






import copy
import os
from typing import Union

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


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """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 			snake_case__   (							lowercase_):

							'''simple docstring'''
							lowerCamelCase							:				List[str] 				= "bridgetower_vision_model"



							def __init__(							self			,						a__=7_68			,						a__=12			,						a__=3			,						a__=16			,						a__=2_88			,						a__=1			,						a__=1e-05			,						a__=False			,						a__=True			,						a__=False			,						**a__			,						) ->      Dict:






												'''simple docstring'''





												super().__init__(**a__    )
												__snake_case			:Union[str, Any]          =						hidden_size
												__snake_case			:Optional[int]          =						num_hidden_layers
												__snake_case			:Dict          =						num_channels
												__snake_case			:str          =						patch_size
												__snake_case			:Tuple          =						image_size
												__snake_case			:Optional[int]          =						initializer_factor
												__snake_case			:Optional[int]          =						layer_norm_eps
												__snake_case			:List[Any]          =						stop_gradient
												__snake_case			:Tuple          =						share_layernorm
												__snake_case			:str          =						remove_last_layer



							@classmethod
							def 					__lowercase						(							cls			,						a__			,						**a__    ) ->      "PretrainedConfig":






												'''simple docstring'''





												__snake_case			:str          =						cls.get_config_dict(a__			,						**a__    )

												if config_dict.get("""model_type"""    ) == "bridgetower":
																	__snake_case			:Optional[Any]          =						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(a__			,						**a__    )




class 			snake_case__   (							lowercase_):

							'''simple docstring'''
							lowerCamelCase							:				List[str] 				= "bridgetower_text_model"



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






												'''simple docstring'''





												super().__init__(**a__    )

												__snake_case			:Union[str, Any]          =						vocab_size
												__snake_case			:Any          =						hidden_size
												__snake_case			:Any          =						num_hidden_layers
												__snake_case			:Dict          =						num_attention_heads
												__snake_case			:Any          =						hidden_act
												__snake_case			:Union[str, Any]          =						initializer_factor
												__snake_case			:int          =						intermediate_size
												__snake_case			:List[Any]          =						hidden_dropout_prob
												__snake_case			:Optional[int]          =						attention_probs_dropout_prob
												__snake_case			:Union[str, Any]          =						max_position_embeddings
												__snake_case			:Dict          =						type_vocab_size
												__snake_case			:Optional[Any]          =						layer_norm_eps
												__snake_case			:Any          =						position_embedding_type
												__snake_case			:Any          =						use_cache
												__snake_case			:Optional[Any]          =						pad_token_id
												__snake_case			:Any          =						bos_token_id
												__snake_case			:Optional[Any]          =						eos_token_id



							@classmethod
							def 					__lowercase						(							cls			,						a__			,						**a__    ) ->      "PretrainedConfig":






												'''simple docstring'''





												__snake_case			:Tuple          =						cls.get_config_dict(a__			,						**a__    )

												if config_dict.get("""model_type"""    ) == "bridgetower":
																	__snake_case			:str          =						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(a__			,						**a__    )




class 			snake_case__   (							lowercase_):

							'''simple docstring'''
							lowerCamelCase							:				Any 				= "bridgetower"



							def __init__(							self			,						a__=True			,						a__="gelu"			,						a__=7_68			,						a__=1			,						a__=1e-05			,						a__=False			,						a__="add"			,						a__=12			,						a__=6			,						a__=False			,						a__=False			,						a__=None			,						a__=None			,						**a__			,						) ->      List[str]:






												'''simple docstring'''





												__snake_case			:Optional[int]          =						kwargs.pop("""text_config_dict"""			,						a__    )
												__snake_case			:int          =						kwargs.pop("""vision_config_dict"""			,						a__    )

												super().__init__(**a__    )
												__snake_case			:Union[str, Any]          =						share_cross_modal_transformer_layers
												__snake_case			:Dict          =						hidden_act
												__snake_case			:int          =						hidden_size
												__snake_case			:Tuple          =						initializer_factor
												__snake_case			:Any          =						layer_norm_eps
												__snake_case			:List[Any]          =						share_link_tower_layers
												__snake_case			:Optional[Any]          =						link_tower_type
												__snake_case			:Tuple          =						num_attention_heads
												__snake_case			:Optional[Any]          =						num_hidden_layers
												__snake_case			:List[str]          =						tie_word_embeddings
												__snake_case			:Optional[Any]          =						init_layernorm_from_vision_encoder

												if text_config is None:
																	__snake_case			:List[str]          =						{}
																	logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""    )

												if vision_config is None:
																	__snake_case			:List[Any]          =						{}
																	logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""    )

