




from typing import TYPE_CHECKING

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


UpperCAmelCase__					  =			{
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__					  =			["Speech2TextTokenizer"]

try:
			if not is_speech_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__					  =			["Speech2TextFeatureExtractor"]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__					  =			[
			    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFSpeech2TextForConditionalGeneration",
			    "TFSpeech2TextModel",
			    "TFSpeech2TextPreTrainedModel",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__					  =			[
			    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Speech2TextForConditionalGeneration",
			    "Speech2TextModel",
			    "Speech2TextPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
			from .processing_speech_to_text import SpeechaTextProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speech_to_text import SpeechaTextTokenizer

			try:
						if not is_speech_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_speech_to_text import (
						    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFSpeechaTextForConditionalGeneration,
						    TFSpeechaTextModel,
						    TFSpeechaTextPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speech_to_text import (
						    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechaTextForConditionalGeneration,
						    SpeechaTextModel,
						    SpeechaTextPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase__					  =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











'''simple docstring'''





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 					snake_case			(UpperCamelCase		,   UpperCamelCase ):



       @register_to_config
       def __init__(					self  ,UpperCAmelCase_ = 768  ,)	->				List[Any]:
           super().__init__()

           lowercase__									=    nn.Parameter(torch.zeros(1  ,UpperCAmelCase_   )   )
           lowercase__									=    nn.Parameter(torch.ones(1  ,UpperCAmelCase_   )   )



       def 				_a   (					self  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,)	->				Any:
           lowercase__									=    nn.Parameter(self.mean.to(UpperCAmelCase_   ).to(UpperCAmelCase_   )   )
           lowercase__									=    nn.Parameter(self.std.to(UpperCAmelCase_   ).to(UpperCAmelCase_   )   )
           return self



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Tuple:
           lowercase__									=    (embeds - self.mean) * 1.0 / self.std
           return embeds







       def 				_a   (					self  ,UpperCAmelCase_   )	->				List[str]:
           lowercase__									=    (embeds * self.std) + self.mean
           return embeds




