




"""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   =				logging.get_logger(__name__)



class      a__				(	__magic_name__   ):
			lowercase_    =	["input_features", "is_longer"]


			def __init__(      self      :    Any      ,  UpperCamelCase_      :    List[Any]=64      ,  UpperCamelCase_      :    int=48000      ,  UpperCamelCase_      :    Optional[int]=480      ,  UpperCamelCase_      :    Optional[int]=10      ,  UpperCamelCase_      :    Union[str, Any]=1024      ,  UpperCamelCase_      :    Optional[int]=0.0      ,  UpperCamelCase_      :    Optional[int]=False      ,  UpperCamelCase_      :    float = 0      ,  UpperCamelCase_      :    float = 14000      ,  UpperCamelCase_      :    int = None      ,  UpperCamelCase_      :    str = "fusion"      ,  UpperCamelCase_      :    str = "repeatpad"      ,  **UpperCamelCase_      :    Optional[Any]      ,  ):


						"""simple docstring"""




						super().__init__(
						    feature_size=UpperCamelCase_      ,  sampling_rate=UpperCamelCase_      ,  padding_value=UpperCamelCase_      ,  return_attention_mask=UpperCamelCase_      ,  **UpperCamelCase_      ,  )
						__UpperCAmelCase	: Union[str, Any]		=					top_db
						__UpperCAmelCase	: str		=					truncation
						__UpperCAmelCase	: str		=					padding
						__UpperCAmelCase	: Union[str, Any]		=					fft_window_size
						__UpperCAmelCase	: str		=					(fft_window_size >> 1) + 1
						__UpperCAmelCase	: Optional[int]		=					hop_length
						__UpperCAmelCase	: int		=					max_length_s
						__UpperCAmelCase	: List[Any]		=					max_length_s * sampling_rate
						__UpperCAmelCase	: Optional[Any]		=					sampling_rate
						__UpperCAmelCase	: Tuple		=					frequency_min
						__UpperCAmelCase	: str		=					frequency_max
						__UpperCAmelCase	: Dict		=					mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins      ,  num_mel_filters=UpperCamelCase_      ,  min_frequency=UpperCamelCase_      ,  max_frequency=UpperCamelCase_      ,  sampling_rate=UpperCamelCase_      ,  norm=UpperCamelCase_      ,  mel_scale="htk"      ,  )
						__UpperCAmelCase	: Union[str, Any]		=					mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins      ,  num_mel_filters=UpperCamelCase_      ,  min_frequency=UpperCamelCase_      ,  max_frequency=UpperCamelCase_      ,  sampling_rate=UpperCamelCase_      ,  norm="slaney"      ,  mel_scale="slaney"      ,  )


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					copy.deepcopy(self.__dict__)
						__UpperCAmelCase	: str		=					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       a_     (      self      :    Dict      ,  UpperCamelCase_      :    np.array      ,  UpperCamelCase_      :    Optional[np.array] = None):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					spectrogram(
						    UpperCamelCase_      ,  window_function(self.fft_window_size      ,  "hann")      ,  frame_length=self.fft_window_size      ,  hop_length=self.hop_length      ,  power=2.0      ,  mel_filters=UpperCamelCase_      ,  log_mel="dB"      ,  )
						return log_mel_spectrogram.T


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    List[str]      ,  UpperCamelCase_      :    Any      ,  UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					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
									__UpperCAmelCase	: Any		=					[0]
						if len(ranges[2]) == 0:
									# if the audio is too short, we just use the first chunk
									__UpperCAmelCase	: int		=					[0]
						# randomly choose index for each part
						__UpperCAmelCase	: Union[str, Any]		=					np.random.choice(ranges[0])
						__UpperCAmelCase	: List[str]		=					np.random.choice(ranges[1])
						__UpperCAmelCase	: Any		=					np.random.choice(ranges[2])

						__UpperCAmelCase	: Any		=					mel[idx_front : idx_front + chunk_frames, :]
						__UpperCAmelCase	: List[Any]		=					mel[idx_middle : idx_middle + chunk_frames, :]
						__UpperCAmelCase	: List[Any]		=					mel[idx_back : idx_back + chunk_frames, :]

						__UpperCAmelCase	: Union[str, Any]		=					torch.tensor(mel[None, None, :])
						__UpperCAmelCase	: List[Any]		=					torch.nn.functional.interpolate(
						    UpperCamelCase_      ,  size=[chunk_frames, 64]      ,  mode="bilinear"      ,  align_corners=UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					mel_shrink[0][0].numpy()
						__UpperCAmelCase	: str		=					np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]      ,  axis=0)
						return mel_fusion


