





'''simple docstring'''


import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowerCamelCase :		List[str] 			=				HfApi()

_lowerCamelCase :		Tuple 			=				{}
# fmt: off
_lowerCamelCase :		Optional[int] 			=				torch.tensor([
    -0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
    1.2_3_4_2, -2.2_4_8_5, 0.4_6_3_6, 0.8_0_7_6, -0.7_9_9_1, 0.3_9_6_9, 0.8_4_9_8, 0.9_1_8_9,
    -1.8_8_8_7, -3.3_5_2_2, 0.7_6_3_9, 0.2_0_4_0, 0.6_2_7_1, -2.7_1_4_8, -1.6_3_1_6, 3.0_8_3_9,
    0.3_1_8_6, 0.2_7_2_1, -0.9_7_5_9, -1.2_4_6_1, 2.6_2_5_7, 1.3_5_5_7
])
_lowerCamelCase :		Union[str, Any] 			=				torch.tensor([
    -2.3_6_3_9, -2.5_3_4_4, 0.0_0_5_4, -0.6_6_7_4, 1.5_9_9_0, 1.0_1_5_8, 0.3_1_2_4, -2.1_4_3_6,
    1.8_7_9_5, -2.5_4_2_9, -0.1_5_6_6, -0.3_9_7_3, 1.2_4_9_0, 2.6_4_4_7, 1.2_2_8_3, -0.5_2_0_8,
    -2.8_1_5_4, -3.5_1_1_9, 2.3_8_3_8, 1.2_0_3_3, 1.7_2_0_1, -2.1_2_5_6, -1.4_5_7_6, 2.7_9_4_8,
    2.4_2_0_4, -0.9_7_5_2, -1.2_5_4_6, 0.8_0_2_7, 3.2_7_5_8, 3.1_3_6_5
])
_lowerCamelCase :		str 			=				torch.tensor([
    -0.6_5_3_1, -0.6_8_9_1, -0.3_1_7_2, -0.5_3_7_5, -0.9_1_4_0, -0.5_3_6_7, -0.1_1_7_5, -0.7_8_6_9,
    -0.3_8_0_8, -0.4_5_1_3, -0.2_0_9_8, -0.0_0_8_3, 0.3_1_8_3, 0.5_1_4_0, 0.2_2_4_7, -0.1_3_0_4,
    -0.1_3_0_2, -0.2_8_0_2, -0.2_0_8_4, -0.2_0_2_5, -0.4_9_6_7, -0.4_8_7_3, -0.0_8_6_1, 0.6_9_2_5,
    0.0_2_5_0, 0.1_2_9_0, -0.1_5_4_3, 0.6_3_1_6, 1.0_4_6_0, 1.4_9_4_3
])
_lowerCamelCase :		Dict 			=				torch.tensor([
    0.0_9_1_1, 0.1_1_0_7, 0.0_1_8_2, 0.0_4_3_5, -0.0_8_0_5, -0.0_6_0_8, 0.0_3_8_1, 0.2_1_7_2,
    -0.0_2_8_0, 0.1_3_2_7, -0.0_2_9_9, -0.0_2_5_5, -0.0_0_5_0, -0.1_1_7_0, -0.1_0_4_6, 0.0_3_0_9,
    0.1_3_6_7, 0.1_7_2_8, -0.0_5_3_3, -0.0_7_4_8, -0.0_5_3_4, 0.1_6_2_4, 0.0_3_8_4, -0.1_8_0_5,
    -0.0_7_0_7, 0.0_6_4_2, 0.0_2_2_0, -0.0_1_3_4, -0.1_3_3_3, -0.1_5_0_5
])
_lowerCamelCase :		Any 			=				torch.tensor([
    0.1_3_2_1, 0.1_3_3_7, 0.0_4_4_0, 0.0_6_2_2, -0.0_5_9_1, -0.0_3_7_0, 0.0_5_0_3, 0.2_1_3_3,
    -0.0_1_7_7, 0.1_4_1_5, -0.0_1_1_6, -0.0_1_1_2, 0.0_0_4_4, -0.0_9_8_0, -0.0_7_8_9, 0.0_3_9_5,
    0.1_5_0_2, 0.1_7_8_5, -0.0_4_8_8, -0.0_5_1_4, -0.0_4_0_4, 0.1_5_3_9, 0.0_4_5_4, -0.1_5_5_9,
