



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


__A       =logging.get_logger(__name__)

__A       ={
    "facebook/regnet-y-040": "https://huggingface.co/facebook/regnet-y-040/blob/main/config.json",
}

class 						UpperCAmelCase__     (     __UpperCamelCase  ):




			'''simple docstring'''






			UpperCamelCase     		=			"""regnet"""
			UpperCamelCase     		=			["""x""", """y"""]




			def __init__(  self				:					Dict			,			a_				:					int=3			,			a_				:					Optional[Any]=32			,			a_				:					Any=[1_28, 1_92, 5_12, 10_88]			,			a_				:					int=[2, 6, 12, 2]			,			a_				:					Union[str, Any]=64			,			a_				:					int="y"			,			a_				:					Any="relu"			,			**a_				:					Any			,			):
					'''simple docstring'''





					super().__init__(**a_					)
					if layer_type not in self.layer_types:
							raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types					)}'					)
					__UpperCAmelCase       :	Any						=      num_channels
					__UpperCAmelCase       :	List[str]						=      embedding_size
					__UpperCAmelCase       :	Optional[Any]						=      hidden_sizes
					__UpperCAmelCase       :	Tuple						=      depths
					__UpperCAmelCase       :	Union[str, Any]						=      groups_width
					__UpperCAmelCase       :	Optional[Any]						=      layer_type
					__UpperCAmelCase       :	Tuple						=      hidden_act
					# always downsample in the first stage
					__UpperCAmelCase       :	Optional[Any]						=      True










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__)





