


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


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

lowerCAmelCase__    :Dict       =				{
    '''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}

class 						__a (							UpperCAmelCase     ):
		_a      :    int           =      'mra'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=50265     ,       _SCREAMING_SNAKE_CASE=768     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=3072     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1e-5     ,       _SCREAMING_SNAKE_CASE="absolute"     ,       _SCREAMING_SNAKE_CASE=4     ,       _SCREAMING_SNAKE_CASE="full"     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=2     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Optional[int]:




									"""simple docstring"""





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

									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     max_position_embeddings
									_UpperCAmelCase											=     hidden_size
									_UpperCAmelCase											=     num_hidden_layers
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     intermediate_size
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     hidden_dropout_prob
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     type_vocab_size
									_UpperCAmelCase											=     layer_norm_eps
									_UpperCAmelCase											=     position_embedding_type
									_UpperCAmelCase											=     block_per_row
									_UpperCAmelCase											=     approx_mode
									_UpperCAmelCase											=     initial_prior_first_n_blocks
									_UpperCAmelCase											=     initial_prior_diagonal_n_blocks




from typing import TYPE_CHECKING

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


lowerCAmelCase__    :Tuple       =				{
    '''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__    :Dict       =				['''SpeechT5Tokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :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

		lowerCAmelCase__    :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