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    np.array      ,  UpperCamelCase_      :    Optional[int]      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Optional[Any]):


						"""simple docstring"""




						if waveform.shape[0] > max_length:
									if truncation == "rand_trunc":
												__UpperCAmelCase	: Dict		=					True
												# random crop to max_length (for compatibility) -> this should be handled by self.pad
												__UpperCAmelCase	: Optional[Any]		=					len(UpperCamelCase_) - max_length
												__UpperCAmelCase	: Optional[int]		=					np.random.randint(0      ,  overflow + 1)
												__UpperCAmelCase	: str		=					waveform[idx : idx + max_length]
												__UpperCAmelCase	: Union[str, Any]		=					self._np_extract_fbank_features(UpperCamelCase_      ,  self.mel_filters_slaney)[None, :]
									elif truncation == "fusion":
												__UpperCAmelCase	: Dict		=					self._np_extract_fbank_features(UpperCamelCase_      ,  self.mel_filters)
												__UpperCAmelCase	: List[str]		=					max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
												__UpperCAmelCase	: List[str]		=					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.
															__UpperCAmelCase	: List[Any]		=					np.stack([mel, mel, mel, mel]      ,  axis=0)
															__UpperCAmelCase	: str		=					False
												else:
															__UpperCAmelCase	: List[Any]		=					self._random_mel_fusion(UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_)
															__UpperCAmelCase	: str		=					True
									else:
												raise NotImplementedError(F"data_truncating {truncation} not implemented")

						else:
									__UpperCAmelCase	: int		=					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":
															__UpperCAmelCase	: Any		=					int(max_length / len(UpperCamelCase_))
															__UpperCAmelCase	: Optional[int]		=					np.stack(np.tile(UpperCamelCase_      ,  n_repeat + 1))[:max_length]
												if padding == "repeatpad":
															__UpperCAmelCase	: Any		=					int(max_length / len(UpperCamelCase_))
															__UpperCAmelCase	: Any		=					np.stack(np.tile(UpperCamelCase_      ,  UpperCamelCase_))
												__UpperCAmelCase	: Optional[Any]		=					np.pad(UpperCamelCase_      ,  (0, max_length - waveform.shape[0])      ,  mode="constant"      ,  constant_values=0)

									if truncation == "fusion":
												__UpperCAmelCase	: Dict		=					self._np_extract_fbank_features(UpperCamelCase_      ,  self.mel_filters)
												__UpperCAmelCase	: Union[str, Any]		=					np.stack([input_mel, input_mel, input_mel, input_mel]      ,  axis=0)
									else:
												__UpperCAmelCase	: Optional[Any]		=					self._np_extract_fbank_features(UpperCamelCase_      ,  self.mel_filters_slaney)[None, :]

						return input_mel, longer


			def __call__(      self      :    int      ,  UpperCamelCase_      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,  UpperCamelCase_      :    str = None      ,  UpperCamelCase_      :    Optional[str] = None      ,  UpperCamelCase_      :    Optional[int] = None      ,  UpperCamelCase_      :    Optional[int] = None      ,  UpperCamelCase_      :    Optional[Union[str, TensorType]] = None      ,  **UpperCamelCase_      :    str      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					truncation if truncation is not None else self.truncation
						__UpperCAmelCase	: Optional[int]		=					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.")

						__UpperCAmelCase	: Any		=					isinstance(UpperCamelCase_      ,  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	: Optional[Any]		=					is_batched_numpy or (
						    isinstance(UpperCamelCase_      ,  (list, tuple)) and (isinstance(raw_speech[0]      ,  (np.ndarray, tuple, list)))
						)

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

						# always return batch
						if not is_batched:
									__UpperCAmelCase	: int		=					[np.asarray(UpperCamelCase_)]

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

						__UpperCAmelCase	: Optional[Any]		=					[]
						__UpperCAmelCase	: Optional[Any]		=					[]
						for mel, longer in padded_inputs:
									input_mel.append(UpperCamelCase_)
									is_longer.append(UpperCamelCase_)

						if truncation == "fusion" and sum(UpperCamelCase_) == 0:
									# if no audio is longer than 10s, then randomly select one audio to be longer
									__UpperCAmelCase	: Union[str, Any]		=					np.random.randint(0      ,  len(UpperCamelCase_))
									__UpperCAmelCase	: int		=					True

						if isinstance(input_mel[0]      ,  UpperCamelCase_):
									__UpperCAmelCase	: Tuple		=					[np.asarray(UpperCamelCase_      ,  dtype=np.floataa) for feature in input_mel]

