

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

				_a:      Optional[Any]								=     True
				_a:      Optional[Any]								=     """"""
except Exception as e:
				_a:      Dict								=     False
				_a:      int								=     (
				    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   __UpperCamelCase      (   lowercase       ,       lowercase  ):
						SCREAMING_SNAKE_CASE__      			=       'mel_config.json'




						@register_to_config
						def __init__(						self		:  Any						,		lowerCAmelCase		:  int = 256						,		lowerCAmelCase		:  int = 256						,		lowerCAmelCase		:  int = 22_050						,		lowerCAmelCase		:  int = 2_048						,		lowerCAmelCase		:  int = 512						,		lowerCAmelCase		:  int = 80						,		lowerCAmelCase		:  int = 32						,		):


							'''simple docstring'''







							UpperCAmelCase_    			=     hop_length
							UpperCAmelCase_    			=     sample_rate
							UpperCAmelCase_    			=     n_fft
							UpperCAmelCase_    			=     top_db
							UpperCAmelCase_    			=     n_iter
							self.set_resolution(lowerCAmelCase						,		lowerCAmelCase							)
							UpperCAmelCase_    			=     None

							if not _librosa_can_be_imported:
								raise ValueError(_import_error							)




						def  __A   (						self		:  Tuple						,		lowerCAmelCase		:  int						,		lowerCAmelCase		:  int							):


							'''simple docstring'''







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




						def  __A   (						self		:  Union[str, Any]						,		lowerCAmelCase		:  str = None						,		lowerCAmelCase		:  np.ndarray = None							):


							'''simple docstring'''







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

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




						def  __A   (						self		:  List[Any]							):


							'''simple docstring'''







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




						def  __A   (						self		:  Tuple						,		lowerCAmelCase		:  int = 0							):


							'''simple docstring'''







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




						def  __A   (						self		:  str							):


							'''simple docstring'''







							return self.sr




						def  __A   (						self		:  Union[str, Any]						,		lowerCAmelCase		:  int							):


							'''simple docstring'''







							UpperCAmelCase_    			=     librosa.feature.melspectrogram(
							    y=self.get_audio_slice(lowerCAmelCase							)						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_mels=self.n_mels							)
							UpperCAmelCase_    			=     librosa.power_to_db(lowerCAmelCase						,		ref=np.max						,		top_db=self.top_db							)
							UpperCAmelCase_    			=     (((log_S + self.top_db) * 255 / self.top_db).clip(0						,		255							) + 0.5).astype(np.uinta							)
							UpperCAmelCase_    			=     Image.fromarray(lowerCAmelCase							)
							return image




						def  __A   (						self		:  int						,		lowerCAmelCase		:  Image.Image							):


							'''simple docstring'''







							UpperCAmelCase_    			=     np.frombuffer(image.tobytes()						,		dtype="uint8"							).reshape((image.height, image.width)							)
							UpperCAmelCase_    			=     bytedata.astype("float"							) * self.top_db / 255 - self.top_db
							UpperCAmelCase_    			=     librosa.db_to_power(lowerCAmelCase							)
							UpperCAmelCase_    			=     librosa.feature.inverse.mel_to_audio(
							    lowerCAmelCase						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_iter=self.n_iter							)
							return audio


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_a:      Dict								=     {
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Any								=     [
				    """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """NystromformerForMaskedLM""",
				    """NystromformerForMultipleChoice""",
				    """NystromformerForQuestionAnswering""",
				    """NystromformerForSequenceClassification""",
				    """NystromformerForTokenClassification""",
				    """NystromformerLayer""",
				    """NystromformerModel""",
				    """NystromformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nystromformer import (
								    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NystromformerForMaskedLM,
								    NystromformerForMultipleChoice,
								    NystromformerForQuestionAnswering,
								    NystromformerForSequenceClassification,
								    NystromformerForTokenClassification,
								    NystromformerLayer,
								    NystromformerModel,
								    NystromformerPreTrainedModel,
								)


else:
				import sys

				_a:      Optional[Any]								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)