    -0.0_6_6_5, 0.0_6_5_9, 0.0_3_8_3, -0.0_0_0_5, -0.1_2_6_6, -0.1_3_8_6
])
_lowerCamelCase :		Union[str, Any] 			=				torch.tensor([
    0.1_1_5_4, 0.1_2_1_8, 0.0_3_0_7, 0.0_5_2_6, -0.0_7_1_1, -0.0_5_4_1, 0.0_3_6_6, 0.2_0_7_8,
    -0.0_2_6_7, 0.1_3_1_7, -0.0_2_2_6, -0.0_1_9_3, -0.0_0_1_4, -0.1_0_5_5, -0.0_9_0_2, 0.0_3_3_0,
    0.1_3_9_1, 0.1_7_0_9, -0.0_5_6_2, -0.0_6_9_3, -0.0_5_6_0, 0.1_4_8_2, 0.0_3_8_1, -0.1_6_8_3,
    -0.0_6_8_1, 0.0_6_6_1, 0.0_3_3_1, -0.0_0_4_6, -0.1_2_6_8, -0.1_4_3_1
])
_lowerCamelCase :		Optional[int] 			=				torch.tensor([
    0.1_1_9_2, 0.1_2_4_0, 0.0_4_1_4, 0.0_6_0_6, -0.0_5_5_7, -0.0_4_1_2, 0.0_4_3_0, 0.2_0_4_2,
    -0.0_2_0_0, 0.1_3_8_5, -0.0_1_1_5, -0.0_1_3_2, 0.0_0_1_7, -0.0_9_6_5, -0.0_8_0_2, 0.0_3_9_8,
    0.1_4_3_3, 0.1_7_4_7, -0.0_4_5_8, -0.0_5_3_3, -0.0_4_0_7, 0.1_5_4_5, 0.0_4_1_9, -0.1_5_7_4,
    -0.0_6_4_5, 0.0_6_2_6, 0.0_3_4_1, -0.0_0_1_0, -0.1_1_9_9, -0.1_3_9_0
])
_lowerCamelCase :		Optional[Any] 			=				torch.tensor([
    0.1_0_7_5, 0.1_0_7_4, 0.0_2_0_5, 0.0_4_3_1, -0.0_7_7_4, -0.0_6_0_7, 0.0_2_9_8, 0.2_0_4_2,
    -0.0_3_2_0, 0.1_2_6_7, -0.0_2_8_1, -0.0_2_5_0, -0.0_0_6_4, -0.1_0_9_1, -0.0_9_4_6, 0.0_2_9_0,
    0.1_3_2_8, 0.1_6_5_0, -0.0_5_8_0, -0.0_7_3_8, -0.0_5_8_6, 0.1_4_4_0, 0.0_3_3_7, -0.1_7_4_6,
    -0.0_7_1_2, 0.0_6_0_5, 0.0_2_5_0, -0.0_0_9_9, -0.1_3_1_6, -0.1_4_7_3
])
_lowerCamelCase :		int 			=				torch.tensor([
    -1.4_5_7_2, -2.0_4_8_1, -0.0_4_1_4, -0.6_0_0_5, 1.4_1_3_6, 0.5_8_4_8, 0.4_0_2_8, -2.7_3_3_0,
    1.2_2_1_2, -2.1_2_2_8, 0.2_1_5_5, 0.4_0_3_9, 0.7_6_6_2, 2.0_5_3_5, 0.7_4_7_7, -0.3_2_4_3,
    -2.1_7_5_8, -2.7_6_4_8, 1.6_9_4_7, 0.7_0_2_6, 1.2_3_3_8, -1.6_0_7_8, -0.8_6_8_2, 2.2_8_1_0,
    1.8_5_7_4, -0.5_7_1_8, -0.5_5_8_6, -0.0_1_8_6, 2.3_4_1_5, 2.1_2_5_1])
_lowerCamelCase :		Any 			=				torch.tensor([
    -1.3_6_9_0, -1.9_7_2_0, -0.4_0_9_0, -0.6_9_6_6, 1.4_6_6_0, 0.9_9_3_8, -0.1_3_8_5, -2.7_3_2_4,
    0.7_7_3_6, -1.8_9_1_7, 0.2_9_2_3, 0.4_2_9_3, 0.1_6_9_3, 1.4_1_1_2, 1.1_8_8_7, -0.3_1_8_1,
    -2.2_1_6_0, -2.6_3_8_1, 1.3_1_7_0, 0.8_1_6_3, 0.9_2_4_0, -1.6_5_4_4, -0.6_0_9_9, 2.5_2_5_9,
