


'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A							:					Tuple     						=     logging.get_logger(__name__)

A							:					int     						=     {
    'shi-labs/dinat-mini-in1k-224': 'https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}






class     UpperCamelCase(  __lowercase     ,							__lowercase   ):
					snake_case_		:				Optional[Any]										=	'dinat'

					snake_case_		:				str										=	{
					    'num_attention_heads': 'num_heads',
					    'num_hidden_layers': 'num_layers',
					}


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Any=4     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=6_4     ,    SCREAMING_SNAKE_CASE	:		Any=[3, 4, 6, 5]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=[2, 4, 8, 1_6]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]     ,    SCREAMING_SNAKE_CASE	:		Tuple=3.0     ,    SCREAMING_SNAKE_CASE	:		int=True     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		List[str]=0.0     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=0.02     ,    SCREAMING_SNAKE_CASE	:		Any=1e-5     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    SCREAMING_SNAKE_CASE	:		Any=None     ,    **SCREAMING_SNAKE_CASE	:		int     ,    )			->	Union[str, Any]:




											'''simple docstring'''

											super().__init__(**__A						)

											__snake_case				=		patch_size
											__snake_case				=		num_channels
											__snake_case				=		embed_dim
											__snake_case				=		depths
											__snake_case				=		len(__A						)
											__snake_case				=		num_heads
											__snake_case				=		kernel_size
											__snake_case				=		dilations
											__snake_case				=		mlp_ratio
											__snake_case				=		qkv_bias
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		drop_path_rate
											__snake_case				=		hidden_act
											__snake_case				=		layer_norm_eps
											__snake_case				=		initializer_range
											# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
											# this indicates the channel dimension after the last stage of the model
											__snake_case				=		int(embed_dim * 2 ** (len(__A						) - 1)						)
											__snake_case				=		layer_scale_init_value
											__snake_case				=		["stem"] + [f'''stage{idx}''' for idx in range(1     ,    len(__A						) + 1						)]
											__snake_case		,					__snake_case				=		get_aligned_output_features_output_indices(
											    out_features=__A     ,    out_indices=__A     ,    stage_names=self.stage_names						)









import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A							:					int     						=     logging.get_logger(__name__)  # pylint: disable=invalid-name






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict=7_6_8						)			->	int:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE						)
											__snake_case				=		proj_size

											__snake_case				=		CLIPVisionModel(SCREAMING_SNAKE_CASE						)
											__snake_case				=		PaintByExampleMapper(SCREAMING_SNAKE_CASE						)
											__snake_case				=		nn.LayerNorm(config.hidden_size						)
											__snake_case				=		nn.Linear(config.hidden_size     ,    self.proj_size						)

											# uncondition for scaling
											__snake_case				=		nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model(pixel_values=SCREAMING_SNAKE_CASE						)
											__snake_case				=		clip_output.pooler_output
											__snake_case				=		self.mapper(latent_states[:, None]						)
											__snake_case				=		self.final_layer_norm(SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.proj_out(SCREAMING_SNAKE_CASE						)
											if return_uncond_vector:
																	return latent_states, self.uncond_vector

											return latent_states






class     UpperCamelCase(  nn.Module   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	List[Any]:




											'''simple docstring'''

											super().__init__()
											__snake_case				=		(config.num_hidden_layers + 1) // 5
											__snake_case				=		config.hidden_size
											__snake_case				=		1
											__snake_case				=		nn.ModuleList(
											    [
											        BasicTransformerBlock(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    activation_fn="gelu"     ,    attention_bias=SCREAMING_SNAKE_CASE						)
											        for _ in range(SCREAMING_SNAKE_CASE						)
											    ]						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Optional[int]:




											'''simple docstring'''

											for block in self.blocks:
																	__snake_case				=		block(SCREAMING_SNAKE_CASE						)

											return hidden_states





