


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


_SCREAMING_SNAKE_CASE			: Optional[Any]			      =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			: Optional[int]			      =    {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}

class   A__					(    A__	):







    """simple docstring"""

    __magic_name__			  =		'beit'
    def __init__(       self			,					__snake_case=8_1_9_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=0.0			,					__snake_case=0.0			,					__snake_case=0.02			,					__snake_case=1E-12			,					__snake_case=2_2_4			,					__snake_case=1_6			,					__snake_case=3			,					__snake_case=False			,					__snake_case=False			,					__snake_case=False			,					__snake_case=False			,					__snake_case=0.1			,					__snake_case=0.1			,					__snake_case=True			,					__snake_case=[3, 5, 7, 1_1]			,					__snake_case=[1, 2, 3, 6]			,					__snake_case=True			,					__snake_case=0.4			,					__snake_case=2_5_6			,					__snake_case=1			,					__snake_case=False			,					__snake_case=2_5_5			,					**__snake_case			,					):
         super().__init__(**__snake_case    )

         snake_case		 =				vocab_size
         snake_case		 =				hidden_size
         snake_case		 =				num_hidden_layers
         snake_case		 =				num_attention_heads
         snake_case		 =				intermediate_size
         snake_case		 =				hidden_act
         snake_case		 =				hidden_dropout_prob
         snake_case		 =				attention_probs_dropout_prob
         snake_case		 =				initializer_range
         snake_case		 =				layer_norm_eps

         snake_case		 =				image_size
         snake_case		 =				patch_size
         snake_case		 =				num_channels
         snake_case		 =				use_mask_token
         snake_case		 =				use_absolute_position_embeddings
         snake_case		 =				use_relative_position_bias
         snake_case		 =				use_shared_relative_position_bias
         snake_case		 =				layer_scale_init_value
         snake_case		 =				drop_path_rate
         snake_case		 =				use_mean_pooling
         # decode head attributes (semantic segmentation)
         snake_case		 =				out_indices
         snake_case		 =				pool_scales
         # auxiliary head attributes (semantic segmentation)
         snake_case		 =				use_auxiliary_head
         snake_case		 =				auxiliary_loss_weight
         snake_case		 =				auxiliary_channels
         snake_case		 =				auxiliary_num_convs
         snake_case		 =				auxiliary_concat_input
         snake_case		 =				semantic_loss_ignore_index






class   A__					(    A__	):







    """simple docstring"""

    __magic_name__			  =		version.parse('1.11'	)
    @property
    def 		a_			(       self    ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]    )




    @property
    def 		a_			(       self    ):
         return 1E-4












from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