    1.6_4_3_0, -0.9_0_9_0, -0.9_3_9_2, -0.0_1_2_6, 2.4_2_6_8, 2.3_2_6_6
])
_lowerCamelCase :		Tuple 			=				torch.tensor([
    -1.3_5_2_5, -1.9_6_2_8, -0.3_9_5_6, -0.6_8_6_0, 1.4_6_6_4, 1.0_0_1_4, -0.1_2_5_9, -2.7_2_1_2,
    0.7_7_7_2, -1.8_8_1_1, 0.2_9_9_6, 0.4_3_8_8, 0.1_7_0_4, 1.4_0_2_9, 1.1_7_0_1, -0.3_0_2_7,
    -2.2_0_5_3, -2.6_2_8_7, 1.3_3_5_0, 0.8_1_3_1, 0.9_2_7_4, -1.6_2_9_2, -0.6_0_9_8, 2.5_1_3_1,
    1.6_5_0_5, -0.8_9_5_8, -0.9_2_9_8, -0.0_1_5_1, 2.4_2_5_7, 2.3_3_5_5
])
_lowerCamelCase :		List[Any] 			=				torch.tensor([
    -2.0_5_8_5, -2.7_8_9_7, -0.2_8_5_0, -0.8_9_4_0, 1.9_0_5_2, 0.5_7_0_2, 0.6_3_4_5, -3.8_9_5_9,
    1.5_9_3_2, -3.2_3_1_9, 0.1_9_7_4, 0.0_2_8_7, 1.7_5_6_6, 2.6_5_4_3, 0.8_3_8_7, -0.5_3_5_1,
    -3.2_7_3_6, -4.3_3_7_5, 2.9_0_2_9, 1.6_3_9_0, 1.4_6_4_0, -2.1_7_0_1, -1.9_0_1_3, 2.9_3_4_1,
    3.4_9_8_1, -0.6_2_5_5, -1.1_6_4_4, -0.1_5_9_1, 3.7_0_9_7, 3.2_0_6_6
])
_lowerCamelCase :		Any 			=				torch.tensor([
    -2.3_1_3_9, -2.5_5_9_4, -0.0_1_9_7, -0.6_7_8_5, 1.7_0_0_1, 1.1_6_0_6, 0.3_0_7_5, -2.1_7_4_0,
    1.8_0_7_1, -2.5_6_3_0, -0.0_9_2_6, -0.3_8_1_1, 1.2_1_1_6, 2.6_2_4_6, 1.2_7_3_1, -0.5_3_9_8,
    -2.8_1_5_3, -3.6_1_4_0, 2.3_8_9_3, 1.3_2_6_2, 1.6_2_5_8, -2.1_8_5_6, -1.3_2_6_7, 2.8_3_9_5,
    2.3_7_7_9, -1.0_6_2_3, -1.2_4_6_8, 0.8_9_5_9, 3.3_3_6_7, 3.2_2_4_3
])
_lowerCamelCase :		List[Any] 			=				torch.tensor([
    -2.0_6_2_8, -2.7_6_6_7, -0.2_0_8_9, -0.8_2_6_3, 2.0_5_3_9, 0.5_9_9_2, 0.6_4_9_5, -3.8_3_3_6,
    1.6_0_2_5, -3.2_8_1_7, 0.1_7_2_1, -0.0_6_3_3, 1.7_5_1_6, 2.7_0_3_9, 0.8_1_0_0, -0.5_9_0_8,
    -3.2_1_1_3, -4.4_3_4_3, 2.9_2_5_7, 1.3_6_3_2, 1.5_5_6_2, -2.1_4_8_9, -1.9_8_9_4, 3.0_5_6_0,
    3.3_3_9_6, -0.7_3_2_8, -1.0_4_1_7, 0.0_3_8_3, 3.7_0_9_3, 3.2_3_4_3
])
_lowerCamelCase :		Union[str, Any] 			=				torch.tensor([
    -1.4_5_7_4, -2.0_5_6_9, -0.0_4_7_3, -0.6_1_1_7, 1.4_0_1_8, 0.5_7_6_9, 0.4_1_2_9, -2.7_3_4_4,
    1.2_2_4_1, -2.1_3_9_7, 0.2_0_0_0, 0.3_9_3_7, 0.7_6_1_6, 2.0_4_5_3, 0.7_3_2_4, -0.3_3_9_1,
    -2.1_7_4_6, -2.7_7_4_4, 1.6_9_6_3, 0.6_9_2_1, 1.2_1_8_7, -1.6_1_7_2, -0.8_8_7_7, 2.2_4_3_9,
    1.8_4_7_1, -0.5_8_3_9, -0.5_6_0_5, -0.0_4_6_4, 2.3_2_5_0, 2.1_2_1_9
])
# fmt: on

