

'''simple docstring'''






import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
lowerCAmelCase__											=	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 			lowercase_	:




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str							=			field(
      default=lowerCamelCase__				,       metadata={'help': 'Model type selected in the list: ' + ', '.join(lowerCamelCase__      )}      )
  SCREAMING_SNAKE_CASE    :  str							=			field(
      default=lowerCamelCase__				,       metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}      )
  SCREAMING_SNAKE_CASE    :  int							=			field(
      default=1_2_8				,       metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }				,       )
  SCREAMING_SNAKE_CASE    :  int							=			field(
      default=1_2_8				,       metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}				,       )
  SCREAMING_SNAKE_CASE    :  int							=			field(
      default=6_4				,       metadata={
          'help': (
              'The maximum number of tokens for the question. Questions longer than this will '
              'be truncated to this length.'
          )
      }				,       )
  SCREAMING_SNAKE_CASE    :  int							=			field(
      default=3_0				,       metadata={
          'help': (
              'The maximum length of an answer that can be generated. This is needed because the start '
              'and end predictions are not conditioned on one another.'
          )
      }				,       )
  SCREAMING_SNAKE_CASE    :  bool							=			field(
      default=lowerCamelCase__				,       metadata={'help': 'Overwrite the cached training and evaluation sets'}      )
  SCREAMING_SNAKE_CASE    :  bool							=			field(
      default=lowerCamelCase__				,       metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}      )
  SCREAMING_SNAKE_CASE    :  float							=			field(
      default=0.0				,       metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}      )
  SCREAMING_SNAKE_CASE    :  int							=			field(
      default=2_0				,       metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}      )
  SCREAMING_SNAKE_CASE    :  int							=			field(
      default=0				,       metadata={
          'help': (
              'language id of input for language-specific xlm models (see'
              ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
          )
      }				,       )
  SCREAMING_SNAKE_CASE    :  int							=			field(default=1				,       metadata={'help': 'multiple threads for converting example to features'}      )






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			'train'
  SCREAMING_SNAKE_CASE    :  List[Any]							=			'dev'






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  SquadDataTrainingArguments
  SCREAMING_SNAKE_CASE    :  List[SquadFeatures]
  SCREAMING_SNAKE_CASE    :  Split
  SCREAMING_SNAKE_CASE    :  bool






  def __init__(      self   :     Optional[Any]					,lowercase__   :     SquadDataTrainingArguments					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     Optional[int] = None					,lowercase__   :     Union[str, Split] = Split.train					,lowercase__   :     Optional[bool] = False					,lowercase__   :     Optional[str] = None					,lowercase__   :     Optional[str] = "pt"					,):
   __lowercase							  = args
   __lowercase							  = is_language_sensitive
   __lowercase							  = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
   if isinstance(lowercase__					,lowercase__			):
    try:
     __lowercase							  = Split[mode]
    except KeyError:
     raise KeyError('''mode is not a valid split name'''			)
   __lowercase							  = mode
   # Load data features from cache or dataset file
   __lowercase							  = '''v2''' if args.version_2_with_negative else '''v1'''
   __lowercase							  = os.path.join(
       cache_dir if cache_dir is not None else args.data_dir					,F"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"					,)

   # Make sure only the first process in distributed training processes the dataset,
   # and the others will use the cache.
   __lowercase							  = cached_features_file + '''.lock'''
   with FileLock(lowercase__			):
    if os.path.exists(lowercase__			) and not args.overwrite_cache:
     __lowercase							  = time.time()
     __lowercase							  = torch.load(lowercase__			)

     # Legacy cache files have only features, while new cache files
     # will have dataset and examples also.
     __lowercase							  = self.old_features['''features''']
     __lowercase							  = self.old_features.get('''dataset'''					,lowercase__			)
     __lowercase							  = self.old_features.get('''examples'''					,lowercase__			)
     logger.info(
         F"Loading features from cached file {cached_features_file} [took %.3f s]"					,time.time() - start			)

     if self.dataset is None or self.examples is None:
      logger.warning(
          F"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
          ''' future run'''			)
    else:
     if mode == Split.dev:
      __lowercase							  = self.processor.get_dev_examples(args.data_dir			)
     else:
      __lowercase							  = self.processor.get_train_examples(args.data_dir			)

     __lowercase  ,	__lowercase							  = squad_convert_examples_to_features(
         examples=self.examples					,tokenizer=lowercase__					,max_seq_length=args.max_seq_length					,doc_stride=args.doc_stride					,max_query_length=args.max_query_length					,is_training=mode == Split.train					,threads=args.threads					,return_dataset=lowercase__					,)

