a__		=			{'image_embeds': image_embeds}
             a__		=			self.unet(
                 sample=__SCREAMING_SNAKE_CASE     ,				timestep=__SCREAMING_SNAKE_CASE     ,				encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,				added_cond_kwargs=__SCREAMING_SNAKE_CASE     ,				return_dict=__SCREAMING_SNAKE_CASE     ,				)[0]

             if do_classifier_free_guidance:
                 a__	,     a__		=			noise_pred.split(latents.shape[1]     ,				dim=1   )
                 a__	,     a__		=			noise_pred.chunk(2   )
                 a__	,     a__		=			variance_pred.chunk(2   )
                 a__		=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 a__		=			torch.cat([noise_pred, variance_pred_text]     ,				dim=1   )

             if not (
                 hasattr(self.scheduler.config     ,				'variance_type'   )
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                 a__	,     a__		=			noise_pred.split(latents.shape[1]     ,				dim=1   )

             # compute the previous noisy sample x_t -> x_t-1
             a__		=			self.scheduler.step(
                 __SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				generator=__SCREAMING_SNAKE_CASE     ,				)[0]

         # post-processing
         a__		=			self.movq.decode(__SCREAMING_SNAKE_CASE     ,				force_not_quantize=__SCREAMING_SNAKE_CASE   )['sample']

         if output_type not in ["pt", "np", "pil"]:
             raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'   )

         if output_type in ["np", "pil"]:
             a__		=			image * 0.5 + 0.5
             a__		=			image.clamp(0     ,				1   )
             a__		=			image.cpu().permute(0     ,				2     ,				3     ,				1   ).float().numpy()

         if output_type == "pil":
             a__		=			self.numpy_to_pil(__SCREAMING_SNAKE_CASE   )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE   )




"""simple docstring"""




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

import numpy as np
import torch

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


a		:       int     						=       logging.get_logger(__name__)







class     lowercase(_lowercase       ):
     __snake_case:	Any						=     ['input_features', 'is_longer']



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE=6_4     ,				__SCREAMING_SNAKE_CASE=4_8_0_0_0     ,				__SCREAMING_SNAKE_CASE=4_8_0     ,				__SCREAMING_SNAKE_CASE=1_0     ,				__SCREAMING_SNAKE_CASE=1_0_2_4     ,				__SCREAMING_SNAKE_CASE=0.0     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE = 0     ,				__SCREAMING_SNAKE_CASE = 1_4_0_0_0     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = "fusion"     ,				__SCREAMING_SNAKE_CASE = "repeatpad"     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     Union[str, Any]:







         """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     ,				)
         a__		=			top_db
         a__		=			truncation
         a__		=			padding
         a__		=			fft_window_size
         a__		=			(fft_window_size >> 1) + 1
         a__		=			hop_length
         a__		=			max_length_s
         a__		=			max_length_s * sampling_rate
         a__		=			sampling_rate
         a__		=			frequency_min
         a__		=			frequency_max
         a__		=			mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins     ,				num_mel_filters=__SCREAMING_SNAKE_CASE     ,				min_frequency=__SCREAMING_SNAKE_CASE     ,				max_frequency=__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				norm=__SCREAMING_SNAKE_CASE     ,				mel_scale='htk'     ,				)
         a__		=			mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins     ,				num_mel_filters=__SCREAMING_SNAKE_CASE     ,				min_frequency=__SCREAMING_SNAKE_CASE     ,				max_frequency=__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				norm='slaney'     ,				mel_scale='slaney'     ,				)



     def 			lowercase__  (							self   )		->     Dict[str, Any]:







         """simple docstring"""



         a__		=			copy.deepcopy(self.__dict__   )
         a__		=			self.__class__.__name__
         if "mel_filters" in output:
             del output["mel_filters"]
         if "mel_filters_slaney" in output:
             del output["mel_filters_slaney"]
         return output



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None   )		->     np.ndarray:







         """simple docstring"""



         a__		=			spectrogram(
             __SCREAMING_SNAKE_CASE     ,				window_function(self.fft_window_size     ,				'hann'   )     ,				frame_length=self.fft_window_size     ,				hop_length=self.hop_length     ,				power=2.0     ,				mel_filters=__SCREAMING_SNAKE_CASE     ,				log_mel='dB'     ,				)
         return log_mel_spectrogram.T



