

from typing import TYPE_CHECKING

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


lowercase_       		=  {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_       		=  ["DeiTFeatureExtractor"]
     lowercase_       		=  ["DeiTImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_       		=  [
         "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DeiTForImageClassification",
         "DeiTForImageClassificationWithTeacher",
         "DeiTForMaskedImageModeling",
         "DeiTModel",
         "DeiTPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_       		=  [
         "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDeiTForImageClassification",
         "TFDeiTForImageClassificationWithTeacher",
         "TFDeiTForMaskedImageModeling",
         "TFDeiTModel",
         "TFDeiTPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_deit import DeiTFeatureExtractor
          from .image_processing_deit import DeiTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deit import (
              DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DeiTForImageClassification,
              DeiTForImageClassificationWithTeacher,
              DeiTForMaskedImageModeling,
              DeiTModel,
              DeiTPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_deit import (
              TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDeiTForImageClassification,
              TFDeiTForImageClassificationWithTeacher,
              TFDeiTForMaskedImageModeling,
              TFDeiTModel,
              TFDeiTPreTrainedModel,
          )


else:
     import sys

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








'''simple docstring'''







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


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    int      	=			{
    'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='gpt_bigcode'
				__a            =['past_key_values']
				__a            ={
				    'hidden_size': 'n_embd',
				    'max_position_embeddings': 'n_positions',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}




				def __init__(  self     :					Optional[Any]					,				__a     :					Tuple=5_02_57					,				__a     :					str=10_24					,				__a     :					Dict=7_68					,				__a     :					Tuple=12					,				__a     :					str=12					,				__a     :					Optional[int]=None					,				__a     :					Dict="gelu_pytorch_tanh"					,				__a     :					Tuple=0.1					,				__a     :					Tuple=0.1					,				__a     :					Union[str, Any]=0.1					,				__a     :					Tuple=1e-5					,				__a     :					str=0.02					,				__a     :					Dict=True					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[int]=5_02_56					,				__a     :					Optional[int]=5_02_56					,				__a     :					Union[str, Any]=True					,				__a     :					Dict=True					,				__a     :					Union[str, Any]=True					,				**__a     :					List[Any]					,				):
								_a								= vocab_size
								_a								= n_positions
								_a								= n_embd
								_a								= n_layer
								_a								= n_head
								_a								= n_inner
								_a								= activation_function
								_a								= resid_pdrop
								_a								= embd_pdrop
								_a								= attn_pdrop
								_a								= layer_norm_epsilon
								_a								= initializer_range
								_a								= scale_attn_weights
								_a								= use_cache
								_a								= attention_softmax_in_fpaa
								_a								= scale_attention_softmax_in_fpaa
								_a								= multi_query

								_a								= bos_token_id
								_a								= eos_token_id

								super().__init__(bos_token_id=__a					,				eos_token_id=__a					,				**__a					)