     __lowercase							  = time.time()
     torch.save(
         {'''features''': self.features, '''dataset''': self.dataset, '''examples''': self.examples}					,lowercase__					,)
     # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
     logger.info(
         F"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"			)






  def __len__(      self   :     Tuple			):
   return len(self.features			)






  def __getitem__(      self   :     Tuple					,lowercase__   :     str			):
   # Convert to Tensors and build dataset
   __lowercase							  = self.features[i]

   __lowercase							  = torch.tensor(feature.input_ids					,dtype=torch.long			)
   __lowercase							  = torch.tensor(feature.attention_mask					,dtype=torch.long			)
   __lowercase							  = torch.tensor(feature.token_type_ids					,dtype=torch.long			)
   __lowercase							  = torch.tensor(feature.cls_index					,dtype=torch.long			)
   __lowercase							  = torch.tensor(feature.p_mask					,dtype=torch.float			)
   __lowercase							  = torch.tensor(feature.is_impossible					,dtype=torch.float			)

   __lowercase							  = {
       '''input_ids''': input_ids,
       '''attention_mask''': attention_mask,
       '''token_type_ids''': token_type_ids,
   }

   if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
    del inputs["token_type_ids"]

   if self.args.model_type in ["xlnet", "xlm"]:
    inputs.update({'''cls_index''': cls_index, '''p_mask''': p_mask}			)
    if self.args.version_2_with_negative:
     inputs.update({'''is_impossible''': is_impossible}			)
    if self.is_language_sensitive:
     inputs.update({'''langs''': (torch.ones(input_ids.shape					,dtype=torch.intaa			) * self.args.lang_id)}			)

   if self.mode == Split.train:
    __lowercase							  = torch.tensor(feature.start_position					,dtype=torch.long			)
    __lowercase							  = torch.tensor(feature.end_position					,dtype=torch.long			)
    inputs.update({'''start_positions''': start_positions, '''end_positions''': end_positions}			)

   return inputs








'''simple docstring'''

import copy
import os
from typing import Union

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """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_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower_vision_model'''


    def __init__(		self ,		A=768 ,		A=12 ,		A=3 ,		A=16 ,		A=288 ,		A=1 ,		A=1e-05 ,		A=False ,		A=True ,		A=False ,		**A ,		)		->   Dict:
        super().__init__(**A       )
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			patch_size
        _SCREAMING_SNAKE_CASE									=			image_size
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			stop_gradient
        _SCREAMING_SNAKE_CASE									=			share_layernorm
        _SCREAMING_SNAKE_CASE									=			remove_last_layer





    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   "PretrainedConfig":
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cls.get_config_dict(A ,		**A       )

        if config_dict.get("""model_type"""       ) == "bridgetower":
            _SCREAMING_SNAKE_CASE									=			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 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower_text_model'''


    def __init__(		self ,		A=5_0265 ,		A=768 ,		A=12 ,		A=12 ,		A=1 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=514 ,		A=1 ,		A=1e-05 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		**A ,		)		->   Union[str, Any]:
        super().__init__(**A       )

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





    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   "PretrainedConfig":
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cls.get_config_dict(A ,		**A       )

        if config_dict.get("""model_type"""       ) == "bridgetower":
            _SCREAMING_SNAKE_CASE									=			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 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower'''


    def __init__(		self ,		A=True ,		A="gelu" ,		A=768 ,		A=1 ,		A=1e-05 ,		A=False ,		A="add" ,		A=12 ,		A=6 ,		A=False ,		A=False ,		A=None ,		A=None ,		**A ,		)		->   Tuple:
        # TODO: remove this once the Hub files are updated.
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""text_config_dict""" ,		A       )
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""vision_config_dict""" ,		A       )

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

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

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

        _SCREAMING_SNAKE_CASE									=			BridgeTowerTextConfig(**A       )
        _SCREAMING_SNAKE_CASE									=			BridgeTowerVisionConfig(**A       )


    @classmethod
    def 			snake_case_(		cls ,		A ,		A ,		**A       )		->   int:
        return cls(text_config=text_config.to_dict() ,		vision_config=vision_config.to_dict() ,		**A       )





    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			copy.deepcopy(self.__dict__       )
        _SCREAMING_SNAKE_CASE									=			self.text_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.vision_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.__class__.model_type
        return output



