"""simple docstring"""







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


_a	:	Union[str, Any]=					logging.get_logger(__name__)

_a	:	Union[str, Any]=					{
    "facebook/regnet-y-040": "https://huggingface.co/facebook/regnet-y-040/blob/main/config.json",
}

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: List[str]       =						"""regnet"""
				UpperCAmelCase							: Optional[Any]       =						["""x""", """y"""]



				def __init__(self							: Optional[int]      ,    _A							: List[str]=3      ,    _A							: List[str]=32      ,    _A							: Optional[Any]=[1_28, 1_92, 5_12, 10_88]      ,    _A							: str=[2, 6, 12, 2]      ,    _A							: Optional[Any]=64      ,    _A							: Dict="y"      ,    _A							: Optional[int]="relu"      ,    **_A							: Optional[Any]      ,    )       ->      int:
					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)}")
					__snake_case     :				int 						=					num_channels
					__snake_case     :				str 						=					embedding_size
					__snake_case     :				Optional[Any] 						=					hidden_sizes
					__snake_case     :				List[Any] 						=					depths
					__snake_case     :				List[str] 						=					groups_width
					__snake_case     :				Dict 						=					layer_type
					__snake_case     :				int 						=					hidden_act
					# always downsample in the first stage
					__snake_case     :				str 						=					True






"""simple docstring"""







from typing import TYPE_CHECKING

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


_a	:	Optional[Any]=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Dict=					[
	    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTJForCausalLM",
	    "GPTJForQuestionAnswering",
	    "GPTJForSequenceClassification",
	    "GPTJModel",
	    "GPTJPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Optional[Any]=					[
	    "TFGPTJForCausalLM",
	    "TFGPTJForQuestionAnswering",
	    "TFGPTJForSequenceClassification",
	    "TFGPTJModel",
	    "TFGPTJPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	List[str]=					[
	    "FlaxGPTJForCausalLM",
	    "FlaxGPTJModel",
	    "FlaxGPTJPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

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





