
"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


_UpperCAmelCase :			List[str]              =	{
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase :			Optional[int]              =	["SpeechT5Tokenizer"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase :			str              =	[
					    "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SpeechT5ForSpeechToText",
					    "SpeechT5ForSpeechToSpeech",
					    "SpeechT5ForTextToSpeech",
					    "SpeechT5Model",
					    "SpeechT5PreTrainedModel",
					    "SpeechT5HifiGan",
					]

if TYPE_CHECKING:
					from .configuration_speechta import (
					    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
					    SpeechTaConfig,
					    SpeechTaHifiGanConfig,
					)
					from .feature_extraction_speechta import SpeechTaFeatureExtractor
					from .processing_speechta import SpeechTaProcessor

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_speechta import SpeechTaTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_speechta import (
										    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SpeechTaForSpeechToSpeech,
										    SpeechTaForSpeechToText,
										    SpeechTaForTextToSpeech,
										    SpeechTaHifiGan,
										    SpeechTaModel,
										    SpeechTaPreTrainedModel,
										)

else:
					import sys

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


from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin


class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE       ):



		@register_to_config
		def __init__(			self    ,				A_ = 768    ,				)  -> List[Any]:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase          =					nn.Parameter(torch.zeros(1    ,				A_	)	)
				UpperCamelCase          =					nn.Parameter(torch.ones(1    ,				A_	)	)



		def __UpperCamelCase			(			self    ,				A_ = None    ,				A_ = None    ,				)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					nn.Parameter(self.mean.to(A_	).to(A_	)	)
				UpperCamelCase          =					nn.Parameter(self.std.to(A_	).to(A_	)	)
				return self



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					(embeds - self.mean) * 1.0 / self.std
				return embeds



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					(embeds * self.std) + self.mean
				return embeds
