




'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


UpperCamelCase_						:			List[Any]  	=						logging.get_logger(__name__)

class    _a				(						_UpperCamelCase							):
     SCREAMING_SNAKE_CASE_							:    Tuple    				=						"""vision-text-dual-encoder"""
     SCREAMING_SNAKE_CASE_							:    List[str]    				=						True






     def __init__( self					,_SCREAMING_SNAKE_CASE=512					,_SCREAMING_SNAKE_CASE=2.6_5_9_2					,**_SCREAMING_SNAKE_CASE    )							->			Tuple:
          super().__init__(**a__    )

          if "vision_config" not in kwargs:
               raise ValueError("`vision_config` can not be `None`."    )

          if "text_config" not in kwargs:
               raise ValueError("`text_config` can not be `None`."    )

          _snake_case							=      kwargs.pop("vision_config"    )
          _snake_case							=      kwargs.pop("text_config"    )

          _snake_case							=      vision_config.pop("model_type"    )
          _snake_case							=      text_config.pop("model_type"    )

          if vision_model_type == "clip":
               _snake_case							=      AutoConfig.for_model(a__					,**a__    ).vision_config
          elif vision_model_type == "clip_vision_model":
               _snake_case							=      CLIPVisionConfig(**a__    )
          else:
               _snake_case							=      AutoConfig.for_model(a__					,**a__    )

          _snake_case							=      AutoConfig.for_model(a__					,**a__    )

          _snake_case							=      projection_dim
          _snake_case							=      logit_scale_init_value






     @classmethod
     def 				_lowercase	( cls					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,**_SCREAMING_SNAKE_CASE    )							->			int:
          return cls(vision_config=vision_config.to_dict()					,text_config=text_config.to_dict()					,**a__    )






     def 				_lowercase	( self    )							->			str:
          _snake_case							=      copy.deepcopy(self.__dict__    )
          _snake_case							=      self.vision_config.to_dict()
          _snake_case							=      self.text_config.to_dict()
          _snake_case							=      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 snake_case					(     unittest.TestCase):







		@slow
		def        a_       (    self					:				int						)				->  Tuple:





						'''simple docstring'''







						_A         =	CamembertModel.from_pretrained("camembert-base"						)
						model.to(a__						)

						_A         =	torch.tensor(
						    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]			,      device=a__			,      dtype=torch.long			,      )  # J'aime le camembert !
						with torch.no_grad():
										_A         =	model(a__						)["last_hidden_state"]
						_A         =	torch.Size((1, 10, 7_68)						)
						self.assertEqual(output.shape			,      a__						)
						# compare the actual values for a slice.
						_A         =	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=a__			,      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]			,      a__			,      atol=1E-4						)						)