





import copy
import os
from typing import Union

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


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

UpperCAmelCase_					:   Any 							=						{
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """git_vision_model"""







  def __init__(	self    ,						_lowercase=768    ,						_lowercase=3_072    ,						_lowercase=12    ,						_lowercase=12    ,						_lowercase=3    ,						_lowercase=224    ,						_lowercase=16    ,						_lowercase="quick_gelu"    ,						_lowercase=1e-5    ,						_lowercase=0.0    ,						_lowercase=0.02    ,						**_lowercase    ,						):
       super().__init__(**UpperCamelCase__						)

       lowerCAmelCase_					:       Union[str, Any]    =		hidden_size
       lowerCAmelCase_					:       Optional[Any]    =		intermediate_size
       lowerCAmelCase_					:       Union[str, Any]    =		num_hidden_layers
       lowerCAmelCase_					:       Optional[int]    =		num_attention_heads
       lowerCAmelCase_					:       List[str]    =		num_channels
       lowerCAmelCase_					:       Union[str, Any]    =		patch_size
       lowerCAmelCase_					:       Optional[int]    =		image_size
       lowerCAmelCase_					:       int    =		initializer_range
       lowerCAmelCase_					:       int    =		attention_dropout
       lowerCAmelCase_					:       int    =		layer_norm_eps
       lowerCAmelCase_					:       Optional[Any]    =		hidden_act







  @classmethod
  def 	UpperCAmelCase__		(	cls    ,						_lowercase    ,						**_lowercase						):
       cls._set_token_in_kwargs(UpperCamelCase__						)

       lowerCAmelCase_					:       Any    =		cls.get_config_dict(UpperCamelCase__    ,						**UpperCamelCase__						)

       # get the vision config dict if we are loading from GITConfig
       if config_dict.get("""model_type"""						) == "git":
            lowerCAmelCase_					:       Dict    =		config_dict['vision_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(UpperCamelCase__    ,						**UpperCamelCase__						)





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """git"""







  def __init__(	self    ,						_lowercase=None    ,						_lowercase=30_522    ,						_lowercase=768    ,						_lowercase=6    ,						_lowercase=12    ,						_lowercase=3_072    ,						_lowercase="gelu"    ,						_lowercase=0.1    ,						_lowercase=0.1    ,						_lowercase=1_024    ,						_lowercase=0.02    ,						_lowercase=1e-12    ,						_lowercase=0    ,						_lowercase="absolute"    ,						_lowercase=True    ,						_lowercase=False    ,						_lowercase=101    ,						_lowercase=102    ,						_lowercase=None    ,						**_lowercase    ,						):
       super().__init__(bos_token_id=UpperCamelCase__    ,						eos_token_id=UpperCamelCase__    ,						pad_token_id=UpperCamelCase__    ,						**UpperCamelCase__						)

       if vision_config is None:
            lowerCAmelCase_					:       Tuple    =		{}
            logger.info("""vision_config is None. initializing the GitVisionConfig with default values."""						)

       lowerCAmelCase_					:       List[Any]    =		GitVisionConfig(**UpperCamelCase__						)
       lowerCAmelCase_					:       str    =		vocab_size
       lowerCAmelCase_					:       Tuple    =		hidden_size
       lowerCAmelCase_					:       List[Any]    =		num_hidden_layers
       lowerCAmelCase_					:       Optional[int]    =		num_attention_heads
       lowerCAmelCase_					:       str    =		hidden_act
       lowerCAmelCase_					:       Union[str, Any]    =		intermediate_size
       lowerCAmelCase_					:       List[str]    =		hidden_dropout_prob
       lowerCAmelCase_					:       Optional[Any]    =		attention_probs_dropout_prob
       lowerCAmelCase_					:       Tuple    =		max_position_embeddings
       lowerCAmelCase_					:       Tuple    =		initializer_range
       lowerCAmelCase_					:       Any    =		layer_norm_eps
       lowerCAmelCase_					:       Optional[int]    =		position_embedding_type
       lowerCAmelCase_					:       str    =		use_cache
       lowerCAmelCase_					:       Tuple    =		tie_word_embeddings
       lowerCAmelCase_					:       str    =		num_image_with_embedding

       lowerCAmelCase_					:       Optional[Any]    =		bos_token_id
       lowerCAmelCase_					:       Tuple    =		eos_token_id







