
'''simple docstring'''







import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__SCREAMING_SNAKE_CASE		:  str        =      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE		:  int        =      {
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}
class  SCREAMING_SNAKE_CASE__       (  snake_case_   ):
	lowercase__	    =							"oneformer"
	lowercase__	    =							{"hidden_size": "hidden_dim"}
	def __init__(				self  ,    __UpperCamelCase = None  ,    __UpperCamelCase = 255  ,    __UpperCamelCase = 150  ,    __UpperCamelCase = 0.1  ,    __UpperCamelCase = 2.0  ,    __UpperCamelCase = 5.0  ,    __UpperCamelCase = 5.0  ,    __UpperCamelCase = 0.5  ,    __UpperCamelCase = 0.0_7  ,    __UpperCamelCase = 1_2544  ,    __UpperCamelCase = 3.0  ,    __UpperCamelCase = 0.7_5  ,    __UpperCamelCase = 0.0_2  ,    __UpperCamelCase = 1.0  ,    __UpperCamelCase = 1E-05  ,    __UpperCamelCase = False  ,    __UpperCamelCase = True  ,    __UpperCamelCase = True  ,    __UpperCamelCase = [4, 8, 16, 32]  ,    __UpperCamelCase = 77  ,    __UpperCamelCase = 256  ,    __UpperCamelCase = 77  ,    __UpperCamelCase = 6  ,    __UpperCamelCase = 4_9408  ,    __UpperCamelCase = 2  ,    __UpperCamelCase = 16  ,    __UpperCamelCase = 256  ,    __UpperCamelCase = 256  ,    __UpperCamelCase = 256  ,    __UpperCamelCase = 1024  ,    __UpperCamelCase = "GN"  ,    __UpperCamelCase = 6  ,    __UpperCamelCase = 10  ,    __UpperCamelCase = True  ,    __UpperCamelCase = 8  ,    __UpperCamelCase = 0.1  ,    __UpperCamelCase = 2048  ,    __UpperCamelCase = False  ,    __UpperCamelCase = False  ,    __UpperCamelCase = 2  ,    __UpperCamelCase = 4  ,    **__UpperCamelCase  ,    ):



					'''simple docstring'''






					if backbone_config is None:
									logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""			)
									__a       :     Tuple								=	CONFIG_MAPPING["""swin"""](
									    image_size=224  ,    in_channels=3  ,    patch_size=4  ,    embed_dim=96  ,    depths=[2, 2, 6, 2]  ,    num_heads=[3, 6, 12, 24]  ,    window_size=7  ,    drop_path_rate=0.3  ,    use_absolute_embeddings=__lowerCamelCase  ,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]  ,    )
					elif isinstance(__lowerCamelCase  ,    __lowerCamelCase			):
									__a       :     List[str]								=	backbone_config.get("""model_type"""			)
									__a       :     Optional[Any]								=	CONFIG_MAPPING[backbone_model_type]
									__a       :     List[str]								=	config_class.from_dict(__lowerCamelCase			)

					__a       :     Union[str, Any]								=	backbone_config

					__a       :     Optional[int]								=	ignore_value
					__a       :     Dict								=	num_queries
					__a       :     Optional[int]								=	no_object_weight
					__a       :     Dict								=	class_weight
					__a       :     Any								=	mask_weight
					__a       :     Union[str, Any]								=	dice_weight
					__a       :     int								=	contrastive_weight
					__a       :     Union[str, Any]								=	contrastive_temperature
					__a       :     Optional[int]								=	train_num_points
					__a       :     List[Any]								=	oversample_ratio
					__a       :     str								=	importance_sample_ratio
					__a       :     List[Any]								=	init_std
					__a       :     Dict								=	init_xavier_std
					__a       :     Union[str, Any]								=	layer_norm_eps
					__a       :     List[Any]								=	is_training
					__a       :     Optional[Any]								=	use_auxiliary_loss
					__a       :     List[Any]								=	output_auxiliary_logits
					__a       :     Union[str, Any]								=	strides
					__a       :     Any								=	task_seq_len
					__a       :     Dict								=	text_encoder_width
					__a       :     Optional[Any]								=	text_encoder_context_length
					__a       :     List[str]								=	text_encoder_num_layers
					__a       :     List[Any]								=	text_encoder_vocab_size
					__a       :     Dict								=	text_encoder_proj_layers
					__a       :     int								=	text_encoder_n_ctx
					__a       :     int								=	conv_dim
					__a       :     str								=	mask_dim
					__a       :     Optional[Any]								=	hidden_dim
					__a       :     Any								=	encoder_feedforward_dim
					__a       :     Optional[int]								=	norm
					__a       :     Optional[Any]								=	encoder_layers
					__a       :     Optional[Any]								=	decoder_layers
					__a       :     str								=	use_task_norm
					__a       :     Union[str, Any]								=	num_attention_heads
					__a       :     List[Any]								=	dropout
					__a       :     Dict								=	dim_feedforward
					__a       :     List[str]								=	pre_norm
					__a       :     List[Any]								=	enforce_input_proj
					__a       :     Tuple								=	query_dec_layers
					__a       :     int								=	common_stride
					__a       :     int								=	decoder_layers

					super().__init__(**__lowerCamelCase			)

	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	copy.deepcopy(self.__dict__			)
					__a       :     Optional[Any]								=	self.backbone_config.to_dict()
					__a       :     str								=	self.__class__.model_type
					return output







"""simple docstring"""

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      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     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						)						)



