"""simple docstring"""






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__    :    Optional[Any]			=      logging.get_logger(__name__)
@dataclass
class 							snake_case			:




					"""simple docstring"""

					snake_case__							       =					42
					snake_case__							       =					42
					snake_case__							       =					None
					snake_case__							       =					None







					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									return json.dumps(dataclasses.asdict(self     )       ,indent=2     ) + "\n"






@dataclass(frozen=A_							)
class 							snake_case			:




					"""simple docstring"""

					snake_case__							       =					42
					snake_case__							       =					None
					snake_case__							       =					None
					snake_case__							       =					None







					def 						__lowerCAmelCase		(							self  :      Any     ):
									return json.dumps(dataclasses.asdict(self     )     ) + "\n"






class 							snake_case			:




					"""simple docstring"""

					def 						__lowerCAmelCase		(							self  :      Union[str, Any]       ,lowerCamelCase__  :      str     ):
									raise NotImplementedError()







					def 						__lowerCAmelCase		(							self  :      Optional[Any]       ,lowerCamelCase__  :      List[str]     ):
									raise NotImplementedError()







					def 						__lowerCAmelCase		(							self  :      Dict       ,lowerCamelCase__  :      Optional[int]     ):
									raise NotImplementedError()







					def 						__lowerCAmelCase		(							self  :      str       ,lowerCamelCase__  :      Union[str, Any]     ):
									raise NotImplementedError()







					def 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									raise NotImplementedError()







					def 						__lowerCAmelCase		(							self  :      List[Any]       ,lowerCamelCase__  :      Any     ):
									if len(self.get_labels()     ) > 1:
													UpperCAmelCase__			 =				self.get_labels()[int(example.label     )]
									return example







					@classmethod
					def 						__lowerCAmelCase		(							cls  :      Any       ,lowerCamelCase__  :      List[Any]       ,lowerCamelCase__  :      List[Any]=None     ):
									with open(_A       ,'r'       ,encoding='utf-8-sig'     ) as f:
													return list(csv.reader(_A       ,delimiter='\t'       ,quotechar=_A     )     )






class 							snake_case			(					A_							):




					"""simple docstring"""

					def __init__(							self  :      Optional[int]       ,lowerCamelCase__  :      List[Any]=None       ,lowerCamelCase__  :      Optional[int]=None       ,lowerCamelCase__  :      Dict="classification"       ,lowerCamelCase__  :      Union[str, Any]=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  :      List[str]       ,lowerCamelCase__  :      Any     ):
									if isinstance(_A       ,_A     ):
													return SingleSentenceClassificationProcessor(labels=self.labels       ,examples=self.examples[idx]     )
									return self.examples[idx]







					@classmethod
					def 						__lowerCAmelCase		(							cls  :      Optional[Any]       ,lowerCamelCase__  :      Optional[int]       ,lowerCamelCase__  :      List[str]=""       ,lowerCamelCase__  :      Any=0       ,lowerCamelCase__  :      Dict=1       ,lowerCamelCase__  :      List[Any]=None       ,lowerCamelCase__  :      str=False       ,**lowerCamelCase__  :      Optional[Any]     ):
									UpperCAmelCase__			 =				cls(**_A     )
									processor.add_examples_from_csv(
									    _A       ,split_name=_A       ,column_label=_A       ,column_text=_A       ,column_id=_A       ,skip_first_row=_A       ,overwrite_labels=_A       ,overwrite_examples=_A       ,)
									return processor







					@classmethod
					def 						__lowerCAmelCase		(							cls  :      Optional[int]       ,lowerCamelCase__  :      Union[str, Any]       ,lowerCamelCase__  :      int=None       ,**lowerCamelCase__  :      Union[str, Any]     ):
									UpperCAmelCase__			 =				cls(**_A     )
									processor.add_examples(_A       ,labels=_A     )
									return processor







					def 						__lowerCAmelCase		(							self  :      List[Any]       ,lowerCamelCase__  :      List[str]       ,lowerCamelCase__  :      str=""       ,lowerCamelCase__  :      Union[str, Any]=0       ,lowerCamelCase__  :      Union[str, Any]=1       ,lowerCamelCase__  :      Any=None       ,lowerCamelCase__  :      List[Any]=False       ,lowerCamelCase__  :      Optional[int]=False       ,lowerCamelCase__  :      int=False       ,):
									UpperCAmelCase__			 =				self._read_tsv(_A     )
									if skip_first_row:
													UpperCAmelCase__			 =				lines[1:]
									UpperCAmelCase__			 =				[]
									UpperCAmelCase__			 =				[]
									UpperCAmelCase__			 =				[]
									for i, line in enumerate(_A     ):
													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(_A     )
																	ids.append(_A     )

