




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


_A							    =	logging.get_logger(__name__)

_A							    =	{
    """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}


class 							_lowerCAmelCase		(     UpperCamelCase__	):
				lowerCamelCase__									=    'cpmant'




				def __init__( self		,		snake_case_ = 30_720		,		snake_case_ = 4_096		,		snake_case_ = 32		,		snake_case_ = 128		,		snake_case_ = 10_240		,		snake_case_ = 48		,		snake_case_ = 0.0		,		snake_case_ = 512		,		snake_case_ = 2_048		,		snake_case_ = 1E-6		,		snake_case_ = 1.0		,		snake_case_ = 32		,		snake_case_ = 32		,		snake_case_ = 32		,		snake_case_ = True		,		**snake_case_		,		)    ->      Union[str, Any]:
											super().__init__(**snake_case_							)
											SCREAMING_SNAKE_CASE			: Tuple	     =prompt_types
											SCREAMING_SNAKE_CASE			: Tuple	     =prompt_length
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =segment_types
											SCREAMING_SNAKE_CASE			: Tuple	     =hidden_size
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =num_attention_heads
											SCREAMING_SNAKE_CASE			: str	     =dim_head
											SCREAMING_SNAKE_CASE			: Tuple	     =dim_ff
											SCREAMING_SNAKE_CASE			: Any	     =num_hidden_layers
											SCREAMING_SNAKE_CASE			: Tuple	     =position_bias_num_buckets
											SCREAMING_SNAKE_CASE			: Tuple	     =position_bias_max_distance
											SCREAMING_SNAKE_CASE			: Optional[int]	     =dropout_p
											SCREAMING_SNAKE_CASE			: int	     =eps
											SCREAMING_SNAKE_CASE			: Any	     =use_cache
											SCREAMING_SNAKE_CASE			: Optional[int]	     =vocab_size
											SCREAMING_SNAKE_CASE			: List[Any]	     =init_std












from typing import TYPE_CHECKING

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


_A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RegNetForImageClassification""",
							    """RegNetModel""",
							    """RegNetPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRegNetForImageClassification""",
							    """TFRegNetModel""",
							    """TFRegNetPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """FlaxRegNetForImageClassification""",
							    """FlaxRegNetModel""",
							    """FlaxRegNetPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_regnet import (
														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RegNetForImageClassification,
														    RegNetModel,
														    RegNetPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_regnet import (
														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRegNetForImageClassification,
														    TFRegNetModel,
														    TFRegNetPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_regnet import (
														    FlaxRegNetForImageClassification,
														    FlaxRegNetModel,
														    FlaxRegNetPreTrainedModel,
														)


else:
							import sys

							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)






