




"""simple docstring"""




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


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''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 		A__    (       __SCREAMING_SNAKE_CASE			):
	lowerCamelCase__		:			int       ="vit-hybrid"






	def __init__(     self    ,					lowerCamelCase=None    ,					lowerCamelCase=768    ,					lowerCamelCase=12    ,					lowerCamelCase=12    ,					lowerCamelCase=3072    ,					lowerCamelCase="gelu"    ,					lowerCamelCase=0.0    ,					lowerCamelCase=0.0    ,					lowerCamelCase=0.0_2    ,					lowerCamelCase=1e-12    ,					lowerCamelCase=224    ,					lowerCamelCase=1    ,					lowerCamelCase=3    ,					lowerCamelCase=[1, 1024, 24, 24]    ,					lowerCamelCase=True    ,					**lowerCamelCase    ,					)				->       Dict:




						"""simple docstring"""






						super().__init__(**lowercase__						)

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

						if isinstance(lowercase__    ,					lowercase__						):
											if "model_type" in backbone_config:
																__magic_name__ :							Optional[int]		       =							CONFIG_MAPPING[backbone_config['''model_type''']]
											else:
																logger.info(
																    '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''						)
																__magic_name__ :							str		       =							BitConfig
											__magic_name__ :							List[str]		       =							backbone_config_class(**lowercase__						)

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






	def 			lowercase				(     self						)				->       Dict[str, any]:




						"""simple docstring"""






						__magic_name__ :							int		       =							copy.deepcopy(self.__dict__						)
						__magic_name__ :							Optional[int]		       =							self.backbone_config.to_dict()
						__magic_name__ :							Tuple		       =							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 		A__    (       unittest.TestCase			):






	@slow
	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							CamembertModel.from_pretrained('''camembert-base'''						)
						model.to(lowerCamelCase						)

						__magic_name__ :							Dict		       =							torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]    ,					device=lowerCamelCase    ,					dtype=torch.long    ,					)  # J'aime le camembert !
						with torch.no_grad():
											__magic_name__ :							Tuple		       =							model(lowerCamelCase						)['''last_hidden_state''']
						__magic_name__ :							int		       =							torch.Size((1, 10, 768)						)
						self.assertEqual(output.shape    ,					lowerCamelCase						)
						# compare the actual values for a slice.
						__magic_name__ :							Tuple		       =							torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]    ,					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						)						)



