




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


__lowerCamelCase      :    Union[str, Any]   		=   logging.get_logger(__name__)

__lowerCamelCase      :    int   		=   {
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"data2vec-vision"



					def __init__(      self  :						Any							,	__A  :						Optional[Any]=7_6_8							,	__A  :						Dict=1_2							,	__A  :						List[Any]=1_2							,	__A  :						Optional[int]=3_0_7_2							,	__A  :						Tuple="gelu"							,	__A  :						Tuple=0.0							,	__A  :						int=0.0							,	__A  :						Union[str, Any]=0.0_2							,	__A  :						Any=1e-1_2							,	__A  :						Dict=2_2_4							,	__A  :						Any=1_6							,	__A  :						List[str]=3							,	__A  :						int=False							,	__A  :						Union[str, Any]=False							,	__A  :						Union[str, Any]=False							,	__A  :						Tuple=False							,	__A  :						Optional[int]=0.1							,	__A  :						Dict=0.1							,	__A  :						Any=True							,	__A  :						Dict=[3, 5, 7, 1_1]							,	__A  :						Any=[1, 2, 3, 6]							,	__A  :						Tuple=True							,	__A  :						Dict=0.4							,	__A  :						List[str]=2_5_6							,	__A  :						Any=1							,	__A  :						List[str]=False							,	__A  :						Optional[int]=2_5_5							,	**__A  :						Dict							,	):
						super().__init__(**__A )

						snake_case__				:   str     =						hidden_size
						snake_case__				:   List[str]     =						num_hidden_layers
						snake_case__				:   Optional[int]     =						num_attention_heads
						snake_case__				:   int     =						intermediate_size
						snake_case__				:   str     =						hidden_act
						snake_case__				:   Optional[Any]     =						hidden_dropout_prob
						snake_case__				:   List[Any]     =						attention_probs_dropout_prob
						snake_case__				:   Dict     =						initializer_range
						snake_case__				:   List[Any]     =						layer_norm_eps

						snake_case__				:   Union[str, Any]     =						image_size
						snake_case__				:   str     =						patch_size
						snake_case__				:   Tuple     =						num_channels
						snake_case__				:   Optional[int]     =						use_mask_token
						snake_case__				:   List[str]     =						use_absolute_position_embeddings
						snake_case__				:   Any     =						use_relative_position_bias
						snake_case__				:   Optional[Any]     =						use_shared_relative_position_bias
						snake_case__				:   Optional[Any]     =						layer_scale_init_value
						snake_case__				:   str     =						drop_path_rate
						snake_case__				:   Optional[int]     =						use_mean_pooling
						# decode head attributes (semantic segmentation)
						snake_case__				:   Optional[Any]     =						out_indices
						snake_case__				:   List[str]     =						pool_scales
						# auxiliary head attributes (semantic segmentation)
						snake_case__				:   List[str]     =						use_auxiliary_head
						snake_case__				:   List[Any]     =						auxiliary_loss_weight
						snake_case__				:   Dict     =						auxiliary_channels
						snake_case__				:   int     =						auxiliary_num_convs
						snake_case__				:   List[str]     =						auxiliary_concat_input
						snake_case__				:   str     =						semantic_loss_ignore_index



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	version.parse("1.11"		)



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






					@property
					def  _lowercase  (      self  :						Optional[Any] ):
						return 1e-4











import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.activations import gelu_new, gelu_python, get_activation



@require_torch
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   List[str]     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Tuple     =						get_activation("gelu" )
						self.assertTrue(torch.allclose(gelu_python(__A )							,	torch_builtin(__A ) ) )
						self.assertFalse(torch.allclose(gelu_python(__A )							,	gelu_new(__A ) ) )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   str     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						snake_case__				:   int     =						get_activation("gelu_10" )

						snake_case__				:   Optional[int]     =						torch_builtin(__A )
						snake_case__				:   Dict     =						geluaa(__A )

						snake_case__				:   Optional[Any]     =						torch.where(y_gelu_aa < 1_0.0							,	1							,	0 )

						self.assertTrue(torch.max(__A ).item() == 1_0.0 )
						self.assertTrue(torch.allclose(y_gelu * clipped_mask							,	y_gelu_aa * clipped_mask ) )



					def  _lowercase  (      self  :						str ):
						get_activation("gelu" )
						get_activation("gelu_10" )
						get_activation("gelu_fast" )
						get_activation("gelu_new" )
						get_activation("gelu_python" )
						get_activation("gelu_pytorch_tanh" )
						get_activation("linear" )
						get_activation("mish" )
						get_activation("quick_gelu" )
						get_activation("relu" )
						get_activation("sigmoid" )
						get_activation("silu" )
						get_activation("swish" )
						get_activation("tanh" )
						with self.assertRaises(__A ):
							get_activation("bogus" )
						with self.assertRaises(__A ):
							get_activation(__A )






					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   List[str]     =						get_activation("gelu" )
						snake_case__				:   Any     =						1
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						self.assertEqual(acta.a							,	1 )
						with self.assertRaises(__A ):
							snake_case__				:   int     =						acta.a



