


import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


a_   :      List[str]				    =      logging.getLogger(__name__)




@dataclass
class 	a       :
 _lowerCAmelCase	   =		4_2
 _lowerCAmelCase	   =		4_2
 _lowerCAmelCase	   =		4_2





@dataclass
class 	a       :
 _lowerCAmelCase	   =		4_2
 _lowerCAmelCase	   =		4_2
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		None





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""train"""
 _lowerCAmelCase	   =		"""dev"""
 _lowerCAmelCase	   =		"""test"""





class 	a       :
 @staticmethod
 def 							__UpperCAmelCase						(	__magic_name__ ,			__magic_name__  )   ->							List[InputExample]:
      raise NotImplementedError
 @staticmethod
 def 							__UpperCAmelCase						(	__magic_name__  )   ->							List[str]:
      raise NotImplementedError




 @staticmethod
 def 							__UpperCAmelCase						(	__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__=False ,			__magic_name__="[CLS]" ,			__magic_name__=1 ,			__magic_name__="[SEP]" ,			__magic_name__=False ,			__magic_name__=False ,			__magic_name__=0 ,			__magic_name__=0 ,			__magic_name__=-1_00 ,			__magic_name__=0 ,			__magic_name__=True ,			)   ->							List[InputFeatures]:
      _a      =							{label: i for i, label in enumerate(__magic_name__  )}

      _a      =							[]
      for ex_index, example in enumerate(__magic_name__  ):
           if ex_index % 1_00_00 == 0:
                logger.info('Writing example %d of %d' ,			__magic_name__ ,			len(__magic_name__  )  )

           _a      =							[]
           _a      =							[]
           for word, label in zip(example.words ,			example.labels  ):
                _a      =							tokenizer.tokenize(__magic_name__  )

                # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                if len(__magic_name__  ) > 0:
                     tokens.extend(__magic_name__  )
                     # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                     label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__magic_name__  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
           _a      =							tokenizer.num_special_tokens_to_add()
           if len(__magic_name__  ) > max_seq_length - special_tokens_count:
                _a      =							tokens[: (max_seq_length - special_tokens_count)]
                _a      =							label_ids[: (max_seq_length - special_tokens_count)]

           # The convention in BERT is:
           # (a) For sequence pairs:
           #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
           #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
           # (b) For single sequences:
           #  tokens:   [CLS] the dog is hairy . [SEP]
           #  type_ids:   0   0   0   0  0     0   0
           #
           # Where "type_ids" are used to indicate whether this is the first
           # sequence or the second sequence. The embedding vectors for `type=0` and
           # `type=1` were learned during pre-training and are added to the wordpiece
           # embedding vector (and position vector). This is not *strictly* necessary
           # since the [SEP] token unambiguously separates the sequences, but it makes
           # it easier for the model to learn the concept of sequences.
           #
           # For classification tasks, the first vector (corresponding to [CLS]) is
           # used as the "sentence vector". Note that this only makes sense because
           # the entire model is fine-tuned.
           tokens += [sep_token]
           label_ids += [pad_token_label_id]
           if sep_token_extra:
                # roberta uses an extra separator b/w pairs of sentences
                tokens += [sep_token]
                label_ids += [pad_token_label_id]
           _a      =							[sequence_a_segment_id] * len(__magic_name__  )

           if cls_token_at_end:
                tokens += [cls_token]
                label_ids += [pad_token_label_id]
                segment_ids += [cls_token_segment_id]
           else:
                _a      =							[cls_token] + tokens
                _a      =							[pad_token_label_id] + label_ids
                _a      =							[cls_token_segment_id] + segment_ids

           _a      =							tokenizer.convert_tokens_to_ids(__magic_name__  )

           # The mask has 1 for real tokens and 0 for padding tokens. Only real
           # tokens are attended to.
           _a      =							[1 if mask_padding_with_zero else 0] * len(__magic_name__  )

