

"""simple docstring"""




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


__lowerCamelCase											=  logging.get_logger(__name__)

__lowerCamelCase											=  {
    "tanreinama/GPTSAN-2.8B-spout_is_uniform": (
        "https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
    ),
}
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Optional[int]													=		'gptsan-japanese'
						lowerCAmelCase__  :						List[Any]													=		[
						    'past_key_values',
						]
						lowerCAmelCase__  :						int													=		{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}





						def __init__(       self		,__UpperCAmelCase=3_60_00		,__UpperCAmelCase=12_80		,__UpperCAmelCase=10_24		,__UpperCAmelCase=81_92		,__UpperCAmelCase=40_96		,__UpperCAmelCase=1_28		,__UpperCAmelCase=10		,__UpperCAmelCase=0		,__UpperCAmelCase=16		,__UpperCAmelCase=16		,__UpperCAmelCase=1_28		,__UpperCAmelCase=0.0		,__UpperCAmelCase=1e-5		,__UpperCAmelCase=False		,__UpperCAmelCase=0.0		,__UpperCAmelCase="float32"		,__UpperCAmelCase=False		,__UpperCAmelCase=False		,__UpperCAmelCase=False		,__UpperCAmelCase=0.0_0_2		,__UpperCAmelCase=False		,__UpperCAmelCase=True		,__UpperCAmelCase=3_59_98		,__UpperCAmelCase=3_59_95		,__UpperCAmelCase=3_59_99		,**__UpperCAmelCase		,)					->				Union[str, Any]:
													A__	     =							vocab_size
													A__	     =							max_position_embeddings
													A__	     =							d_model
													A__	     =							d_ff
													A__	     =							d_ext
													A__	     =							d_spout
													A__	     =							num_switch_layers
													A__	     =							num_ext_layers
													A__	     =							num_switch_layers + num_ext_layers
													A__	     =							num_heads
													A__	     =							num_experts
													A__	     =							expert_capacity
													A__	     =							dropout_rate
													A__	     =							layer_norm_epsilon
													A__	     =							router_bias
													A__	     =							router_jitter_noise
													A__	     =							router_dtype
													A__	     =							router_ignore_padding_tokens
													A__	     =							output_hidden_states
													A__	     =							output_attentions
													A__	     =							initializer_factor
													A__	     =							output_router_logits
													A__	     =							use_cache

													super().__init__(
													    separator_token_id=__UpperCAmelCase		,pad_token_id=__UpperCAmelCase		,eos_token_id=__UpperCAmelCase		,**__UpperCAmelCase		,)






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase											=  {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  ["GLPNFeatureExtractor"]
				__lowerCamelCase											=  ["GLPNImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  [
				    "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "GLPNForDepthEstimation",
				    "GLPNLayer",
				    "GLPNModel",
				    "GLPNPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_glpn import GLPNFeatureExtractor
								from .image_processing_glpn import GLPNImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_glpn import (
								    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GLPNForDepthEstimation,
								    GLPNLayer,
								    GLPNModel,
								    GLPNPreTrainedModel,
								)


else:
				import sys

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