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			np.array_split(list(range(0     ,				total_frames - chunk_frames + 1   )   )     ,				3   )
         if len(ranges[1]   ) == 0:
             # if the audio is too short, we just use the first chunk
             a__		=			[0]
         if len(ranges[2]   ) == 0:
             # if the audio is too short, we just use the first chunk
             a__		=			[0]
         # randomly choose index for each part
         a__		=			np.random.choice(ranges[0]   )
         a__		=			np.random.choice(ranges[1]   )
         a__		=			np.random.choice(ranges[2]   )

         a__		=			mel[idx_front : idx_front + chunk_frames, :]
         a__		=			mel[idx_middle : idx_middle + chunk_frames, :]
         a__		=			mel[idx_back : idx_back + chunk_frames, :]

         a__		=			torch.tensor(mel[None, None, :]   )
         a__		=			torch.nn.functional.interpolate(
             __SCREAMING_SNAKE_CASE     ,				size=[chunk_frames, 6_4]     ,				mode='bilinear'     ,				align_corners=__SCREAMING_SNAKE_CASE   )
         a__		=			mel_shrink[0][0].numpy()
         a__		=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,				axis=0   )
         return mel_fusion



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     np.array:







         """simple docstring"""



         if waveform.shape[0] > max_length:
             if truncation == "rand_trunc":
                 a__		=			True
                 # random crop to max_length (for compatibility) -> this should be handled by self.pad
                 a__		=			len(__SCREAMING_SNAKE_CASE   ) - max_length
                 a__		=			np.random.randint(0     ,				overflow + 1   )
                 a__		=			waveform[idx : idx + max_length]
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters_slaney   )[None, :]
             elif truncation == "fusion":
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters   )
                 a__		=			max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                 a__		=			mel.shape[0]
                 if chunk_frames == total_frames:
                     # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                     # In this case, we just use the whole audio.
                     a__		=			np.stack([mel, mel, mel, mel]     ,				axis=0   )
                     a__		=			False
                 else:
                     a__		=			self._random_mel_fusion(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
                     a__		=			True
             else:
                 raise NotImplementedError(f'data_truncating {truncation} not implemented'   )

         else:
             a__		=			False
             # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
             if waveform.shape[0] < max_length:
                 if padding == "repeat":
                     a__		=			int(max_length / len(__SCREAMING_SNAKE_CASE   )   )
                     a__		=			np.stack(np.tile(__SCREAMING_SNAKE_CASE     ,				n_repeat + 1   )   )[:max_length]
                 if padding == "repeatpad":
                     a__		=			int(max_length / len(__SCREAMING_SNAKE_CASE   )   )
                     a__		=			np.stack(np.tile(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )   )
                 a__		=			np.pad(__SCREAMING_SNAKE_CASE     ,				(0, max_length - waveform.shape[0])     ,				mode='constant'     ,				constant_values=0   )

             if truncation == "fusion":
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters   )
                 a__		=			np.stack([input_mel, input_mel, input_mel, input_mel]     ,				axis=0   )
             else:
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters_slaney   )[None, :]

         return input_mel, longer




     def __call__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     BatchFeature:







         """simple docstring"""



         a__		=			truncation if truncation is not None else self.truncation
         a__		=			padding if padding else self.padding

         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.'   )

         a__		=			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}'   )
         a__		=			is_batched_numpy or (
             isinstance(__SCREAMING_SNAKE_CASE     ,				(list, tuple)   ) and (isinstance(raw_speech[0]     ,				(np.ndarray, tuple, list)   ))
         )

         if is_batched:
             a__		=			[np.asarray(__SCREAMING_SNAKE_CASE     ,				dtype=np.floataa   ) for speech in raw_speech]
         elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE     ,				np.ndarray   ):
             a__		=			np.asarray(__SCREAMING_SNAKE_CASE     ,				dtype=np.floataa   )
         elif isinstance(__SCREAMING_SNAKE_CASE     ,				np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
             a__		=			raw_speech.astype(np.floataa   )

         # always return batch
         if not is_batched:
             a__		=			[np.asarray(__SCREAMING_SNAKE_CASE   )]

         # convert to mel spectrogram, truncate and pad if needed.
         a__		=			[
             self._get_input_mel(__SCREAMING_SNAKE_CASE     ,				max_length if max_length else self.nb_max_samples     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
             for waveform in raw_speech
         ]

         a__		=			[]
         a__		=			[]
         for mel, longer in padded_inputs:
             input_mel.append(__SCREAMING_SNAKE_CASE   )
             is_longer.append(__SCREAMING_SNAKE_CASE   )

         if truncation == "fusion" and sum(__SCREAMING_SNAKE_CASE   ) == 0:
             # if no audio is longer than 10s, then randomly select one audio to be longer
             a__		=			np.random.randint(0     ,				len(__SCREAMING_SNAKE_CASE   )   )
             a__		=			True

         if isinstance(input_mel[0]     ,				__SCREAMING_SNAKE_CASE   ):
             a__		=			[np.asarray(__SCREAMING_SNAKE_CASE     ,				dtype=np.floataa   ) for feature in input_mel]

         # is_longer is a list of bool
         a__		=			[[longer] for longer in is_longer]

         a__		=			{'input_features': input_mel, 'is_longer': is_longer}
         a__		=			BatchFeature(__SCREAMING_SNAKE_CASE   )

         if return_tensors is not None:
             a__		=			input_features.convert_to_tensors(__SCREAMING_SNAKE_CASE   )

         return input_features


