



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


__A       =logging.get_logger(__name__)

__A       ={
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class 						UpperCAmelCase__     (     __UpperCamelCase  ):




			'''simple docstring'''






			UpperCamelCase     		=			"""splinter"""




			def __init__(  self				:					str			,			a_				:					Optional[Any]=3_05_22			,			a_				:					Any=7_68			,			a_				:					Optional[int]=12			,			a_				:					Optional[int]=12			,			a_				:					Optional[Any]=30_72			,			a_				:					Optional[int]="gelu"			,			a_				:					Union[str, Any]=0.1			,			a_				:					Optional[int]=0.1			,			a_				:					str=5_12			,			a_				:					Dict=2			,			a_				:					str=0.0_2			,			a_				:					Dict=1e-12			,			a_				:					Dict=True			,			a_				:					Union[str, Any]=0			,			a_				:					Dict=1_04			,			**a_				:					Optional[Any]			,			):
					'''simple docstring'''





					super().__init__(pad_token_id=a_			,			**a_					)

					__UpperCAmelCase       :	str						=      vocab_size
					__UpperCAmelCase       :	Optional[Any]						=      max_position_embeddings
					__UpperCAmelCase       :	str						=      hidden_size
					__UpperCAmelCase       :	int						=      num_hidden_layers
					__UpperCAmelCase       :	Union[str, Any]						=      num_attention_heads
					__UpperCAmelCase       :	Tuple						=      intermediate_size
					__UpperCAmelCase       :	List[Any]						=      hidden_act
					__UpperCAmelCase       :	int						=      hidden_dropout_prob
					__UpperCAmelCase       :	Union[str, Any]						=      attention_probs_dropout_prob
					__UpperCAmelCase       :	int						=      initializer_range
					__UpperCAmelCase       :	Union[str, Any]						=      type_vocab_size
					__UpperCAmelCase       :	str						=      layer_norm_eps
					__UpperCAmelCase       :	Optional[Any]						=      use_cache
					__UpperCAmelCase       :	Optional[int]						=      question_token_id










from typing import TYPE_CHECKING

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


__A       ={
    "configuration_speech_to_text_2": ["SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2Text2Config"],
    "processing_speech_to_text_2": ["Speech2Text2Processor"],
    "tokenization_speech_to_text_2": ["Speech2Text2Tokenizer"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A       =[
				    "SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Speech2Text2ForCausalLM",
				    "Speech2Text2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
				from .processing_speech_to_text_a import SpeechaTextaProcessor
				from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speech_to_text_a import (
								    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechaTextaForCausalLM,
								    SpeechaTextaPreTrainedModel,
								)

else:
				import sys

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