           # Zero-pad up to the sequence length.
           _a      =							max_seq_length - len(__magic_name__  )
           if pad_on_left:
                _a      =							([pad_token] * padding_length) + input_ids
                _a      =							([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                _a      =							([pad_token_segment_id] * padding_length) + segment_ids
                _a      =							([pad_token_label_id] * padding_length) + label_ids
           else:
                input_ids += [pad_token] * padding_length
                input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                segment_ids += [pad_token_segment_id] * padding_length
                label_ids += [pad_token_label_id] * padding_length

           assert len(__magic_name__  ) == max_seq_length
           assert len(__magic_name__  ) == max_seq_length
           assert len(__magic_name__  ) == max_seq_length
           assert len(__magic_name__  ) == max_seq_length

           if ex_index < 5:
                logger.info('*** Example ***'  )
                logger.info('guid: %s' ,			example.guid  )
                logger.info('tokens: %s' ,			' '.join([str(__magic_name__  ) for x in tokens]  )  )
                logger.info('input_ids: %s' ,			' '.join([str(__magic_name__  ) for x in input_ids]  )  )
                logger.info('input_mask: %s' ,			' '.join([str(__magic_name__  ) for x in input_mask]  )  )
                logger.info('segment_ids: %s' ,			' '.join([str(__magic_name__  ) for x in segment_ids]  )  )
                logger.info('label_ids: %s' ,			' '.join([str(__magic_name__  ) for x in label_ids]  )  )

           if "token_type_ids" not in tokenizer.model_input_names:
                _a      =							None

           features.append(
               InputFeatures(
                   input_ids=__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			label_ids=__magic_name__  )  )
      return features


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset




 class 	a       (       _SCREAMING_SNAKE_CASE    ):
  _lowerCAmelCase	   =		4_2
  _lowerCAmelCase	   =		nn.CrossEntropyLoss().ignore_index
  def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__=False ,			__magic_name__ = Split.train ,			)   ->							Any:
       # Load data features from cache or dataset file
       _a      =							os.path.join(
           __magic_name__ ,			'cached_{}_{}_{}'.format(mode.value ,			tokenizer.__class__.__name__ ,			str(__magic_name__  )  ) ,			)

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       _a      =							cached_features_file + '.lock'
       with FileLock(__magic_name__  ):
            if os.path.exists(__magic_name__  ) and not overwrite_cache:
                 logger.info(f'Loading features from cached file {cached_features_file}'  )
                 _a      =							torch.load(__magic_name__  )
            else:
                 logger.info(f'Creating features from dataset file at {data_dir}'  )
                 _a      =							token_classification_task.read_examples_from_file(__magic_name__ ,			__magic_name__  )
                 # TODO clean up all this to leverage built-in features of tokenizers
                 _a      =							token_classification_task.convert_examples_to_features(
                     __magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			cls_token_at_end=bool(model_type in ['xlnet']  ) ,			cls_token=tokenizer.cls_token ,			cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,			sep_token=tokenizer.sep_token ,			sep_token_extra=__magic_name__ ,			pad_on_left=bool(tokenizer.padding_side == 'left'  ) ,			pad_token=tokenizer.pad_token_id ,			pad_token_segment_id=tokenizer.pad_token_type_id ,			pad_token_label_id=self.pad_token_label_id ,			)
                 logger.info(f'Saving features into cached file {cached_features_file}'  )
                 torch.save(self.features ,			__magic_name__  )
  def __len__(	self  )   ->							List[str]:
       return len(self.features  )




  def __getitem__(	self ,			__magic_name__  )   ->							InputFeatures:
       return self.features[i]


if is_tf_available():
 import tensorflow as tf




 class 	a       :
  _lowerCAmelCase	   =		4_2
  _lowerCAmelCase	   =		-1_0_0
  def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__=False ,			__magic_name__ = Split.train ,			)   ->							Any:
       _a      =							token_classification_task.read_examples_from_file(__magic_name__ ,			__magic_name__  )
       # TODO clean up all this to leverage built-in features of tokenizers
       _a      =							token_classification_task.convert_examples_to_features(
           __magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			cls_token_at_end=bool(model_type in ['xlnet']  ) ,			cls_token=tokenizer.cls_token ,			cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,			sep_token=tokenizer.sep_token ,			sep_token_extra=__magic_name__ ,			pad_on_left=bool(tokenizer.padding_side == 'left'  ) ,			pad_token=tokenizer.pad_token_id ,			pad_token_segment_id=tokenizer.pad_token_type_id ,			pad_token_label_id=self.pad_token_label_id ,			)

