




import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_lowerCamelCase    				=		logging.get_logger(__name__)



@dataclass
class     a  :

     '''simple docstring'''





     lowerCAmelCase       : str
     lowerCAmelCase       : str
     lowerCAmelCase       : Optional[str]     	=						None
     lowerCAmelCase       : Optional[str]     	=						None
     def 						lowerCamelCase_     (			self     :    Dict       ):
         return json.dumps(dataclasses.asdict(self       )      ,					indent=2       ) + "\n"



@dataclass(frozen=_A    )
class     a  :

     '''simple docstring'''





     lowerCAmelCase       : List[int]
     lowerCAmelCase       : Optional[List[int]]     	=						None
     lowerCAmelCase       : Optional[List[int]]     	=						None
     lowerCAmelCase       : Optional[Union[int, float]]     	=						None
     def 						lowerCamelCase_     (			self     :    Any       ):
         return json.dumps(dataclasses.asdict(self       )       ) + "\n"



class     a  :

     '''simple docstring'''





     def 						lowerCamelCase_     (			self     :    Tuple      ,					__snake_case     :    List[str]       ):
         raise NotImplementedError()
     def 						lowerCamelCase_     (			self     :    Union[str, Any]      ,					__snake_case     :    Optional[Any]       ):
         raise NotImplementedError()
     def 						lowerCamelCase_     (			self     :    List[Any]      ,					__snake_case     :    Tuple       ):
         raise NotImplementedError()
     def 						lowerCamelCase_     (			self     :    Optional[Any]      ,					__snake_case     :    Any       ):
         raise NotImplementedError()
     def 						lowerCamelCase_     (			self     :    int       ):
         raise NotImplementedError()
     def 						lowerCamelCase_     (			self     :    Optional[int]      ,					__snake_case     :    Union[str, Any]       ):
         if len(self.get_labels()       ) > 1:
             UpperCAmelCase_											=  self.get_labels()[int(example.label       )]
         return example




     @classmethod
     def 						lowerCamelCase_     (			cls     :    Optional[int]      ,					__snake_case     :    Union[str, Any]      ,					__snake_case     :    Union[str, Any]=None       ):
         with open(__snake_case      ,					'''r'''      ,					encoding='''utf-8-sig'''       ) as f:
             return list(csv.reader(__snake_case      ,					delimiter='''\t'''      ,					quotechar=__snake_case       )       )



class     a  (     _A    ):

     '''simple docstring'''





     def __init__(			self     :    Optional[int]      ,					__snake_case     :    Union[str, Any]=None      ,					__snake_case     :    int=None      ,					__snake_case     :    str="classification"      ,					__snake_case     :    Optional[int]=False       ):
         UpperCAmelCase_											=  [] if labels is None else labels
         UpperCAmelCase_											=  [] if examples is None else examples
         UpperCAmelCase_											=  mode
         UpperCAmelCase_											=  verbose
     def __len__(			self     :    int       ):
         return len(self.examples       )
     def __getitem__(			self     :    Union[str, Any]      ,					__snake_case     :    List[Any]       ):
         if isinstance(__snake_case      ,					__snake_case       ):
             return SingleSentenceClassificationProcessor(labels=self.labels      ,					examples=self.examples[idx]       )
         return self.examples[idx]
     @classmethod
     def 						lowerCamelCase_     (			cls     :    List[str]      ,					__snake_case     :    int      ,					__snake_case     :    int=""      ,					__snake_case     :    int=0      ,					__snake_case     :    Dict=1      ,					__snake_case     :    Tuple=None      ,					__snake_case     :    Optional[Any]=False      ,					**__snake_case     :    Tuple       ):
         UpperCAmelCase_											=  cls(**__snake_case       )
         processor.add_examples_from_csv(
             __snake_case      ,					split_name=__snake_case      ,					column_label=__snake_case      ,					column_text=__snake_case      ,					column_id=__snake_case      ,					skip_first_row=__snake_case      ,					overwrite_labels=__snake_case      ,					overwrite_examples=__snake_case      ,					)
         return processor
     @classmethod
     def 						lowerCamelCase_     (			cls     :    Optional[Any]      ,					__snake_case     :    List[str]      ,					__snake_case     :    Tuple=None      ,					**__snake_case     :    Optional[int]       ):
         UpperCAmelCase_											=  cls(**__snake_case       )
         processor.add_examples(__snake_case      ,					labels=__snake_case       )
         return processor
     def 						lowerCamelCase_     (			self     :    Dict      ,					__snake_case     :    Union[str, Any]      ,					__snake_case     :    List[str]=""      ,					__snake_case     :    str=0      ,					__snake_case     :    Optional[Any]=1      ,					__snake_case     :    Any=None      ,					__snake_case     :    Union[str, Any]=False      ,					__snake_case     :    Optional[Any]=False      ,					__snake_case     :    Union[str, Any]=False      ,					):
         UpperCAmelCase_											=  self._read_tsv(__snake_case       )
         if skip_first_row:
             UpperCAmelCase_											=  lines[1:]
         UpperCAmelCase_											=  []
         UpperCAmelCase_											=  []
         UpperCAmelCase_											=  []
         for i, line in enumerate(__snake_case       ):
             texts.append(line[column_text]       )
             labels.append(line[column_label]       )
             if column_id is not None:
                 ids.append(line[column_id]       )
             else:
                 UpperCAmelCase_											=  F'{split_name}-{i}' if split_name else str(__snake_case       )
                 ids.append(__snake_case       )

