'''simple docstring'''
import copy
import os
from typing import Union

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


a=  logging.get_logger(__name__)

a=  {
    '''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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''bridgetower_vision_model'''







     def __init__(		self     ,			_lowerCamelCase=7_6_8     ,			_lowerCamelCase=1_2     ,			_lowerCamelCase=3     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=2_8_8     ,			_lowerCamelCase=1     ,			_lowerCamelCase=1E-0_5     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			**_lowerCamelCase     ,			):
         super().__init__(**_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  hidden_size
         __UpperCamelCase	:	Union[str, Any]		    =  num_hidden_layers
         __UpperCamelCase	:	Dict		    =  num_channels
         __UpperCamelCase	:	Tuple		    =  patch_size
         __UpperCamelCase	:	Tuple		    =  image_size
         __UpperCamelCase	:	int		    =  initializer_factor
         __UpperCamelCase	:	Optional[Any]		    =  layer_norm_eps
         __UpperCamelCase	:	List[Any]		    =  stop_gradient
         __UpperCamelCase	:	List[str]		    =  share_layernorm
         __UpperCamelCase	:	List[str]		    =  remove_last_layer







     @classmethod
     def lowerCAmelCase     (		cls     ,			_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase    ,					__UpperCamelCase	:	int		    =  cls.get_config_dict(_lowerCamelCase     ,			**_lowerCamelCase    )

         if config_dict.get('model_type'    ) == "bridgetower":
             __UpperCamelCase	:	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(_lowerCamelCase     ,			**_lowerCamelCase    )






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''bridgetower_text_model'''







     def __init__(		self     ,			_lowerCamelCase=5_0_2_6_5     ,			_lowerCamelCase=7_6_8     ,			_lowerCamelCase=1_2     ,			_lowerCamelCase=1_2     ,			_lowerCamelCase=1     ,			_lowerCamelCase=3_0_7_2     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5_1_4     ,			_lowerCamelCase=1     ,			_lowerCamelCase=1E-0_5     ,			_lowerCamelCase=1     ,			_lowerCamelCase=0     ,			_lowerCamelCase=2     ,			_lowerCamelCase="absolute"     ,			_lowerCamelCase=True     ,			**_lowerCamelCase     ,			):
         super().__init__(**_lowerCamelCase    )

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







     @classmethod
     def lowerCAmelCase     (		cls     ,			_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase    ,					__UpperCamelCase	:	Tuple		    =  cls.get_config_dict(_lowerCamelCase     ,			**_lowerCamelCase    )

         if config_dict.get('model_type'    ) == "bridgetower":
             __UpperCamelCase	:	Dict		    =  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(_lowerCamelCase     ,			**_lowerCamelCase    )






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''bridgetower'''







     def __init__(		self     ,			_lowerCamelCase=True     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=7_6_8     ,			_lowerCamelCase=1     ,			_lowerCamelCase=1E-0_5     ,			_lowerCamelCase=False     ,			_lowerCamelCase="add"     ,			_lowerCamelCase=1_2     ,			_lowerCamelCase=6     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			**_lowerCamelCase     ,			):
         # TODO: remove this once the Hub files are updated.
         __UpperCamelCase	:	Any		    =  kwargs.pop('text_config_dict'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  kwargs.pop('vision_config_dict'     ,			_lowerCamelCase    )

         super().__init__(**_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  share_cross_modal_transformer_layers
         __UpperCamelCase	:	Optional[Any]		    =  hidden_act
         __UpperCamelCase	:	Optional[int]		    =  hidden_size
         __UpperCamelCase	:	Dict		    =  initializer_factor
         __UpperCamelCase	:	int		    =  layer_norm_eps
         __UpperCamelCase	:	Any		    =  share_link_tower_layers
         __UpperCamelCase	:	int		    =  link_tower_type
         __UpperCamelCase	:	int		    =  num_attention_heads
         __UpperCamelCase	:	Dict		    =  num_hidden_layers
         __UpperCamelCase	:	Optional[int]		    =  tie_word_embeddings
         __UpperCamelCase	:	List[str]		    =  init_layernorm_from_vision_encoder

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

         if vision_config is None:
             __UpperCamelCase	:	Union[str, Any]		    =  {}
             logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'    )

         __UpperCamelCase	:	Dict		    =  BridgeTowerTextConfig(**_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  BridgeTowerVisionConfig(**_lowerCamelCase    )







     @classmethod
     def lowerCAmelCase     (		cls     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ):
         return cls(text_config=text_config.to_dict()     ,			vision_config=vision_config.to_dict()     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  copy.deepcopy(self.__dict__    )
         __UpperCamelCase	:	Optional[int]		    =  self.text_config.to_dict()
         __UpperCamelCase	:	List[Any]		    =  self.vision_config.to_dict()
         __UpperCamelCase	:	List[str]		    =  self.__class__.model_type
         return output




