






import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
		import librosa  # noqa: E402

		lowerCamelCase_						     =  True
		lowerCamelCase_						     =  """"""
except Exception as e:
		lowerCamelCase_						     =  False
		lowerCamelCase_						     =  (
		    f'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
		)


from PIL import Image  # noqa: E402




class 							a_ (						a_	,	a_		):


							'''simple docstring'''






							__a:	List[str]     =    '''mel_config.json'''

							@register_to_config
							def __init__(		self			,			lowercase_ = 2_5_6			,			lowercase_ = 2_5_6			,			lowercase_ = 2_2_0_5_0			,			lowercase_ = 2_0_4_8			,			lowercase_ = 5_1_2			,			lowercase_ = 8_0			,			lowercase_ = 3_2			,			)					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     hop_length
														lowerCAmelCase_										=     sample_rate
														lowerCAmelCase_										=     n_fft
														lowerCAmelCase_										=     top_db
														lowerCAmelCase_										=     n_iter
														self.set_resolution(lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     None

														if not _librosa_can_be_imported:
																					raise ValueError(_import_error   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_   )					->					int:
														'''simple docstring'''

														lowerCAmelCase_										=     x_res
														lowerCAmelCase_										=     y_res
														lowerCAmelCase_										=     self.y_res
														lowerCAmelCase_										=     self.x_res * self.hop_length - 1

							def      _lowercase       (		self			,			lowercase_ = None			,			lowercase_ = None   )					->					Tuple:
														'''simple docstring'''

														if audio_file is not None:
																					lowerCAmelCase_						,   lowerCAmelCase_										=     librosa.load(lowercase_			,			mono=lowercase_			,			sr=self.sr   )
														else:
																					lowerCAmelCase_										=     raw_audio

														# Pad with silence if necessary.
														if len(self.audio   ) < self.x_res * self.hop_length:
																					lowerCAmelCase_										=     np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio   ),)   )]   )

							def      _lowercase       (		self   )					->					int:
														'''simple docstring'''

														return len(self.audio   ) // self.slice_size

							def      _lowercase       (		self			,			lowercase_ = 0   )					->					np.ndarray:
														'''simple docstring'''

														return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]

							def      _lowercase       (		self   )					->					int:
														'''simple docstring'''

														return self.sr

							def      _lowercase       (		self			,			lowercase_   )					->					Image.Image:
														'''simple docstring'''

														lowerCAmelCase_										=     librosa.feature.melspectrogram(
														    y=self.get_audio_slice(lowercase_   )			,			sr=self.sr			,			n_fft=self.n_fft			,			hop_length=self.hop_length			,			n_mels=self.n_mels   )
														lowerCAmelCase_										=     librosa.power_to_db(lowercase_			,			ref=np.max			,			top_db=self.top_db   )
														lowerCAmelCase_										=     (((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0			,			2_5_5   ) + 0.5).astype(np.uinta   )
														lowerCAmelCase_										=     Image.fromarray(lowercase_   )
														return image






							def      _lowercase       (		self			,			lowercase_   )					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     np.frombuffer(image.tobytes()			,			dtype='uint8'   ).reshape((image.height, image.width)   )
														lowerCAmelCase_										=     bytedata.astype('float'   ) * self.top_db / 2_5_5 - self.top_db
														lowerCAmelCase_										=     librosa.db_to_power(lowercase_   )
														lowerCAmelCase_										=     librosa.feature.inverse.mel_to_audio(
														    lowercase_			,			sr=self.sr			,			n_fft=self.n_fft			,			hop_length=self.hop_length			,			n_iter=self.n_iter   )
														return audio











import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


lowerCamelCase_						     =  logging.get_logger(__name__)




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def __init__(		self   )					->					Any:
														'''simple docstring'''

														super().__init__()
														if version.parse(torch.__version__   ) < version.parse('1.12.0'   ):
																					raise ImportError(
																					    f'''You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '''
																					    'PytorchGELUTanh. Please upgrade torch.'   )






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return nn.functional.gelu(lowercase_			,			approximate='tanh'   )




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.04_47_15 * torch.pow(lowercase_			,			3.0   ))   ))