         return self.add_examples(
             __snake_case      ,					__snake_case      ,					__snake_case      ,					overwrite_labels=__snake_case      ,					overwrite_examples=__snake_case       )
     def 						lowerCamelCase_     (			self     :    int      ,					__snake_case     :    Optional[Any]      ,					__snake_case     :    Optional[Any]=None      ,					__snake_case     :    Dict=None      ,					__snake_case     :    Any=False      ,					__snake_case     :    List[Any]=False       ):
         if labels is not None and len(__snake_case       ) != len(__snake_case       ):
             raise ValueError(
                 F'Text and labels have mismatched lengths {len(__snake_case       )} and {len(__snake_case       )}'       )
         if ids is not None and len(__snake_case       ) != len(__snake_case       ):
             raise ValueError(F'Text and ids have mismatched lengths {len(__snake_case       )} and {len(__snake_case       )}'       )
         if ids is None:
             UpperCAmelCase_											=  [None] * len(__snake_case       )
         if labels is None:
             UpperCAmelCase_											=  [None] * len(__snake_case       )
         UpperCAmelCase_											=  []
         UpperCAmelCase_											=  set()
         for text_or_text_and_label, label, guid in zip(__snake_case      ,					__snake_case      ,					__snake_case       ):
             if isinstance(__snake_case      ,					(tuple, list)       ) and label is None:
                 UpperCAmelCase_				,							UpperCAmelCase_											=  text_or_text_and_label
             else:
                 UpperCAmelCase_											=  text_or_text_and_label
             added_labels.add(__snake_case       )
             examples.append(InputExample(guid=__snake_case      ,					text_a=__snake_case      ,					text_b=__snake_case      ,					label=__snake_case       )       )

         # Update examples
         if overwrite_examples:
             UpperCAmelCase_											=  examples
         else:
             self.examples.extend(__snake_case       )

         # Update labels
         if overwrite_labels:
             UpperCAmelCase_											=  list(__snake_case       )
         else:
             UpperCAmelCase_											=  list(set(self.labels       ).union(__snake_case       )       )

         return self.examples




     def 						lowerCamelCase_     (			self     :    Tuple      ,					__snake_case     :    Tuple      ,					__snake_case     :    Tuple=None      ,					__snake_case     :    Dict=False      ,					__snake_case     :    Optional[int]=0      ,					__snake_case     :    Optional[Any]=True      ,					__snake_case     :    int=None      ,					):
         if max_length is None:
             UpperCAmelCase_											=  tokenizer.max_len

         UpperCAmelCase_											=  {label: i for i, label in enumerate(self.labels       )}

         UpperCAmelCase_											=  []
         for ex_index, example in enumerate(self.examples       ):
             if ex_index % 1_00_00 == 0:
                 logger.info(F'Tokenizing example {ex_index}'       )

             UpperCAmelCase_											=  tokenizer.encode(
                 example.text_a      ,					add_special_tokens=__snake_case      ,					max_length=min(__snake_case      ,					tokenizer.max_len       )      ,					)
             all_input_ids.append(__snake_case       )

         UpperCAmelCase_											=  max(len(__snake_case       ) for input_ids in all_input_ids       )

         UpperCAmelCase_											=  []
         for ex_index, (input_ids, example) in enumerate(zip(__snake_case      ,					self.examples       )       ):
             if ex_index % 1_00_00 == 0:
                 logger.info(F'Writing example {ex_index}/{len(self.examples       )}'       )
             # The mask has 1 for real tokens and 0 for padding tokens. Only real
             # tokens are attended to.
             UpperCAmelCase_											=  [1 if mask_padding_with_zero else 0] * len(__snake_case       )

             # Zero-pad up to the sequence length.
             UpperCAmelCase_											=  batch_length - len(__snake_case       )
             if pad_on_left:
                 UpperCAmelCase_											=  ([pad_token] * padding_length) + input_ids
                 UpperCAmelCase_											=  ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
             else:
                 UpperCAmelCase_											=  input_ids + ([pad_token] * padding_length)
                 UpperCAmelCase_											=  attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

             if len(__snake_case       ) != batch_length:
                 raise ValueError(F'Error with input length {len(__snake_case       )} vs {batch_length}'       )
             if len(__snake_case       ) != batch_length:
                 raise ValueError(F'Error with input length {len(__snake_case       )} vs {batch_length}'       )

