
import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__lowerCamelCase								=					logging.get_logger(__name__)

__lowerCamelCase								=					{
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):
						__A:						Optional[int]				   =      """vit-hybrid"""

						def __init__(					self		:			Tuple	,						_lowerCamelCase		:			List[str]=None	,						_lowerCamelCase		:			List[str]=7_68	,						_lowerCamelCase		:			List[Any]=12	,						_lowerCamelCase		:			List[Any]=12	,						_lowerCamelCase		:			Optional[int]=30_72	,						_lowerCamelCase		:			Union[str, Any]="gelu"	,						_lowerCamelCase		:			Dict=0.0	,						_lowerCamelCase		:			str=0.0	,						_lowerCamelCase		:			List[str]=0.02	,						_lowerCamelCase		:			Dict=1E-12	,						_lowerCamelCase		:			Dict=2_24	,						_lowerCamelCase		:			Optional[Any]=1	,						_lowerCamelCase		:			Tuple=3	,						_lowerCamelCase		:			int=[1, 10_24, 24, 24]	,						_lowerCamelCase		:			Any=True	,						**_lowerCamelCase		:			Optional[Any]	,						):
													super().__init__(**_lowerCamelCase			)

													if backbone_config is None:
																				logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."			)
																				_UpperCAmelCase  :			Tuple	       =		{
																				    "global_padding": "same",
																				    "layer_type": "bottleneck",
																				    "depths": [3, 4, 9],
																				    "out_features": ["stage3"],
																				    "embedding_dynamic_padding": True,
																				}

													if isinstance(_lowerCamelCase	,						_lowerCamelCase			):
																				if "model_type" in backbone_config:
																											_UpperCAmelCase  :			Union[str, Any]	       =		CONFIG_MAPPING[backbone_config["model_type"]]
																				else:
																											logger.info(
																											    "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."			)
																											_UpperCAmelCase  :			str	       =		BitConfig
																				_UpperCAmelCase  :			Any	       =		backbone_config_class(**_lowerCamelCase			)

													_UpperCAmelCase  :			List[str]	       =		backbone_featmap_shape
													_UpperCAmelCase  :			str	       =		backbone_config
													_UpperCAmelCase  :			Union[str, Any]	       =		hidden_size
													_UpperCAmelCase  :			str	       =		num_hidden_layers
													_UpperCAmelCase  :			Union[str, Any]	       =		num_attention_heads
													_UpperCAmelCase  :			Tuple	       =		intermediate_size
													_UpperCAmelCase  :			Tuple	       =		hidden_act
													_UpperCAmelCase  :			Any	       =		hidden_dropout_prob
													_UpperCAmelCase  :			Tuple	       =		attention_probs_dropout_prob
													_UpperCAmelCase  :			Union[str, Any]	       =		initializer_range
													_UpperCAmelCase  :			str	       =		layer_norm_eps
													_UpperCAmelCase  :			Tuple	       =		image_size
													_UpperCAmelCase  :			Tuple	       =		patch_size
													_UpperCAmelCase  :			Optional[int]	       =		num_channels
													_UpperCAmelCase  :			Tuple	       =		qkv_bias






						def 						a__ (					self		:			Union[str, Any]			):
													_UpperCAmelCase  :			str	       =		copy.deepcopy(self.__dict__			)
													_UpperCAmelCase  :			str	       =		self.backbone_config.to_dict()
													_UpperCAmelCase  :			List[Any]	       =		self.__class__.model_type
													return output






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class    _UpperCamelCase(   unittest.TestCase				):

						@slow
						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Tuple	       =		CamembertModel.from_pretrained("camembert-base"			)
													model.to(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		torch.tensor(
													    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,						device=_lowerCamelCase	,						dtype=torch.long	,						)  # J'aime le camembert !
													with torch.no_grad():
																				_UpperCAmelCase  :			Dict	       =		model(_lowerCamelCase			)["last_hidden_state"]
													_UpperCAmelCase  :			Optional[int]	       =		torch.Size((1, 10, 7_68)			)
													self.assertEqual(output.shape	,						_lowerCamelCase			)
													# compare the actual values for a slice.
													_UpperCAmelCase  :			List[str]	       =		torch.tensor(
													    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]	,						device=_lowerCamelCase	,						dtype=torch.float	,						)
													# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
													# camembert.eval()
													# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

													self.assertTrue(torch.allclose(output[:, :3, :3]	,						_lowerCamelCase	,						atol=1E-4			)			)