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_ = False   )					->					Optional[Any]:
														'''simple docstring'''

														super().__init__()
														if use_gelu_python:
																					lowerCAmelCase_										=     self._gelu_python
														else:
																					lowerCAmelCase_										=     nn.functional.gelu

							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0   )   ))






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return self.act(lowercase_   )




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return 0.5 * input * (1.0 + torch.tanh(input * 0.79_78_84_56_08 * (1.0 + 0.04_47_15 * input * input)   ))




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return input * torch.sigmoid(1.7_02 * input   )




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_   )					->					Optional[int]:
														'''simple docstring'''

														if min > max:
																					raise ValueError(f'''min should be < max (got min: {min}, max: {max})'''   )

														super().__init__()
														lowerCAmelCase_										=     min
														lowerCAmelCase_										=     max






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return torch.clip(gelu(lowercase_   )			,			self.min			,			self.max   )




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def __init__(		self   )					->					Union[str, Any]:
														'''simple docstring'''

														super().__init__()
														lowerCAmelCase_										=     math.sqrt(2 / math.pi   )






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_47_15 * torch.pow(lowercase_			,			3   ))   ))




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return nn.functional.silu(lowercase_   )




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def __init__(		self   )					->					List[Any]:
														'''simple docstring'''

														super().__init__()
														if version.parse(torch.__version__   ) < version.parse('1.9.0'   ):
																					lowerCAmelCase_										=     self._mish_python
														else:
																					lowerCAmelCase_										=     nn.functional.mish

							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return input * torch.tanh(nn.functional.softplus(lowercase_   )   )






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return self.act(lowercase_   )




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_   )					->					Tensor:
														'''simple docstring'''

														return input




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_			,			lowercase_=0.70_71_07			,			lowercase_=0.28_20_95   )					->					Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     (input - mu).div(sigma * math.sqrt(2.0   )   )
														return 0.5 * (1.0 + torch.erf(lowercase_   ))




class 							a_ (						nn.Module		):


							'''simple docstring'''






							def      _lowercase       (		self			,			lowercase_   )					->					Any:
														'''simple docstring'''

														lowerCAmelCase_										=     nn.functional.relu(lowercase_   )
														lowerCAmelCase_										=     torch.square(lowercase_   )
														return squared




class 							a_ (						a_		):


							'''simple docstring'''






							def __getitem__(		self			,			lowercase_   )					->					Any:
														'''simple docstring'''

														lowerCAmelCase_										=     super().__getitem__(lowercase_   )
														lowerCAmelCase_						,   lowerCAmelCase_										=     content if isinstance(lowercase_			,			lowercase_   ) else (content, {})
														return cls(**lowercase_   )


lowerCamelCase_						     =  {
    """gelu""": GELUActivation,
    """gelu_10""": (ClippedGELUActivation, {"""min""": -1_0, """max""": 1_0}),
    """gelu_fast""": FastGELUActivation,
    """gelu_new""": NewGELUActivation,
    """gelu_python""": (GELUActivation, {"""use_gelu_python""": True}),
    """gelu_pytorch_tanh""": PytorchGELUTanh,
    """gelu_accurate""": AccurateGELUActivation,
    """laplace""": LaplaceActivation,
    """linear""": LinearActivation,
    """mish""": MishActivation,
    """quick_gelu""": QuickGELUActivation,
    """relu""": nn.ReLU,
    """relu2""": ReLUSquaredActivation,
    """relu6""": nn.ReLUa,
    """sigmoid""": nn.Sigmoid,
    """silu""": SiLUActivation,
    """swish""": SiLUActivation,
    """tanh""": nn.Tanh,
}
lowerCamelCase_						     =  ClassInstantier(ACTaCLS)
def      lowerCamelCase					(						a_	)	->			str:
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()	)}'''	)


# For backwards compatibility with: from activations import gelu_python
lowerCamelCase_						     =  get_activation("""gelu_python""")
lowerCamelCase_						     =  get_activation("""gelu_new""")
lowerCamelCase_						     =  get_activation("""gelu""")
lowerCamelCase_						     =  get_activation("""gelu_fast""")
lowerCamelCase_						     =  get_activation("""quick_gelu""")
lowerCamelCase_						     =  get_activation("""silu""")
lowerCamelCase_						     =  get_activation("""mish""")



lowerCamelCase_						     =  get_activation("""linear""")



