

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


a_      =     logging.get_logger(__name__)

a_      =     {
    'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
        'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
    ),
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='gptsan-japanese'
			lowerCamelCase__							     =[
			    'past_key_values',
			]
			lowerCamelCase__							     ={
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'num_heads',
			    'num_hidden_layers': 'num_layers',
			}
			def __init__(      self				:			int   ,   a				:			Any=3_6000   ,   a				:			Optional[int]=1280   ,   a				:			Tuple=1024   ,   a				:			Optional[Any]=8192   ,   a				:			List[str]=4096   ,   a				:			Optional[Any]=128   ,   a				:			Optional[Any]=10   ,   a				:			List[Any]=0   ,   a				:			Union[str, Any]=16   ,   a				:			str=16   ,   a				:			Tuple=128   ,   a				:			List[str]=0.0   ,   a				:			Union[str, Any]=1e-5   ,   a				:			int=False   ,   a				:			Optional[int]=0.0   ,   a				:			Union[str, Any]="float32"   ,   a				:			str=False   ,   a				:			Dict=False   ,   a				:			Dict=False   ,   a				:			Optional[Any]=0.002   ,   a				:			Tuple=False   ,   a				:			List[str]=True   ,   a				:			int=3_5998   ,   a				:			Union[str, Any]=3_5995   ,   a				:			int=3_5999   ,   **a				:			Optional[int]   ,   )				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							vocab_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   List[Any]					  =							d_model
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							d_ff
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							d_ext
				SCREAMING_SNAKE_CASE :   List[str]					  =							d_spout
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							num_switch_layers
				SCREAMING_SNAKE_CASE :   str					  =							num_ext_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_switch_layers + num_ext_layers
				SCREAMING_SNAKE_CASE :   str					  =							num_heads
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							num_experts
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							expert_capacity
				SCREAMING_SNAKE_CASE :   List[str]					  =							dropout_rate
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							layer_norm_epsilon
				SCREAMING_SNAKE_CASE :   List[str]					  =							router_bias
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							router_jitter_noise
				SCREAMING_SNAKE_CASE :   Tuple					  =							router_dtype
				SCREAMING_SNAKE_CASE :   Dict					  =							router_ignore_padding_tokens
				SCREAMING_SNAKE_CASE :   Tuple					  =							output_hidden_states
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							output_attentions
				SCREAMING_SNAKE_CASE :   str					  =							initializer_factor
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							output_router_logits
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							use_cache

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_      =     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'InstructBlipQFormerModel',
			    'InstructBlipPreTrainedModel',
			    'InstructBlipForConditionalGeneration',
			    'InstructBlipVisionModel',
			]

if TYPE_CHECKING:
			from .configuration_instructblip import (
			    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    InstructBlipConfig,
			    InstructBlipQFormerConfig,
			    InstructBlipVisionConfig,
			)
			from .processing_instructblip import InstructBlipProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InstructBlipForConditionalGeneration,
						    InstructBlipPreTrainedModel,
						    InstructBlipQFormerModel,
						    InstructBlipVisionModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)