             if self.mode == "classification":
                 UpperCAmelCase_											=  label_map[example.label]
             elif self.mode == "regression":
                 UpperCAmelCase_											=  float(example.label       )
             else:
                 raise ValueError(self.mode       )

             if ex_index < 5 and self.verbose:
                 logger.info('''*** Example ***'''       )
                 logger.info(F'guid: {example.guid}'       )
                 logger.info(F'input_ids: {" ".join([str(__snake_case       ) for x in input_ids]       )}'       )
                 logger.info(F'attention_mask: {" ".join([str(__snake_case       ) for x in attention_mask]       )}'       )
                 logger.info(F'label: {example.label} (id = {label})'       )

             features.append(InputFeatures(input_ids=__snake_case      ,					attention_mask=__snake_case      ,					label=__snake_case       )       )

         if return_tensors is None:
             return features
         elif return_tensors == "tf":
             if not is_tf_available():
                 raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''       )
             import tensorflow as tf

             def gen():
                 for ex in features:
                     yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

             UpperCAmelCase_											=  tf.data.Dataset.from_generator(
                 __snake_case      ,					({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)      ,					({'''input_ids''': tf.TensorShape([None]       ), '''attention_mask''': tf.TensorShape([None]       )}, tf.TensorShape([]       ))      ,					)
             return dataset
         elif return_tensors == "pt":
             if not is_torch_available():
                 raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''       )
             import torch
             from torch.utils.data import TensorDataset

             UpperCAmelCase_											=  torch.tensor([f.input_ids for f in features]      ,					dtype=torch.long       )
             UpperCAmelCase_											=  torch.tensor([f.attention_mask for f in features]      ,					dtype=torch.long       )
             if self.mode == "classification":
                 UpperCAmelCase_											=  torch.tensor([f.label for f in features]      ,					dtype=torch.long       )
             elif self.mode == "regression":
                 UpperCAmelCase_											=  torch.tensor([f.label for f in features]      ,					dtype=torch.float       )

             UpperCAmelCase_											=  TensorDataset(__snake_case      ,					__snake_case      ,					__snake_case       )
             return dataset
         else:
             raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''       )











import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_lowerCamelCase    				=		logging.get_logger(__name__)

_lowerCamelCase    				=		{
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class     a  (     _A    ):

     '''simple docstring'''





     lowerCAmelCase       : Any     	=						'vit-hybrid'
     def __init__(			self     :    str      ,					__snake_case     :    str=None      ,					__snake_case     :    List[Any]=7_68      ,					__snake_case     :    List[Any]=12      ,					__snake_case     :    Dict=12      ,					__snake_case     :    Optional[int]=30_72      ,					__snake_case     :    Optional[int]="gelu"      ,					__snake_case     :    Union[str, Any]=0.0      ,					__snake_case     :    Any=0.0      ,					__snake_case     :    List[Any]=0.02      ,					__snake_case     :    Union[str, Any]=1E-12      ,					__snake_case     :    Optional[int]=2_24      ,					__snake_case     :    Optional[int]=1      ,					__snake_case     :    Tuple=3      ,					__snake_case     :    Union[str, Any]=[1, 10_24, 24, 24]      ,					__snake_case     :    List[Any]=True      ,					**__snake_case     :    Tuple      ,					):
         super().__init__(**__snake_case       )

         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''       )
             UpperCAmelCase_											=  {
                 '''global_padding''': '''same''',
                 '''layer_type''': '''bottleneck''',
                 '''depths''': [3, 4, 9],
                 '''out_features''': ['''stage3'''],
                 '''embedding_dynamic_padding''': True,
             }

         if isinstance(__snake_case      ,					__snake_case       ):
             if "model_type" in backbone_config:
                 UpperCAmelCase_											=  CONFIG_MAPPING[backbone_config['''model_type''']]
             else:
                 logger.info(
                     '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''       )
                 UpperCAmelCase_											=  BitConfig
             UpperCAmelCase_											=  backbone_config_class(**__snake_case       )

         UpperCAmelCase_											=  backbone_featmap_shape
         UpperCAmelCase_											=  backbone_config
         UpperCAmelCase_											=  hidden_size
         UpperCAmelCase_											=  num_hidden_layers
         UpperCAmelCase_											=  num_attention_heads
         UpperCAmelCase_											=  intermediate_size
         UpperCAmelCase_											=  hidden_act
         UpperCAmelCase_											=  hidden_dropout_prob
         UpperCAmelCase_											=  attention_probs_dropout_prob
         UpperCAmelCase_											=  initializer_range
         UpperCAmelCase_											=  layer_norm_eps
         UpperCAmelCase_											=  image_size
         UpperCAmelCase_											=  patch_size
         UpperCAmelCase_											=  num_channels
         UpperCAmelCase_											=  qkv_bias




     def 						lowerCamelCase_     (			self     :    Any       ):
         UpperCAmelCase_											=  copy.deepcopy(self.__dict__       )
         UpperCAmelCase_											=  self.backbone_config.to_dict()
         UpperCAmelCase_											=  self.__class__.model_type
         return output





