


'''simple docstring'''






import copy
import os
from typing import Union

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


__UpperCAmelCase       							=				logging.get_logger(__name__)

__UpperCAmelCase       							=				{
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  int      		= "bridgetower_vision_model"







							def __init__(						self   ,       lowerCamelCase_=7_68   ,       lowerCamelCase_=12   ,       lowerCamelCase_=3   ,       lowerCamelCase_=16   ,       lowerCamelCase_=2_88   ,       lowerCamelCase_=1   ,       lowerCamelCase_=1e-05   ,       lowerCamelCase_=False   ,       lowerCamelCase_=True   ,       lowerCamelCase_=False   ,       **lowerCamelCase_   ,       )			->				List[Any]:
														super().__init__(**snake_case__  )
														lowerCAmelCase__			  =      hidden_size
														lowerCAmelCase__			  =      num_hidden_layers
														lowerCAmelCase__			  =      num_channels
														lowerCAmelCase__			  =      patch_size
														lowerCAmelCase__			  =      image_size
														lowerCAmelCase__			  =      initializer_factor
														lowerCAmelCase__			  =      layer_norm_eps
														lowerCAmelCase__			  =      stop_gradient
														lowerCAmelCase__			  =      share_layernorm
														lowerCAmelCase__			  =      remove_last_layer







							@classmethod
							def     __SCREAMING_SNAKE_CASE				(						cls   ,       lowerCamelCase_   ,       **lowerCamelCase_  )			->				Optional[Any]:
														lowerCAmelCase__			  =      cls.get_config_dict(snake_case__   ,       **snake_case__  )

														if config_dict.get('''model_type'''  ) == "bridgetower":
																					lowerCAmelCase__			  =      config_dict["text_config"]

														if "model_type" in config_dict and hasattr(cls   ,       '''model_type'''  ) and config_dict["model_type"] != cls.model_type:
																					logger.warning(
																					    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																					    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""  )

														return cls.from_dict(snake_case__   ,       **snake_case__  )




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  Union[str, Any]      		= "bridgetower_text_model"







							def __init__(						self   ,       lowerCamelCase_=5_02_65   ,       lowerCamelCase_=7_68   ,       lowerCamelCase_=12   ,       lowerCamelCase_=12   ,       lowerCamelCase_=1   ,       lowerCamelCase_=30_72   ,       lowerCamelCase_="gelu"   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=5_14   ,       lowerCamelCase_=1   ,       lowerCamelCase_=1e-05   ,       lowerCamelCase_=1   ,       lowerCamelCase_=0   ,       lowerCamelCase_=2   ,       lowerCamelCase_="absolute"   ,       lowerCamelCase_=True   ,       **lowerCamelCase_   ,       )			->				Dict:
														super().__init__(**snake_case__  )

														lowerCAmelCase__			  =      vocab_size
														lowerCAmelCase__			  =      hidden_size
														lowerCAmelCase__			  =      num_hidden_layers
														lowerCAmelCase__			  =      num_attention_heads
														lowerCAmelCase__			  =      hidden_act
														lowerCAmelCase__			  =      initializer_factor
														lowerCAmelCase__			  =      intermediate_size
														lowerCAmelCase__			  =      hidden_dropout_prob
														lowerCAmelCase__			  =      attention_probs_dropout_prob
														lowerCAmelCase__			  =      max_position_embeddings
														lowerCAmelCase__			  =      type_vocab_size
														lowerCAmelCase__			  =      layer_norm_eps
														lowerCAmelCase__			  =      position_embedding_type
														lowerCAmelCase__			  =      use_cache
														lowerCAmelCase__			  =      pad_token_id
														lowerCAmelCase__			  =      bos_token_id
														lowerCAmelCase__			  =      eos_token_id







