


from ...configuration_utils import PretrainedConfig
from ...utils import logging


A   :					Optional[Any]	=      logging.get_logger(__name__)

A   :					Optional[Any]	=      {
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class     _lowercase				(							_UpperCamelCase):


       """simple docstring"""




       A__      		=    "vit_mae"





       def __init__(     self							:    List[Any]      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    List[Any]=12      ,   __lowerCamelCase							:    Optional[Any]=12      ,   __lowerCamelCase							:    Optional[int]=3072      ,   __lowerCamelCase							:    int="gelu"      ,   __lowerCamelCase							:    str=0.0      ,   __lowerCamelCase							:    Union[str, Any]=0.0      ,   __lowerCamelCase							:    Tuple=0.0_2      ,   __lowerCamelCase							:    Optional[int]=1E-1_2      ,   __lowerCamelCase							:    Any=224      ,   __lowerCamelCase							:    Dict=16      ,   __lowerCamelCase							:    int=3      ,   __lowerCamelCase							:    str=True      ,   __lowerCamelCase							:    Dict=16      ,   __lowerCamelCase							:    Optional[int]=512      ,   __lowerCamelCase							:    List[Any]=8      ,   __lowerCamelCase							:    Optional[int]=2048      ,   __lowerCamelCase							:    Tuple=0.7_5      ,   __lowerCamelCase							:    Optional[Any]=False      ,   **__lowerCamelCase							:    int      ,   ):


        '''simple docstring'''



        super().__init__(**__a )

        lowerCamelCase__	:       Optional[int]					=						hidden_size
        lowerCamelCase__	:       Any					=						num_hidden_layers
        lowerCamelCase__	:       Tuple					=						num_attention_heads
        lowerCamelCase__	:       Any					=						intermediate_size
        lowerCamelCase__	:       Dict					=						hidden_act
        lowerCamelCase__	:       Optional[Any]					=						hidden_dropout_prob
        lowerCamelCase__	:       List[Any]					=						attention_probs_dropout_prob
        lowerCamelCase__	:       str					=						initializer_range
        lowerCamelCase__	:       str					=						layer_norm_eps
        lowerCamelCase__	:       int					=						image_size
        lowerCamelCase__	:       Tuple					=						patch_size
        lowerCamelCase__	:       str					=						num_channels
        lowerCamelCase__	:       List[str]					=						qkv_bias
        lowerCamelCase__	:       List[str]					=						decoder_num_attention_heads
        lowerCamelCase__	:       Any					=						decoder_hidden_size
        lowerCamelCase__	:       Optional[int]					=						decoder_num_hidden_layers
        lowerCamelCase__	:       Optional[Any]					=						decoder_intermediate_size
        lowerCamelCase__	:       str					=						mask_ratio
        lowerCamelCase__	:       Optional[Any]					=						norm_pix_loss






from typing import TYPE_CHECKING

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


A   :					Optional[int]	=      {
    "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:
    A   :					List[Any]	=      ["SpeechT5Tokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[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

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