												__snake_case			:Any          =						BridgeTowerTextConfig(**a__    )
												__snake_case			:Any          =						BridgeTowerVisionConfig(**a__    )



							@classmethod
							def 					__lowercase						(							cls			,						a__			,						a__			,						**a__    ) ->      Optional[Any]:






												'''simple docstring'''





												return cls(text_config=text_config.to_dict()			,						vision_config=vision_config.to_dict()			,						**a__    )



							def 					__lowercase						(							self    ) ->      List[str]:






												'''simple docstring'''





												__snake_case			:str          =						copy.deepcopy(self.__dict__    )
												__snake_case			:Union[str, Any]          =						self.text_config.to_dict()
												__snake_case			:List[str]          =						self.vision_config.to_dict()
												__snake_case			:List[str]          =						self.__class__.model_type
												return output










import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__=2			,						a__=3			,						a__=64			,						a__=None    ) ->      int:






            '''simple docstring'''





            __snake_case			:Any          =						np.random.default_rng(a__    )
            __snake_case			:List[str]          =						length
            __snake_case			:Optional[Any]          =						rng.normal(size=(length,)    ).astype(np.floataa    )
            __snake_case			:Optional[int]          =						a * self.x + b + rng.normal(scale=0.1			,						size=(length,)    ).astype(np.floataa    )



       def __len__(							self    ) ->      str:






            '''simple docstring'''





            return self.length



       def __getitem__(							self			,						a__    ) ->      int:






            '''simple docstring'''





            return {"x": self.x[i], "y": self.y[i]}




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Any          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:int          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:Dict          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      Optional[Any]:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:Tuple          =						False
            return x * self.a[0] + self.b[0]




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Optional[int]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:List[str]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:str          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      str:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:List[Any]          =						False
            return x * self.a + self.b






def   UpperCamelCase  (       snake_case__     :   Optional[int]	,snake_case__     :   int = 16  ):




     '''simple docstring'''






     from datasets import load_dataset
     from transformers import AutoTokenizer

     __snake_case			:Union[str, Any]          =						AutoTokenizer.from_pretrained("""bert-base-cased"""  )
     __snake_case			:Dict          =						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
     __snake_case			:Any          =						load_dataset("""csv"""	,data_files=snake_case__  )
     __snake_case			:Dict          =						datasets["""train"""].unique("""label"""  )

     __snake_case			:List[Any]          =						{v: i for i, v in enumerate(snake_case__  )}

     def tokenize_function(snake_case__     :   List[Any]  ):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case			:Optional[Any]          =						tokenizer(
              examples["""sentence1"""]	,examples["""sentence2"""]	,truncation=snake_case__	,max_length=snake_case__	,padding="""max_length"""  )
          if "label" in examples:
               __snake_case			:Dict          =						[label_to_id[l] for l in examples["""label"""]]
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     __snake_case			:List[str]          =						datasets.map(
         snake_case__	,batched=snake_case__	,remove_columns=["""sentence1""", """sentence2""", """label"""]	,)

     def collate_fn(snake_case__     :   Optional[int]  ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(snake_case__	,padding="""max_length"""	,max_length=128	,return_tensors="""pt"""  )
          return tokenizer.pad(snake_case__	,padding="""longest"""	,return_tensors="""pt"""  )

     # Instantiate dataloaders.
     __snake_case			:List[Any]          =						DataLoader(tokenized_datasets["""train"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=2  )
     __snake_case			:str          =						DataLoader(tokenized_datasets["""validation"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=1  )

     return train_dataloader, eval_dataloader


