



"""simple docstring"""







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


lowerCAmelCase_   						=   logging.get_logger(__name__)

lowerCAmelCase_   						=   {
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[str]   	=				"mra"







		def __init__(	self						:  Dict			,_snake_case						:  Any=50_265			,_snake_case						:  Union[str, Any]=768			,_snake_case						:  List[str]=12			,_snake_case						:  Tuple=12			,_snake_case						:  Any=3_072			,_snake_case						:  int="gelu"			,_snake_case						:  str=0.1			,_snake_case						:  Union[str, Any]=0.1			,_snake_case						:  List[str]=512			,_snake_case						:  List[Any]=1			,_snake_case						:  Optional[Any]=0.02			,_snake_case						:  Union[str, Any]=1e-5			,_snake_case						:  Tuple="absolute"			,_snake_case						:  int=4			,_snake_case						:  Any="full"			,_snake_case						:  List[Any]=0			,_snake_case						:  int=0			,_snake_case						:  str=1			,_snake_case						:  List[str]=0			,_snake_case						:  Optional[int]=2			,**_snake_case						:  Optional[int]			,)					->		Union[str, Any]:






				"""simple docstring"""



				super().__init__(pad_token_id=_snake_case			,bos_token_id=_snake_case			,eos_token_id=_snake_case			,**_snake_case						)

				lowercase__   :    Any				=			vocab_size
				lowercase__   :    Dict				=			max_position_embeddings
				lowercase__   :    str				=			hidden_size
				lowercase__   :    Union[str, Any]				=			num_hidden_layers
				lowercase__   :    Tuple				=			num_attention_heads
				lowercase__   :    Tuple				=			intermediate_size
				lowercase__   :    int				=			hidden_act
				lowercase__   :    int				=			hidden_dropout_prob
				lowercase__   :    Tuple				=			attention_probs_dropout_prob
				lowercase__   :    Dict				=			initializer_range
				lowercase__   :    Tuple				=			type_vocab_size
				lowercase__   :    List[str]				=			layer_norm_eps
				lowercase__   :    int				=			position_embedding_type
				lowercase__   :    List[Any]				=			block_per_row
				lowercase__   :    Optional[int]				=			approx_mode
				lowercase__   :    Tuple				=			initial_prior_first_n_blocks
				lowercase__   :    List[Any]				=			initial_prior_diagonal_n_blocks





"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase_   						=   {
    '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:
							lowerCAmelCase_   						=   ['SpeechT5Tokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    '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

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