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


snake_case_      :					Any				      =     logging.get_logger(__name__)

snake_case_      :					Dict				      =     {
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				Optional[Any]      =							'''qdqbert'''



							def __init__(     self	,				__snake_case=3_0_5_2_2	,				__snake_case=7_6_8	,				__snake_case=1_2	,				__snake_case=1_2	,				__snake_case=3_0_7_2	,				__snake_case="gelu"	,				__snake_case=0.1	,				__snake_case=0.1	,				__snake_case=5_1_2	,				__snake_case=2	,				__snake_case=0.02	,				__snake_case=1e-1_2	,				__snake_case=True	,				__snake_case=1	,				__snake_case=0	,				__snake_case=2	,				**__snake_case	,				)						->     Tuple:






								"""simple docstring"""



								super().__init__(pad_token_id=__snake_case	,				bos_token_id=__snake_case	,				eos_token_id=__snake_case	,				**__snake_case							)

								UpperCAmelCase:					Any 						=     vocab_size
								UpperCAmelCase:					Optional[Any] 						=     max_position_embeddings
								UpperCAmelCase:					Optional[Any] 						=     hidden_size
								UpperCAmelCase:					Any 						=     num_hidden_layers
								UpperCAmelCase:					List[str] 						=     num_attention_heads
								UpperCAmelCase:					Optional[Any] 						=     intermediate_size
								UpperCAmelCase:					str 						=     hidden_act
								UpperCAmelCase:					Optional[Any] 						=     hidden_dropout_prob
								UpperCAmelCase:					int 						=     attention_probs_dropout_prob
								UpperCAmelCase:					List[str] 						=     initializer_range
								UpperCAmelCase:					Any 						=     type_vocab_size
								UpperCAmelCase:					str 						=     layer_norm_eps
								UpperCAmelCase:					List[str] 						=     use_cache


from typing import TYPE_CHECKING

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


snake_case_      :					Any				      =     {
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					str				      =     ['ImageGPTFeatureExtractor']
	snake_case_      :					Optional[int]				      =     ['ImageGPTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					List[str]				      =     [
	    'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ImageGPTForCausalImageModeling',
	    'ImageGPTForImageClassification',
	    'ImageGPTModel',
	    'ImageGPTPreTrainedModel',
	    'load_tf_weights_in_imagegpt',
	]


if TYPE_CHECKING:
	from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
		from .image_processing_imagegpt import ImageGPTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_imagegpt import (
		    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ImageGPTForCausalImageModeling,
		    ImageGPTForImageClassification,
		    ImageGPTModel,
		    ImageGPTPreTrainedModel,
		    load_tf_weights_in_imagegpt,
		)

else:
	import sys

	snake_case_      :					Dict				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