									return self.add_examples(
									    _A       ,_A       ,_A       ,overwrite_labels=_A       ,overwrite_examples=_A     )







					def 						__lowerCAmelCase		(							self  :      Union[str, Any]       ,lowerCamelCase__  :      Optional[Any]       ,lowerCamelCase__  :      Tuple=None       ,lowerCamelCase__  :      Optional[int]=None       ,lowerCamelCase__  :      Dict=False       ,lowerCamelCase__  :      List[str]=False     ):
									if labels is not None and len(_A     ) != len(_A     ):
													raise ValueError(
													    f'''Text and labels have mismatched lengths {len(_A     )} and {len(_A     )}'''     )
									if ids is not None and len(_A     ) != len(_A     ):
													raise ValueError(f'''Text and ids have mismatched lengths {len(_A     )} and {len(_A     )}'''     )
									if ids is None:
													UpperCAmelCase__			 =				[None] * len(_A     )
									if labels is None:
													UpperCAmelCase__			 =				[None] * len(_A     )
									UpperCAmelCase__			 =				[]
									UpperCAmelCase__			 =				set()
									for text_or_text_and_label, label, guid in zip(_A       ,_A       ,_A     ):
													if isinstance(_A       ,(tuple, list)     ) and label is None:
																	UpperCAmelCase__ ,      UpperCAmelCase__			 =				text_or_text_and_label
													else:
																	UpperCAmelCase__			 =				text_or_text_and_label
													added_labels.add(_A     )
													examples.append(InputExample(guid=_A       ,text_a=_A       ,text_b=_A       ,label=_A     )     )

									# Update examples
									if overwrite_examples:
													UpperCAmelCase__			 =				examples
									else:
													self.examples.extend(_A     )

									# Update labels
									if overwrite_labels:
													UpperCAmelCase__			 =				list(_A     )
									else:
													UpperCAmelCase__			 =				list(set(self.labels     ).union(_A     )     )

									return self.examples







					def 						__lowerCAmelCase		(							self  :      Any       ,lowerCamelCase__  :      int       ,lowerCamelCase__  :      List[Any]=None       ,lowerCamelCase__  :      Optional[int]=False       ,lowerCamelCase__  :      Dict=0       ,lowerCamelCase__  :      Optional[int]=True       ,lowerCamelCase__  :      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 % 10_000 == 0:
																	logger.info(f'''Tokenizing example {ex_index}'''     )

													UpperCAmelCase__			 =				tokenizer.encode(
													    example.text_a       ,add_special_tokens=_A       ,max_length=min(_A       ,tokenizer.max_len     )       ,)
													all_input_ids.append(_A     )

									UpperCAmelCase__			 =				max(len(_A     ) for input_ids in all_input_ids     )

									UpperCAmelCase__			 =				[]
									for ex_index, (input_ids, example) in enumerate(zip(_A       ,self.examples     )     ):
													if ex_index % 10_000 == 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(_A     )

													# Zero-pad up to the sequence length.
													UpperCAmelCase__			 =				batch_length - len(_A     )
													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(_A     ) != batch_length:
																	raise ValueError(f'''Error with input length {len(_A     )} vs {batch_length}'''     )
													if len(_A     ) != batch_length:
																	raise ValueError(f'''Error with input length {len(_A     )} 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(_A     ) for x in input_ids]     )}'''     )
																	logger.info(f'''attention_mask: {" ".join([str(_A     ) for x in attention_mask]     )}'''     )
																	logger.info(f'''label: {example.label} (id = {label})'''     )

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

									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(
													    _A       ,({'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(_A       ,_A       ,_A     )
													return dataset
									else:
													raise ValueError('return_tensors should be one of \'tf\' or \'pt\''     )





import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


_A       =					logging.get_logger(__name__)







class 	lowerCamelCase						(    A_    ):





     def __init__(self :       Any							,       *_A :       Optional[Any]							,       **_A :       List[Any]  )		->		None:
         warnings.warn(
             "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use LevitImageProcessor instead."							,       _A							,       )
         super().__init__(*_A							,       **_A  )

