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


UpperCAmelCase     						=			logging.get_logger(__name__)




@dataclass
class 				__lowerCAmelCase					:



 '''simple docstring'''
 _lowerCamelCase  :		str
 _lowerCamelCase  :		str
 _lowerCamelCase  :		Optional[str]			      =	None
 _lowerCamelCase  :		Optional[str]			      =	None

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







@dataclass(frozen=a_  )
class 				__lowerCAmelCase					:



 '''simple docstring'''
 _lowerCamelCase  :		List[int]
 _lowerCamelCase  :		Optional[List[int]]			      =	None
 _lowerCamelCase  :		Optional[List[int]]			      =	None
 _lowerCamelCase  :		Optional[Union[int, float]]			      =	None

 def    __snake_case   (     self    :						Tuple					)	->							List[Any]:
        return json.dumps(dataclasses.asdict(self					)					) + "\n"







class 				__lowerCAmelCase					:



 '''simple docstring'''

 def    __snake_case   (     self    :						int    ,     lowerCamelCase    :						Any					)	->							Dict:
        raise NotImplementedError()

 def    __snake_case   (     self    :						str    ,     lowerCamelCase    :						int					)	->							Any:
        raise NotImplementedError()

 def    __snake_case   (     self    :						str    ,     lowerCamelCase    :						Tuple					)	->							Union[str, Any]:
        raise NotImplementedError()

 def    __snake_case   (     self    :						Dict    ,     lowerCamelCase    :						int					)	->							Any:
        raise NotImplementedError()

 def    __snake_case   (     self    :						str					)	->							Union[str, Any]:
        raise NotImplementedError()

 def    __snake_case   (     self    :						str    ,     lowerCamelCase    :						List[str]					)	->							Union[str, Any]:
        if len(self.get_labels()					) > 1:
               _UpperCamelCase       :    Tuple					  =					self.get_labels()[int(example.label					)]
        return example

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







class 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''

 def __init__(     self    :						List[str]    ,     lowerCamelCase    :						Any=None    ,     lowerCamelCase    :						List[Any]=None    ,     lowerCamelCase    :						Tuple="classification"    ,     lowerCamelCase    :						Any=False					)	->							List[Any]:
        _UpperCamelCase       :    List[Any]					  =					[] if labels is None else labels
        _UpperCamelCase       :    int					  =					[] if examples is None else examples
        _UpperCamelCase       :    Union[str, Any]					  =					mode
        _UpperCamelCase       :    Optional[int]					  =					verbose

 def __len__(     self    :						List[str]					)	->							Dict:
        return len(self.examples					)

 def __getitem__(     self    :						List[Any]    ,     lowerCamelCase    :						str					)	->							Any:
        if isinstance(lowerCamelCase    ,     lowerCamelCase					):
               return SingleSentenceClassificationProcessor(labels=self.labels    ,     examples=self.examples[idx]					)
        return self.examples[idx]

 @classmethod
 def    __snake_case   (     cls    :						Tuple    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						str=""    ,     lowerCamelCase    :						Any=0    ,     lowerCamelCase    :						List[str]=1    ,     lowerCamelCase    :						Optional[Any]=None    ,     lowerCamelCase    :						List[Any]=False    ,     **lowerCamelCase    :						Optional[Any]					)	->							Optional[Any]:
        _UpperCamelCase       :    Optional[int]					  =					cls(**lowerCamelCase					)
        processor.add_examples_from_csv(
            lowerCamelCase    ,     split_name=lowerCamelCase    ,     column_label=lowerCamelCase    ,     column_text=lowerCamelCase    ,     column_id=lowerCamelCase    ,     skip_first_row=lowerCamelCase    ,     overwrite_labels=lowerCamelCase    ,     overwrite_examples=lowerCamelCase    ,     )
        return processor

 @classmethod
 def    __snake_case   (     cls    :						List[Any]    ,     lowerCamelCase    :						Optional[Any]    ,     lowerCamelCase    :						str=None    ,     **lowerCamelCase    :						str					)	->							List[Any]:
        _UpperCamelCase       :    List[str]					  =					cls(**lowerCamelCase					)
        processor.add_examples(lowerCamelCase    ,     labels=lowerCamelCase					)
        return processor

 def    __snake_case   (     self    :						int    ,     lowerCamelCase    :						List[Any]    ,     lowerCamelCase    :						Any=""    ,     lowerCamelCase    :						Optional[int]=0    ,     lowerCamelCase    :						List[Any]=1    ,     lowerCamelCase    :						List[Any]=None    ,     lowerCamelCase    :						Any=False    ,     lowerCamelCase    :						Dict=False    ,     lowerCamelCase    :						List[str]=False    ,     )	->							Optional[Any]:
        _UpperCamelCase       :    Tuple					  =					self._read_tsv(lowerCamelCase					)
        if skip_first_row:
               _UpperCamelCase       :    Union[str, Any]					  =					lines[1:]
        _UpperCamelCase       :    Union[str, Any]					  =					[]
        _UpperCamelCase       :    int					  =					[]
        _UpperCamelCase       :    Optional[Any]					  =					[]
        for i, line in enumerate(lowerCamelCase					):
               texts.append(line[column_text]					)
               labels.append(line[column_label]					)
               if column_id is not None:
                      ids.append(line[column_id]					)
               else:
                      _UpperCamelCase       :    Tuple					  =					f"{split_name}-{i}" if split_name else str(lowerCamelCase					)
                      ids.append(lowerCamelCase					)