_lowerCamelCase :		Tuple 			=				api.list_models(filter='diffusers')
for mod in models:
				if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
								_lowerCamelCase :		Dict 			=				'/home/patrick/google_checkpoints/' + mod.modelId.split('/')[-1]

								print(f"Started running {mod.modelId}!!!")

								if mod.modelId.startswith('CompVis'):
												_lowerCamelCase :		List[str] 			=				UNetaDModel.from_pretrained(local_checkpoint, subfolder='unet')
								else:
												_lowerCamelCase :		Tuple 			=				UNetaDModel.from_pretrained(local_checkpoint)

								torch.manual_seed(0)
								random.seed(0)

								_lowerCamelCase :		int 			=				torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
								_lowerCamelCase :		List[Any] 			=				torch.tensor([10] * noise.shape[0])
								with torch.no_grad():
												_lowerCamelCase :		Union[str, Any] 			=				model(noise, time_step).sample

								assert torch.allclose(
								    logits[0, 0, 0, :30], results['_'.join('_'.join(mod.modelId.split('/')).split('-'))], atol=1e-3
								)
								print(f"{mod.modelId} has passed successfully!!!")


'''simple docstring'''




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


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Union[str, Any]      =       ['''input_features''']



			def __init__(   self       ,    __lowerCAmelCase=80       ,    __lowerCAmelCase=16000       ,    __lowerCAmelCase=160       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=False       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(
					    feature_size=__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    padding_value=__lowerCAmelCase       ,    return_attention_mask=__lowerCAmelCase       ,    **__lowerCAmelCase       ,    )
					lowerCAmelCase   					=  n_fft
					lowerCAmelCase   					=  hop_length
					lowerCAmelCase   					=  chunk_length
					lowerCAmelCase   					=  chunk_length * sampling_rate
					lowerCAmelCase   					=  self.n_samples // hop_length
					lowerCAmelCase   					=  sampling_rate
					lowerCAmelCase   					=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2       ,    num_mel_filters=__lowerCAmelCase       ,    min_frequency=0.0       ,    max_frequency=8000.0       ,    sampling_rate=__lowerCAmelCase       ,    norm="""slaney"""       ,    mel_scale="""slaney"""       ,    )



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  spectrogram(
					    __lowerCAmelCase       ,    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"""       ,    )
					lowerCAmelCase   					=  log_spec[:, :-1]
					lowerCAmelCase   					=  np.maximum(__lowerCAmelCase       ,    log_spec.max() - 8.0)
					lowerCAmelCase   					=  (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 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = 0.0):




					"""simple docstring"""


					if attention_mask is not None:
							lowerCAmelCase   					=  np.array(__lowerCAmelCase       ,    np.intaa)
							lowerCAmelCase   					=  []

							for vector, length in zip(__lowerCAmelCase       ,    attention_mask.sum(-1)):
									lowerCAmelCase   					=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
									if length < normed_slice.shape[0]:
											lowerCAmelCase   					=  padding_value

									normed_input_values.append(__lowerCAmelCase)
					else:
							lowerCAmelCase   					=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

					return normed_input_values



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




					"""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.""")

					lowerCAmelCase   					=  isinstance(__lowerCAmelCase       ,    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}")
					lowerCAmelCase   					=  is_batched_numpy or (
					    isinstance(__lowerCAmelCase       ,    (list, tuple)) and (isinstance(raw_speech[0]       ,    (np.ndarray, tuple, list)))
					)

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

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

					lowerCAmelCase   					=  BatchFeature({"""input_features""": raw_speech})

					# convert into correct format for padding

					lowerCAmelCase   					=  self.pad(
					    __lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    max_length=max_length if max_length else self.n_samples       ,    truncation=__lowerCAmelCase       ,    pad_to_multiple_of=__lowerCAmelCase       ,    return_attention_mask=return_attention_mask or do_normalize       ,    )

					# zero-mean and unit-variance normalization
					if do_normalize:
							lowerCAmelCase   					=  self.zero_mean_unit_var_norm(
							    padded_inputs["""input_features"""]       ,    attention_mask=padded_inputs["""attention_mask"""]       ,    padding_value=self.padding_value       ,    )
							lowerCAmelCase   					=  np.stack(padded_inputs["""input_features"""]       ,    axis=0)

					# make sure list is in array format
					lowerCAmelCase   					=  padded_inputs.get("""input_features""").transpose(2       ,    0       ,    1)

					lowerCAmelCase   					=  [self._np_extract_fbank_features(__lowerCAmelCase) for waveform in input_features[0]]

					if isinstance(input_features[0]       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  [np.asarray(__lowerCAmelCase       ,    dtype=np.floataa) for feature in input_features]
					else:
							lowerCAmelCase   					=  input_features

					if return_attention_mask:
							# rescale from sample (48000) to feature (3000)
							lowerCAmelCase   					=  padded_inputs["""attention_mask"""][:, :: self.hop_length]

					if return_tensors is not None:
							lowerCAmelCase   					=  padded_inputs.convert_to_tensors(__lowerCAmelCase)

					return padded_inputs







			def 							a_       (   self):




					"""simple docstring"""


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




