






"""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						)						)











"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase           =							logging.get_logger(__name__)

lowercase           =							{
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}

class     A_				(   snake_case_	):







				def __init__(				self				:    List[Any]    ,     __lowerCamelCase				:    str=2_5_6    ,     __lowerCamelCase				:    str=1_0_2_4    ,     __lowerCamelCase				:    Optional[Any]=1_6    ,     __lowerCamelCase				:    Union[str, Any]=1_6    ,     __lowerCamelCase				:    str=4    ,     __lowerCamelCase				:    Union[str, Any]="gelu"    ,     __lowerCamelCase				:    Optional[int]=1e-6    ,     **__lowerCamelCase				:    str    ,     )					->       List[Any]:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  hidden_size
										__magic_name__      =  image_size
										__magic_name__      =  patch_size
										__magic_name__      =  image_size // patch_size
										__magic_name__      =  mask_input_channels
										__magic_name__      =  num_point_embeddings
										__magic_name__      =  hidden_act
										__magic_name__      =  layer_norm_eps





class     A_				(   snake_case_	):







				def __init__(				self				:    List[str]    ,     __lowerCamelCase				:    Optional[Any]=2_5_6    ,     __lowerCamelCase				:    int="relu"    ,     __lowerCamelCase				:    List[str]=2_0_4_8    ,     __lowerCamelCase				:    Optional[Any]=2    ,     __lowerCamelCase				:    Optional[Any]=8    ,     __lowerCamelCase				:    Optional[Any]=2    ,     __lowerCamelCase				:    Dict=3    ,     __lowerCamelCase				:    Optional[int]=3    ,     __lowerCamelCase				:    Tuple=2_5_6    ,     __lowerCamelCase				:    List[str]=1e-6    ,     **__lowerCamelCase				:    Any    ,     )					->       str:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  hidden_size
										__magic_name__      =  hidden_act
										__magic_name__      =  mlp_dim
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  attention_downsample_rate
										__magic_name__      =  num_multimask_outputs
										__magic_name__      =  iou_head_depth
										__magic_name__      =  iou_head_hidden_dim
										__magic_name__      =  layer_norm_eps





class     A_				(   snake_case_	):







				def __init__(				self				:    Union[str, Any]    ,     __lowerCamelCase				:    str=7_6_8    ,     __lowerCamelCase				:    Optional[int]=2_5_6    ,     __lowerCamelCase				:    Tuple=1_2    ,     __lowerCamelCase				:    int=1_2    ,     __lowerCamelCase				:    int=3    ,     __lowerCamelCase				:    int=1_0_2_4    ,     __lowerCamelCase				:    Any=1_6    ,     __lowerCamelCase				:    Tuple="gelu"    ,     __lowerCamelCase				:    Dict=1e-06    ,     __lowerCamelCase				:    List[Any]=0.0    ,     __lowerCamelCase				:    Optional[Any]=1e-10    ,     __lowerCamelCase				:    str=True    ,     __lowerCamelCase				:    List[str]=4.0    ,     __lowerCamelCase				:    Union[str, Any]=True    ,     __lowerCamelCase				:    str=True    ,     __lowerCamelCase				:    Optional[Any]=1_4    ,     __lowerCamelCase				:    str=[2, 5, 8, 1_1]    ,     __lowerCamelCase				:    List[Any]=1_2_8    ,     __lowerCamelCase				:    Dict=None    ,     **__lowerCamelCase				:    List[str]    ,     )					->       Optional[int]:
										super().__init__(**__lowerCamelCase						)

										__magic_name__      =  hidden_size
										__magic_name__      =  output_channels
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  num_channels
										__magic_name__      =  image_size
										__magic_name__      =  patch_size
										__magic_name__      =  hidden_act
										__magic_name__      =  layer_norm_eps
										__magic_name__      =  attention_dropout
										__magic_name__      =  initializer_range
										__magic_name__      =  qkv_bias
										__magic_name__      =  mlp_ratio
										__magic_name__      =  use_abs_pos
										__magic_name__      =  use_rel_pos
										__magic_name__      =  window_size
										__magic_name__      =  global_attn_indexes
										__magic_name__      =  num_pos_feats
										__magic_name__      =  int(hidden_size * mlp_ratio						) if mlp_dim is None else mlp_dim





class     A_				(   snake_case_	):
				UpperCAmelCase__										=					'''sam'''
				UpperCAmelCase__										=					True







				def __init__(				self				:    Optional[Any]    ,     __lowerCamelCase				:    Dict=None    ,     __lowerCamelCase				:    Dict=None    ,     __lowerCamelCase				:    Optional[Any]=None    ,     __lowerCamelCase				:    int=0.02    ,     **__lowerCamelCase				:    List[Any]    ,     )					->       List[str]:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  vision_config if vision_config is not None else {}
										__magic_name__      =  prompt_encoder_config if prompt_encoder_config is not None else {}
										__magic_name__      =  mask_decoder_config if mask_decoder_config is not None else {}

										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  vision_config.to_dict()
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  prompt_encoder_config.to_dict()
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  mask_decoder_config.to_dict()

										__magic_name__      =  SamVisionConfig(**__lowerCamelCase						)
										__magic_name__      =  SamPromptEncoderConfig(**__lowerCamelCase						)
										__magic_name__      =  SamMaskDecoderConfig(**__lowerCamelCase						)
										__magic_name__      =  initializer_range







				def      _snake_case       (				self				:    Optional[int]						)					->       str:
										__magic_name__      =  copy.deepcopy(self.__dict__						)
										__magic_name__      =  self.vision_config.to_dict()
										__magic_name__      =  self.prompt_encoder_config.to_dict()
										__magic_name__      =  self.mask_decoder_config.to_dict()
										__magic_name__      =  self.__class__.model_type
										return output