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[int]    =		copy.deepcopy(self.__dict__						)
       lowerCAmelCase_					:       Dict    =		self.vision_config.to_dict()
       lowerCAmelCase_					:       Optional[Any]    =		self.__class__.model_type
       return output







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

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

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCAmelCase_					:   str 							=						logging.get_logger(__name__)





@dataclass
class      lowercase__    :
  __UpperCamelCase	     =  field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()  )}  )
  __UpperCamelCase	     =  field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}  )
  __UpperCamelCase	     =  field(
      default=128     ,					metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }     ,					)
  __UpperCamelCase	     =  field(
      default=__A     ,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[str]    =		self.task_name.lower()





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """train"""
  __UpperCamelCase	     =  """dev"""
  __UpperCamelCase	     =  """test"""





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None    ,						_lowercase = Split.train    ,						_lowercase = None    ,						):
       warnings.warn(
           """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
           """library. You can have a look at this example script for pointers: """
           """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""    ,						_lowercase    ,						)
       lowerCAmelCase_					:       Any    =		args
       lowerCAmelCase_					:       List[str]    =		glue_processors[args.task_name]()
       lowerCAmelCase_					:       Tuple    =		glue_output_modes[args.task_name]
       if isinstance(_lowercase    ,						_lowercase						):
            try:
                 lowerCAmelCase_					:       Dict    =		Split[mode]
            except KeyError:
                 raise KeyError("""mode is not a valid split name"""						)
        # Load data features from cache or dataset file
       lowerCAmelCase_					:       Optional[int]    =		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}_{args.task_name}'    ,						)
       lowerCAmelCase_					:       List[str]    =		self.processor.get_labels()
       if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
           "RobertaTokenizer",
           "RobertaTokenizerFast",
           "XLMRobertaTokenizer",
           "BartTokenizer",
           "BartTokenizerFast",
       ):
            # HACK(label indices are swapped in RoBERTa pretrained model)
            lowerCAmelCase_   ,  lowerCAmelCase_					:       List[str]    =		label_list[2], label_list[1]
       lowerCAmelCase_					:       int    =		label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       lowerCAmelCase_					:       Optional[int]    =		cached_features_file + """.lock"""
       with FileLock(_lowercase						):
            if os.path.exists(_lowercase						) and not args.overwrite_cache:
                 lowerCAmelCase_					:       Dict    =		time.time()
                 lowerCAmelCase_					:       str    =		torch.load(_lowercase						)
                 logger.info(
                     F'Loading features from cached file {cached_features_file} [took %.3f s]'    ,						time.time() - start						)
            else:
                 logger.info(F'Creating features from dataset file at {args.data_dir}'						)

                 if mode == Split.dev:
                      lowerCAmelCase_					:       List[Any]    =		self.processor.get_dev_examples(args.data_dir						)
                 elif mode == Split.test:
                      lowerCAmelCase_					:       Dict    =		self.processor.get_test_examples(args.data_dir						)
                 else:
                      lowerCAmelCase_					:       List[str]    =		self.processor.get_train_examples(args.data_dir						)
                 if limit_length is not None:
                      lowerCAmelCase_					:       Optional[int]    =		examples[:limit_length]
                 lowerCAmelCase_					:       Any    =		glue_convert_examples_to_features(
                     _lowercase    ,						_lowercase    ,						max_length=args.max_seq_length    ,						label_list=_lowercase    ,						output_mode=self.output_mode    ,						)
                 lowerCAmelCase_					:       str    =		time.time()
                 torch.save(self.features    ,						_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						):
       return len(self.features						)







  def __getitem__(	self    ,						_lowercase						):
       return self.features[i]







  def 	UpperCAmelCase__		(	self						):
       return self.label_list
