




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


snake_case_ : Dict    		=       logging.get_logger(__name__)

snake_case_ : int    		=       {
    "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 __snake_case			(			a		):
   UpperCAmelCase__  :    Dict          =  '''vit'''







   def __init__(      self		:						Any					,						_snake_case		:						List[Any]=768					,						_snake_case		:						Any=12					,						_snake_case		:						List[Any]=12					,						_snake_case		:						Union[str, Any]=3072					,						_snake_case		:						Dict="gelu"					,						_snake_case		:						List[str]=0.0					,						_snake_case		:						List[Any]=0.0					,						_snake_case		:						Optional[Any]=0.0_2					,						_snake_case		:						Tuple=1e-12					,						_snake_case		:						Dict=224					,						_snake_case		:						Tuple=16					,						_snake_case		:						int=3					,						_snake_case		:						List[str]=True					,						_snake_case		:						Optional[int]=16					,						**_snake_case		:						Tuple					,						):

        """simple docstring"""


        super().__init__(**_snake_case)

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







class __snake_case			(			a		):
   UpperCAmelCase__  :    Optional[Any]          =  version.parse('''1.11'''		)







   @property
   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        return OrderedDict(
            [
                ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
            ])







   @property
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        return 1e-4












from typing import TYPE_CHECKING

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


snake_case_ : Union[str, Any]    		=       {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Dict    		=       ["GPTNeoXTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       [
        "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
        "GPTNeoXForCausalLM",
        "GPTNeoXForQuestionAnswering",
        "GPTNeoXForSequenceClassification",
        "GPTNeoXForTokenClassification",
        "GPTNeoXLayer",
        "GPTNeoXModel",
        "GPTNeoXPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_neox import (
            GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
            GPTNeoXLayer,
            GPTNeoXModel,
            GPTNeoXPreTrainedModel,
        )


else:
    import sys

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