       def gen():
            for ex in self.features:
                 if ex.token_type_ids is None:
                      yield (
                          {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                          ex.label_ids,
                      )
                 else:
                      yield (
                          {
                              "input_ids": ex.input_ids,
                              "attention_mask": ex.attention_mask,
                              "token_type_ids": ex.token_type_ids,
                          },
                          ex.label_ids,
                      )

       if "token_type_ids" not in tokenizer.model_input_names:
            _a      =							tf.data.Dataset.from_generator(
                __magic_name__ ,			({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa) ,			(
                    {'input_ids': tf.TensorShape([None]  ), 'attention_mask': tf.TensorShape([None]  )},
                    tf.TensorShape([None]  ),
                ) ,			)
       else:
            _a      =							tf.data.Dataset.from_generator(
                __magic_name__ ,			({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa) ,			(
                    {
                        'input_ids': tf.TensorShape([None]  ),
                        'attention_mask': tf.TensorShape([None]  ),
                        'token_type_ids': tf.TensorShape([None]  ),
                    },
                    tf.TensorShape([None]  ),
                ) ,			)
  def 							__UpperCAmelCase						(	self  )   ->							str:
       _a      =							self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

       return self.dataset
  def __len__(	self  )   ->							str:
       return len(self.features  )




  def __getitem__(	self ,			__magic_name__  )   ->							InputFeatures:
       return self.features[i]





'''simple docstring'''






import copy
import os
from typing import Union

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


a_   :      List[str]				    =      logging.get_logger(__name__)

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




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""git_vision_model"""
 def __init__(	self ,			__magic_name__=7_68 ,			__magic_name__=30_72 ,			__magic_name__=12 ,			__magic_name__=12 ,			__magic_name__=3 ,			__magic_name__=2_24 ,			__magic_name__=16 ,			__magic_name__="quick_gelu" ,			__magic_name__=1e-5 ,			__magic_name__=0.0 ,			__magic_name__=0.0_2 ,			**__magic_name__ ,			)   ->							Union[str, Any]:
      super().__init__(**__magic_name__  )

      _a      =							hidden_size
      _a      =							intermediate_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							num_channels
      _a      =							patch_size
      _a      =							image_size
      _a      =							initializer_range
      _a      =							attention_dropout
      _a      =							layer_norm_eps
      _a      =							hidden_act




 @classmethod
 def 							__UpperCAmelCase						(	cls ,			__magic_name__ ,			**__magic_name__  )   ->							"PretrainedConfig":
      cls._set_token_in_kwargs(__magic_name__  )

      _a ,				_a      =							cls.get_config_dict(__magic_name__ ,			**__magic_name__  )

      # get the vision config dict if we are loading from GITConfig
      if config_dict.get('model_type'  ) == "git":
           _a      =							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(__magic_name__ ,			**__magic_name__  )





class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		"""git"""
 def __init__(	self ,			__magic_name__=None ,			__magic_name__=3_05_22 ,			__magic_name__=7_68 ,			__magic_name__=6 ,			__magic_name__=12 ,			__magic_name__=30_72 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=10_24 ,			__magic_name__=0.0_2 ,			__magic_name__=1e-12 ,			__magic_name__=0 ,			__magic_name__="absolute" ,			__magic_name__=True ,			__magic_name__=False ,			__magic_name__=1_01 ,			__magic_name__=1_02 ,			__magic_name__=None ,			**__magic_name__ ,			)   ->							Optional[int]:
      super().__init__(bos_token_id=__magic_name__ ,			eos_token_id=__magic_name__ ,			pad_token_id=__magic_name__ ,			**__magic_name__  )

      if vision_config is None:
           _a      =							{}
           logger.info('vision_config is None. initializing the GitVisionConfig with default values.'  )

      _a      =							GitVisionConfig(**__magic_name__  )
      _a      =							vocab_size
      _a      =							hidden_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							hidden_act
      _a      =							intermediate_size
      _a      =							hidden_dropout_prob
      _a      =							attention_probs_dropout_prob
      _a      =							max_position_embeddings
      _a      =							initializer_range
      _a      =							layer_norm_eps
      _a      =							position_embedding_type
      _a      =							use_cache
      _a      =							tie_word_embeddings
      _a      =							num_image_with_embedding

      _a      =							bos_token_id
      _a      =							eos_token_id




 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							copy.deepcopy(self.__dict__  )
      _a      =							self.vision_config.to_dict()
      _a      =							self.__class__.model_type
      return output