							@classmethod
							def     __SCREAMING_SNAKE_CASE				(						cls   ,       lowerCamelCase_   ,       **lowerCamelCase_  )			->				int:
														lowerCAmelCase__			  =      cls.get_config_dict(snake_case__   ,       **snake_case__  )

														if config_dict.get('''model_type'''  ) == "bridgetower":
																					lowerCAmelCase__			  =      config_dict["text_config"]

														if "model_type" in config_dict and hasattr(cls   ,       '''model_type'''  ) and config_dict["model_type"] != cls.model_type:
																					logger.warning(
																					    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																					    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""  )

														return cls.from_dict(snake_case__   ,       **snake_case__  )




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  List[str]      		= "bridgetower"







							def __init__(						self   ,       lowerCamelCase_=True   ,       lowerCamelCase_="gelu"   ,       lowerCamelCase_=7_68   ,       lowerCamelCase_=1   ,       lowerCamelCase_=1e-05   ,       lowerCamelCase_=False   ,       lowerCamelCase_="add"   ,       lowerCamelCase_=12   ,       lowerCamelCase_=6   ,       lowerCamelCase_=False   ,       lowerCamelCase_=False   ,       lowerCamelCase_=None   ,       lowerCamelCase_=None   ,       **lowerCamelCase_   ,       )			->				int:
														lowerCAmelCase__			  =      kwargs.pop('''text_config_dict'''   ,       snake_case__  )
														lowerCAmelCase__			  =      kwargs.pop('''vision_config_dict'''   ,       snake_case__  )

														super().__init__(**snake_case__  )
														lowerCAmelCase__			  =      share_cross_modal_transformer_layers
														lowerCAmelCase__			  =      hidden_act
														lowerCAmelCase__			  =      hidden_size
														lowerCAmelCase__			  =      initializer_factor
														lowerCAmelCase__			  =      layer_norm_eps
														lowerCAmelCase__			  =      share_link_tower_layers
														lowerCAmelCase__			  =      link_tower_type
														lowerCAmelCase__			  =      num_attention_heads
														lowerCAmelCase__			  =      num_hidden_layers
														lowerCAmelCase__			  =      tie_word_embeddings
														lowerCAmelCase__			  =      init_layernorm_from_vision_encoder

														if text_config is None:
																					lowerCAmelCase__			  =      {}
																					logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'''  )

														if vision_config is None:
																					lowerCAmelCase__			  =      {}
																					logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'''  )

														lowerCAmelCase__			  =      BridgeTowerTextConfig(**snake_case__  )
														lowerCAmelCase__			  =      BridgeTowerVisionConfig(**snake_case__  )







							@classmethod
							def     __SCREAMING_SNAKE_CASE				(						cls   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       **lowerCamelCase_  )			->				Optional[int]:
														return cls(text_config=text_config.to_dict()   ,       vision_config=vision_config.to_dict()   ,       **snake_case__  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      copy.deepcopy(self.__dict__  )
														lowerCAmelCase__			  =      self.text_config.to_dict()
														lowerCAmelCase__			  =      self.vision_config.to_dict()
														lowerCAmelCase__			  =      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


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							XLMRobertaModel.from_pretrained("xlm-roberta-base"							)
        UpperCAmelCase__						:    str							=							torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
        # The dog is cute and lives in the garden house

        UpperCAmelCase__						:    Tuple							=							torch.Size((1, 1_2, 7_6_8)							)  # batch_size, sequence_length, embedding_vector_dim
        UpperCAmelCase__						:    Optional[int]							=							torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]							)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
             UpperCAmelCase__						:    int							=							model(snake_case__							)["last_hidden_state"].detach()
        self.assertEqual(output.shape							,			snake_case__							)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]							,			snake_case__							,			atol=1e-3							)							)




   @slow
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							XLMRobertaModel.from_pretrained("xlm-roberta-large"							)
        UpperCAmelCase__						:    Tuple							=							torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
        # The dog is cute and lives in the garden house

        UpperCAmelCase__						:    Union[str, Any]							=							torch.Size((1, 1_2, 1_0_2_4)							)  # batch_size, sequence_length, embedding_vector_dim
        UpperCAmelCase__						:    Optional[Any]							=							torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]							)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
             UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							)["last_hidden_state"].detach()
        self.assertEqual(output.shape							,			snake_case__							)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]							,			snake_case__							,			atol=1e-3							)							)


