




"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__    :							List[Any]            =					logging.get_logger(__name__)

a__    :							Tuple            =					{
    """google/vit-base-patch16-224""": """https://huggingface.co/vit-base-patch16-224/resolve/main/config.json""",
    # See all ViT models at https://huggingface.co/models?filter=vit
}




class 			UpperCamelCase_     (			lowerCamelCase__):

	"""simple docstring"""





	snake_case__    :  List[str]    =      'vit'




	def __init__(			self      :    str      ,      UpperCAmelCase__      :    Optional[int]=7_6_8      ,      UpperCAmelCase__      :    str=1_2      ,      UpperCAmelCase__      :    Optional[int]=1_2      ,      UpperCAmelCase__      :    List[Any]=3_0_7_2      ,      UpperCAmelCase__      :    Optional[int]="gelu"      ,      UpperCAmelCase__      :    List[str]=0.0      ,      UpperCAmelCase__      :    Union[str, Any]=0.0      ,      UpperCAmelCase__      :    Optional[int]=0.02      ,      UpperCAmelCase__      :    List[Any]=1E-12      ,      UpperCAmelCase__      :    List[Any]=2_2_4      ,      UpperCAmelCase__      :    Union[str, Any]=1_6      ,      UpperCAmelCase__      :    Dict=3      ,      UpperCAmelCase__      :    Union[str, Any]=True      ,      UpperCAmelCase__      :    List[Any]=1_6      ,      **UpperCAmelCase__      :    Tuple      ,      )	->     Union[str, Any]:
						super().__init__(**UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    hidden_size
						__SCREAMING_SNAKE_CASE						       =    num_hidden_layers
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    intermediate_size
						__SCREAMING_SNAKE_CASE						       =    hidden_act
						__SCREAMING_SNAKE_CASE						       =    hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    layer_norm_eps
						__SCREAMING_SNAKE_CASE						       =    image_size
						__SCREAMING_SNAKE_CASE						       =    patch_size
						__SCREAMING_SNAKE_CASE						       =    num_channels
						__SCREAMING_SNAKE_CASE						       =    qkv_bias
						__SCREAMING_SNAKE_CASE						       =    encoder_stride




class 			UpperCamelCase_     (			lowerCamelCase__):

	"""simple docstring"""





	snake_case__    :  Tuple    =      version.parse("1.11")




	@property
	def   UpperCAmelCase_				(			self      :    int       )	->     Union[str, Any]:
						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						    ]       )




	@property
	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     Optional[Any]:
						return 1E-4




"""simple docstring"""







from typing import TYPE_CHECKING

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


a__    :							List[str]            =					{
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					[
		    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BioGptForCausalLM''',
		    '''BioGptForTokenClassification''',
		    '''BioGptForSequenceClassification''',
		    '''BioGptModel''',
		    '''BioGptPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a__    :							Optional[int]            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
