





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_  :							Any            =	{
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							List[Any]            =	["MPNetTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Union[str, Any]            =	[
					    "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "MPNetForMaskedLM",
					    "MPNetForMultipleChoice",
					    "MPNetForQuestionAnswering",
					    "MPNetForSequenceClassification",
					    "MPNetForTokenClassification",
					    "MPNetLayer",
					    "MPNetModel",
					    "MPNetPreTrainedModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							List[str]            =	[
					    "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFMPNetEmbeddings",
					    "TFMPNetForMaskedLM",
					    "TFMPNetForMultipleChoice",
					    "TFMPNetForQuestionAnswering",
					    "TFMPNetForSequenceClassification",
					    "TFMPNetForTokenClassification",
					    "TFMPNetMainLayer",
					    "TFMPNetModel",
					    "TFMPNetPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
					from .tokenization_mpnet import MPNetTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mpnet_fast import MPNetTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mpnet import (
										    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MPNetForMaskedLM,
										    MPNetForMultipleChoice,
										    MPNetForQuestionAnswering,
										    MPNetForSequenceClassification,
										    MPNetForTokenClassification,
										    MPNetLayer,
										    MPNetModel,
										    MPNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mpnet import (
										    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFMPNetEmbeddings,
										    TFMPNetForMaskedLM,
										    TFMPNetForMultipleChoice,
										    TFMPNetForQuestionAnswering,
										    TFMPNetForSequenceClassification,
										    TFMPNetForTokenClassification,
										    TFMPNetMainLayer,
										    TFMPNetModel,
										    TFMPNetPreTrainedModel,
										)

else:
					import sys

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










"""simple docstring"""




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


_UpperCamelCase:					Tuple     				=	logging.get_logger(__name__)

_UpperCamelCase:					List[str]     				=	{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					'regnet'
   _lowerCamelCase       				=					['x', 'y']


   def __init__(				self   :				Union[str, Any],  lowerCAmelCase   :				Union[str, Any]=3,  lowerCAmelCase   :				Tuple=32,  lowerCAmelCase   :				Optional[int]=[128, 192, 512, 1088],  lowerCAmelCase   :				Optional[int]=[2, 6, 12, 2],  lowerCAmelCase   :				str=64,  lowerCAmelCase   :				List[str]="y",  lowerCAmelCase   :				Optional[int]="relu",  **lowerCAmelCase   :				List[Any],  )     ->							Union[str, Any]:
         super().__init__(**lowerCAmelCase      )
         if layer_type not in self.layer_types:
               raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types      )}'''      )
         lowercase	:   str											= num_channels
         lowercase	:   str											= embedding_size
         lowercase	:   int											= hidden_sizes
         lowercase	:   Tuple											= depths
         lowercase	:   str											= groups_width
         lowercase	:   List[str]											= layer_type
         lowercase	:   Optional[int]											= hidden_act
         # always downsample in the first stage
         lowercase	:   Optional[Any]											= True
