


"""simple docstring"""
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

				__SCREAMING_SNAKE_CASE				:     Optional[int]		=     True
				__SCREAMING_SNAKE_CASE				:     Dict		=     ''
except Exception as e:
				__SCREAMING_SNAKE_CASE				:     Any		=     False
				__SCREAMING_SNAKE_CASE				:     Optional[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   __A  (lowerCAmelCase_    ,		lowerCAmelCase_):





							'''simple docstring'''

							__lowercase:		Tuple				   =     '''mel_config.json'''



							@register_to_config
							def __init__(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   int = 256		,							UpperCAmelCase_				:   int = 256		,							UpperCAmelCase_				:   int = 22_050		,							UpperCAmelCase_				:   int = 2_048		,							UpperCAmelCase_				:   int = 512		,							UpperCAmelCase_				:   int = 80		,							UpperCAmelCase_				:   int = 32		,							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		hop_length
														snake_case_     							=		sample_rate
														snake_case_     							=		n_fft
														snake_case_     							=		top_db
														snake_case_     							=		n_iter
														self.set_resolution(UpperCamelCase_		,							UpperCamelCase_							)
														snake_case_     							=		None

														if not _librosa_can_be_imported:
																					raise ValueError(_import_error							)



							def 		lowerCAmelCase							(					self				:   Optional[int]		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int							)       ->Dict:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   str = None		,							UpperCAmelCase_				:   np.ndarray = None							)       ->Optional[int]:



														"""simple docstring"""



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

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



							def 		lowerCAmelCase							(					self				:   Optional[int]							)       ->Tuple:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   int = 0							)       ->str:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Dict							)       ->List[Any]:



														"""simple docstring"""



														return self.sr



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   int							)       ->Tuple:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   Image.Image							)       ->Optional[Any]:



														"""simple docstring"""



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





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase	=    {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GPTJForCausalLM''',
							    '''GPTJForQuestionAnswering''',
							    '''GPTJForSequenceClassification''',
							    '''GPTJModel''',
							    '''GPTJPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''TFGPTJForCausalLM''',
							    '''TFGPTJForQuestionAnswering''',
							    '''TFGPTJForSequenceClassification''',
							    '''TFGPTJModel''',
							    '''TFGPTJPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''FlaxGPTJForCausalLM''',
							    '''FlaxGPTJModel''',
							    '''FlaxGPTJPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