'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
  import datasets






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     @slow
     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,			'prajjwal1/bert-tiny'    )
         __UpperCamelCase	:	Optional[int]		    =  BertTokenizer.from_pretrained('bert-base-uncased'    )

         __UpperCamelCase	:	str		    =  bertabert.config.encoder.vocab_size
         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.sep_token_id
         __UpperCamelCase	:	Any		    =  tokenizer.cls_token_id
         __UpperCamelCase	:	Optional[Any]		    =  1_2_8

         __UpperCamelCase	:	str		    =  datasets.load_dataset('cnn_dailymail'     ,			'3.0.0'     ,			split='train[:1%]'    )
         __UpperCamelCase	:	Tuple		    =  datasets.load_dataset('cnn_dailymail'     ,			'3.0.0'     ,			split='validation[:1%]'    )

         __UpperCamelCase	:	Dict		    =  train_dataset.select(range(3_2    )    )
         __UpperCamelCase	:	List[Any]		    =  val_dataset.select(range(1_6    )    )

         __UpperCamelCase	:	Optional[int]		    =  4

         def _map_to_encoder_decoder_inputs(_lowerCamelCase    ):
             # Tokenizer will automatically set [BOS] <text> [EOS]
             __UpperCamelCase	:	Union[str, Any]		    =  tokenizer(batch['article']     ,			padding='max_length'     ,			truncation=_lowerCamelCase     ,			max_length=5_1_2    )
             __UpperCamelCase	:	Optional[int]		    =  tokenizer(batch['highlights']     ,			padding='max_length'     ,			truncation=_lowerCamelCase     ,			max_length=1_2_8    )
             __UpperCamelCase	:	Any		    =  inputs.input_ids
             __UpperCamelCase	:	Optional[Any]		    =  inputs.attention_mask

             __UpperCamelCase	:	Optional[Any]		    =  outputs.input_ids
             __UpperCamelCase	:	List[Any]		    =  outputs.input_ids.copy()
             __UpperCamelCase	:	Optional[Any]		    =  [
                 [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
             ]
             __UpperCamelCase	:	Union[str, Any]		    =  outputs.attention_mask

             assert all(len(_lowerCamelCase    ) == 5_1_2 for x in inputs.input_ids    )
             assert all(len(_lowerCamelCase    ) == 1_2_8 for x in outputs.input_ids    )

             return batch

         def _compute_metrics(_lowerCamelCase    ):
             __UpperCamelCase	:	str		    =  pred.label_ids
             __UpperCamelCase	:	Dict		    =  pred.predictions

             # all unnecessary tokens are removed
             __UpperCamelCase	:	Tuple		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )
             __UpperCamelCase	:	Optional[Any]		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )

             __UpperCamelCase	:	Dict		    =  sum([int(pred_str[i] == label_str[i]    ) for i in range(len(_lowerCamelCase    )    )]    ) / len(_lowerCamelCase    )

             return {"accuracy": accuracy}

         # map train dataset
         __UpperCamelCase	:	str		    =  train_dataset.map(
             _map_to_encoder_decoder_inputs     ,			batched=_lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			remove_columns=['article', 'highlights']     ,			)
         train_dataset.set_format(
             type='torch'     ,			columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,			)

         # same for validation dataset
         __UpperCamelCase	:	Any		    =  val_dataset.map(
             _map_to_encoder_decoder_inputs     ,			batched=_lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			remove_columns=['article', 'highlights']     ,			)
         val_dataset.set_format(
             type='torch'     ,			columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,			)

         __UpperCamelCase	:	Optional[Any]		    =  self.get_auto_remove_tmp_dir()

         __UpperCamelCase	:	List[Any]		    =  SeqaSeqTrainingArguments(
             output_dir=_lowerCamelCase     ,			per_device_train_batch_size=_lowerCamelCase     ,			per_device_eval_batch_size=_lowerCamelCase     ,			predict_with_generate=_lowerCamelCase     ,			evaluation_strategy='steps'     ,			do_train=_lowerCamelCase     ,			do_eval=_lowerCamelCase     ,			warmup_steps=0     ,			eval_steps=2     ,			logging_steps=2     ,			)

         # instantiate trainer
         __UpperCamelCase	:	str		    =  SeqaSeqTrainer(
             model=_lowerCamelCase     ,			args=_lowerCamelCase     ,			compute_metrics=_compute_metrics     ,			train_dataset=_lowerCamelCase     ,			eval_dataset=_lowerCamelCase     ,			tokenizer=_lowerCamelCase     ,			)

         # start training
         trainer.train()