						# is_longer is a list of bool
						__UpperCAmelCase	: Dict		=					[[longer] for longer in is_longer]

						__UpperCAmelCase	: Dict		=					{"input_features": input_mel, "is_longer": is_longer}
						__UpperCAmelCase	: List[str]		=					BatchFeature(UpperCamelCase_)

						if return_tensors is not None:
									__UpperCAmelCase	: Optional[Any]		=					input_features.convert_to_tensors(UpperCamelCase_)

						return input_features








"""simple docstring"""





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

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


enable_full_determinism()



class      a__				(	__magic_name__							,    unittest.TestCase   ):
			lowercase_    =	LDMTextToImagePipeline
			lowercase_    =	TEXT_TO_IMAGE_PARAMS - {
			    "negative_prompt",
			    "negative_prompt_embeds",
			    "cross_attention_kwargs",
			    "prompt_embeds",
			}
			lowercase_    =	PipelineTesterMixin.required_optional_params - {
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			lowercase_    =	TEXT_TO_IMAGE_BATCH_PARAMS
			lowercase_    =	False


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						torch.manual_seed(0)
						__UpperCAmelCase	: str		=					UNetaDConditionModel(
						    block_out_channels=(32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=4      ,  out_channels=4      ,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,  cross_attention_dim=32      ,  )
						__UpperCAmelCase	: Dict		=					DDIMScheduler(
						    beta_start=0.00085      ,  beta_end=0.012      ,  beta_schedule="scaled_linear"      ,  clip_sample=UpperCamelCase_      ,  set_alpha_to_one=UpperCamelCase_      ,  )
						torch.manual_seed(0)
						__UpperCAmelCase	: Optional[int]		=					AutoencoderKL(
						    block_out_channels=(32, 64)      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")      ,  up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")      ,  latent_channels=4      ,  )
						torch.manual_seed(0)
						__UpperCAmelCase	: List[str]		=					CLIPTextConfig(
						    bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=32      ,  intermediate_size=37      ,  layer_norm_eps=1e-05      ,  num_attention_heads=4      ,  num_hidden_layers=5      ,  pad_token_id=1      ,  vocab_size=1000      ,  )
						__UpperCAmelCase	: List[str]		=					CLIPTextModel(UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

						__UpperCAmelCase	: Optional[int]		=					{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vqvae": vae,
						    "bert": text_encoder,
						    "tokenizer": tokenizer,
						}
						return components


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Tuple=0):


						"""simple docstring"""




						if str(UpperCamelCase_).startswith("mps"):
									__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
						else:
									__UpperCAmelCase	: Tuple		=					torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					"cpu"  # ensure determinism for the device-dependent torch.Generator

						__UpperCAmelCase	: Union[str, Any]		=					self.get_dummy_components()
						__UpperCAmelCase	: Union[str, Any]		=					LDMTextToImagePipeline(**UpperCamelCase_)
						pipe.to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: int		=					self.get_dummy_inputs(UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					pipe(**UpperCamelCase_).images
						__UpperCAmelCase	: List[Any]		=					image[0, -3:, -3:, -1]

						assert image.shape == (1, 16, 16, 3)
						__UpperCAmelCase	: List[str]		=					np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3






@slow
@require_torch_gpu
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




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


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Dict=0):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					torch.manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Optional[Any]		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
						__UpperCAmelCase	: str		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					self.get_inputs(UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					pipe(**UpperCamelCase_).images
						__UpperCAmelCase	: Dict		=					image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 256, 256, 3)
						__UpperCAmelCase	: List[Any]		=					np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878])
						__UpperCAmelCase	: List[Any]		=					np.abs(expected_slice - image_slice).max()
						assert max_diff < 1e-3






@nightly
@require_torch_gpu
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




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


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Tuple      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Optional[int]=0):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
						__UpperCAmelCase	: Optional[Any]		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 50,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: Union[str, Any]		=					self.get_inputs(UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					pipe(**UpperCamelCase_).images[0]

						__UpperCAmelCase	: int		=					load_numpy(
						    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy")
						__UpperCAmelCase	: int		=					np.abs(expected_image - image).max()
						assert max_diff < 1e-3