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

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

        # Update examples
        if overwrite_examples:
               _UpperCamelCase       :    int					  =					examples
        else:
               self.examples.extend(lowerCamelCase					)

        # Update labels
        if overwrite_labels:
               _UpperCamelCase       :    str					  =					list(lowerCamelCase					)
        else:
               _UpperCamelCase       :    Optional[int]					  =					list(set(self.labels					).union(lowerCamelCase					)					)

        return self.examples

 def    __snake_case   (     self    :						Tuple    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						int=None    ,     lowerCamelCase    :						Union[str, Any]=False    ,     lowerCamelCase    :						List[Any]=0    ,     lowerCamelCase    :						Optional[Any]=True    ,     lowerCamelCase    :						Dict=None    ,     )	->							Any:
        if max_length is None:
               _UpperCamelCase       :    Optional[int]					  =					tokenizer.max_len

        _UpperCamelCase       :    List[str]					  =					{label: i for i, label in enumerate(self.labels					)}

        _UpperCamelCase       :    Tuple					  =					[]
        for ex_index, example in enumerate(self.examples					):
               if ex_index % 1_0_0_0_0 == 0:
                      logger.info(f"Tokenizing example {ex_index}"					)

               _UpperCamelCase       :    List[str]					  =					tokenizer.encode(
                   example.text_a    ,     add_special_tokens=lowerCamelCase    ,     max_length=min(lowerCamelCase    ,     tokenizer.max_len					)    ,     )
               all_input_ids.append(lowerCamelCase					)

        _UpperCamelCase       :    str					  =					max(len(lowerCamelCase					) for input_ids in all_input_ids					)

        _UpperCamelCase       :    List[str]					  =					[]
        for ex_index, (input_ids, example) in enumerate(zip(lowerCamelCase    ,     self.examples					)					):
               if ex_index % 1_0_0_0_0 == 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       :    Dict					  =					[1 if mask_padding_with_zero else 0] * len(lowerCamelCase					)

               # Zero-pad up to the sequence length.
               _UpperCamelCase       :    str					  =					batch_length - len(lowerCamelCase					)
               if pad_on_left:
                      _UpperCamelCase       :    str					  =					([pad_token] * padding_length) + input_ids
                      _UpperCamelCase       :    str					  =					([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
               else:
                      _UpperCamelCase       :    int					  =					input_ids + ([pad_token] * padding_length)
                      _UpperCamelCase       :    Optional[Any]					  =					attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

               if len(lowerCamelCase					) != batch_length:
                      raise ValueError(f"Error with input length {len(lowerCamelCase					)} vs {batch_length}"					)
               if len(lowerCamelCase					) != batch_length:
                      raise ValueError(f"Error with input length {len(lowerCamelCase					)} vs {batch_length}"					)

               if self.mode == "classification":
                      _UpperCamelCase       :    List[str]					  =					label_map[example.label]
               elif self.mode == "regression":
                      _UpperCamelCase       :    Optional[int]					  =					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(lowerCamelCase					) for x in input_ids]					)}"					)
                      logger.info(f"attention_mask: {' '.join([str(lowerCamelCase					) for x in attention_mask]					)}"					)
                      logger.info(f"label: {example.label} (id = {label})"					)

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

        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       :    Optional[int]					  =					tf.data.Dataset.from_generator(
                   lowerCamelCase    ,     ({'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       :    List[str]					  =					torch.tensor([f.input_ids for f in features]    ,     dtype=torch.long					)
               _UpperCamelCase       :    List[Any]					  =					torch.tensor([f.attention_mask for f in features]    ,     dtype=torch.long					)
               if self.mode == "classification":
                      _UpperCamelCase       :    str					  =					torch.tensor([f.label for f in features]    ,     dtype=torch.long					)
               elif self.mode == "regression":
                      _UpperCamelCase       :    Optional[int]					  =					torch.tensor([f.label for f in features]    ,     dtype=torch.float					)

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




def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]			):

       # validate:
       if not isinstance(lowercase_						,  lowercase_			) or not all(
           isinstance(lowercase_						,  lowercase_			) and item >= 0 for item in citations			):
              raise ValueError('The citations should be a list of non negative integers.'			)

       citations.sort()
       _UpperCamelCase       :    List[str]					  =					len(lowercase_			)

       for i in range(lowercase_			):
              if citations[len_citations - 1 - i] <= i:
                     return i

       return len_citations


if __name__ == "__main__":
 import doctest

 doctest.testmod()



